




'''simple docstring'''



import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         FalconForCausalLM,
         FalconForQuestionAnswering,
         FalconForSequenceClassification,
         FalconForTokenClassification,
         FalconModel,
     )





class      lowerCAmelCase :


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=None     ,	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_input_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_labels
            __snake_case            =	num_choices
            __snake_case            =	scope


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_input_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None

            __snake_case            =	None
            __snake_case            =	None
            __snake_case            =	None
            if self.use_labels:
                 __snake_case            =	ids_tensor([self.batch_size]     ,	self.type_sequence_label_size	)
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels	)
                 __snake_case            =	ids_tensor([self.batch_size]     ,	self.num_choices	)

            __snake_case            =	self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            return FalconConfig(
                vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	pad_token_id=1     ,	new_decoder_architecture=__SCREAMING_SNAKE_CASE     ,	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	FalconModel(config=__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	True
            __snake_case            =	FalconModel(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(
                __SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,	encoder_attention_mask=__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	model(
                __SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	FalconForCausalLM(config=__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	True
            __snake_case            =	True
            __snake_case            =	FalconForCausalLM(config=__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()

            # first forward pass
            __snake_case            =	model(
                __SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,	encoder_attention_mask=__SCREAMING_SNAKE_CASE     ,	use_cache=__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            __snake_case            =	ids_tensor((self.batch_size, 3)     ,	config.vocab_size	)
            __snake_case            =	ids_tensor((self.batch_size, 3)     ,	vocab_size=2	)

            # append to next input_ids and
            __snake_case            =	torch.cat([input_ids, next_tokens]     ,	dim=-1	)
            __snake_case            =	torch.cat([input_mask, next_mask]     ,	dim=-1	)

            __snake_case            =	model(
                __SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,	encoder_attention_mask=__SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE     ,	)['''hidden_states'''][0]
            __snake_case            =	model(
                __SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,	encoder_attention_mask=__SCREAMING_SNAKE_CASE     ,	past_key_values=__SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE     ,	)['''hidden_states'''][0]

            # select random slice
            __snake_case            =	ids_tensor((1,)     ,	output_from_past.shape[-1]	).item()
            __snake_case            =	output_from_no_past[:, -3:, random_slice_idx].detach()
            __snake_case            =	output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-3	)	)




       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            (
                (
                __snake_case       
            )       ,     (
                __snake_case       
            )       ,     (
                __snake_case       
            )       ,     (
                __snake_case       
            )       ,     (
                __snake_case       
            )       ,     (
                __snake_case       
            )       ,     (
                __snake_case       
            )       ,            
            )            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
            return config, inputs_dict





@require_torch
class      lowerCAmelCase (			_a					, _a					, _a					, unittest.TestCase):
       __lowercase	:   Optional[Any]										=      (
           (
               FalconModel,
               FalconForCausalLM,
               FalconForSequenceClassification,
               FalconForTokenClassification,
               FalconForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       __lowercase	:   List[Any]										=      (FalconForCausalLM,) if is_torch_available() else ()
       __lowercase	:   Optional[int]										=      (
           {
               """feature-extraction""": FalconModel,
               """text-classification""": FalconForSequenceClassification,
               """text-generation""": FalconForCausalLM,
               """question-answering""": FalconForQuestionAnswering,
               """token-classification""": FalconForTokenClassification,
               """zero-shot""": FalconForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __lowercase	:   Union[str, Any]										=      False
       __lowercase	:   Dict										=      False


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	FalconModelTester(self	)
            __snake_case            =	ConfigTester(self     ,	config_class=__SCREAMING_SNAKE_CASE     ,	hidden_size=37	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            self.config_tester.run_common_tests()


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case       ,     *__snake_case            =	self.model_tester.prepare_config_and_inputs()
            for alibi in [True, False]:
                 __snake_case            =	alibi
                 self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE     ,	*__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case            =	3
            __snake_case            =	input_dict['''input_ids''']
            __snake_case            =	input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ids_tensor([self.model_tester.batch_size]     ,	self.model_tester.type_sequence_label_size	)
            __snake_case            =	FalconForSequenceClassification(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE	)
            self.assertEqual(result.logits.shape     ,	(self.model_tester.batch_size, self.model_tester.num_labels)	)


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case            =	3
            __snake_case            =	'''single_label_classification'''
            __snake_case            =	input_dict['''input_ids''']
            __snake_case            =	input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ids_tensor([self.model_tester.batch_size]     ,	self.model_tester.type_sequence_label_size	)
            __snake_case            =	FalconForSequenceClassification(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE	)
            self.assertEqual(result.logits.shape     ,	(self.model_tester.batch_size, self.model_tester.num_labels)	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case            =	input_dict['''input_ids''']
            __snake_case            =	FalconForCausalLM(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	use_cache=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	input_ids.shape[0]
            __snake_case            =	model._convert_to_rw_cache(result.past_key_values	)
            __snake_case            =	model._convert_cache_to_standard_format(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            for layer in range(len(__SCREAMING_SNAKE_CASE	)	):
                 for tensor_idx in range(2	):
                      self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3	)
                      self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4	)
                      self.assertTrue(
                          torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]	)	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case            =	3
            __snake_case            =	'''multi_label_classification'''
            __snake_case            =	input_dict['''input_ids''']
            __snake_case            =	input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ids_tensor(
                [self.model_tester.batch_size, config.num_labels]     ,	self.model_tester.type_sequence_label_size	).to(torch.float	)
            __snake_case            =	FalconForSequenceClassification(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE	)
            self.assertEqual(result.logits.shape     ,	(self.model_tester.batch_size, self.model_tester.num_labels)	)




       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            for model_class in self.all_generative_model_classes:
                 __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()

                 # If it doesn't support cache, pass the test
                 if not hasattr(__SCREAMING_SNAKE_CASE     ,	'''use_cache'''	):
                      return

                 __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	).to(__SCREAMING_SNAKE_CASE	)
                 if "use_cache" not in inputs:
                      __snake_case            =	True
                 __snake_case            =	model(**__SCREAMING_SNAKE_CASE	)

                 # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
                 if "past_key_values" not in outputs:
                      return

                 __snake_case            =	(
                     getattr(__SCREAMING_SNAKE_CASE     ,	'''decoder_layers'''     ,	__SCREAMING_SNAKE_CASE	)
                     or getattr(__SCREAMING_SNAKE_CASE     ,	'''num_decoder_layers'''     ,	__SCREAMING_SNAKE_CASE	)
                     or config.num_hidden_layers
                 )
                 __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	'''num_kv_heads'''     ,	config.num_attention_heads	)
                 __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	'''d_model'''     ,	config.hidden_size	)
                 __snake_case            =	embed_dim // num_attention_heads

                 __snake_case            =	outputs['''past_key_values''']
                 self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)

                 __snake_case       ,     __snake_case            =	inputs['''input_ids'''].shape
                 for i in range(__SCREAMING_SNAKE_CASE	):
                      if config.new_decoder_architecture:
                           __snake_case            =	config.num_attention_heads
                      elif config.multi_query:
                           __snake_case            =	1
                      self.assertEqual(len(past_kv[0]	)     ,	2	)  # K V for the decoder = 2
                      self.assertEqual(
                          past_kv[i][0].shape     ,	(batch_size, num_attention_heads, seq_length, per_head_embed_dim)	)
                      self.assertEqual(
                          past_kv[i][1].shape     ,	(batch_size, num_attention_heads, seq_length, per_head_embed_dim)	)





@require_torch
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b'''	)
            __snake_case            =	FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b'''	)
            model.eval()
            model.to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer('''My favorite food is'''     ,	return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	(
                '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
            )

            __snake_case            =	model.generate(**__SCREAMING_SNAKE_CASE     ,	do_sample=__SCREAMING_SNAKE_CASE     ,	max_new_tokens=19	)
            __snake_case            =	tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)[0]

            self.assertEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       @slow
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
                 __snake_case            =	AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	FalconForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 model.eval()
                 model.to(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	tokenizer('''My favorite food is'''     ,	return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)

                 # We just test that these run without errors - the models are randomly initialized
                 # and so the actual text outputs will be garbage
                 model.generate(**__SCREAMING_SNAKE_CASE     ,	do_sample=__SCREAMING_SNAKE_CASE     ,	max_new_tokens=4	)
                 model.generate(**__SCREAMING_SNAKE_CASE     ,	do_sample=__SCREAMING_SNAKE_CASE     ,	max_new_tokens=4	)
                 model.generate(**__SCREAMING_SNAKE_CASE     ,	num_beams=2     ,	max_new_tokens=4	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            with torch.no_grad():
                 for repo in [
                     "Rocketknight1/falcon-rw-1b",
                     "Rocketknight1/tiny-random-falcon-7b",
                     "Rocketknight1/tiny-random-falcon-40b",
                 ]:
                      __snake_case            =	AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	FalconForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE	)
                      model.eval()
                      model.to(device=__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	tokenizer('''My favorite food is'''     ,	return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)

                      # Test results are the same with and without cache
                      __snake_case            =	model.generate(**__SCREAMING_SNAKE_CASE     ,	do_sample=__SCREAMING_SNAKE_CASE     ,	max_new_tokens=20     ,	use_cache=__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	model.generate(**__SCREAMING_SNAKE_CASE     ,	do_sample=__SCREAMING_SNAKE_CASE     ,	max_new_tokens=20     ,	use_cache=__SCREAMING_SNAKE_CASE	)
                      self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0	)










import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class      UpperCamelCase(	unittest.TestCase ):
					snake_case_		:					int    					=						MODEL_FOR_MASKED_LM_MAPPING
					snake_case_		:					Optional[int]    					=						TF_MODEL_FOR_MASKED_LM_MAPPING


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Union[str, Any]:




											'''simple docstring'''

											super().tearDown()
											# clean-up as much as possible GPU memory occupied by PyTorch
											gc.collect()
											if is_torch_available():
																	import torch

																	torch.cuda.empty_cache()


					@require_tf
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		pipeline(task="fill-mask"  ,     model="sshleifer/tiny-distilroberta-base"  ,     top_k=2  ,     framework="tf"    )
											__snake_case				=		unmasker("My name is <mask>"    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE  ,     decimals=6    )  ,     [
											        {"sequence": "My name is grouped", "score": 2.1e-0_5, "token": 3_8_0_1_5, "token_str": " grouped"},
											        {"sequence": "My name is accuser", "score": 2.1e-0_5, "token": 2_5_5_0_6, "token_str": " accuser"},
											    ]  ,     )

											__snake_case				=		unmasker("The largest city in France is <mask>"    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE  ,     decimals=6    )  ,     [
											        {
											            "sequence": "The largest city in France is grouped",
											            "score": 2.1e-0_5,
											            "token": 3_8_0_1_5,
											            "token_str": " grouped",
											        },
											        {
											            "sequence": "The largest city in France is accuser",
											            "score": 2.1e-0_5,
											            "token": 2_5_5_0_6,
											            "token_str": " accuser",
											        },
											    ]  ,     )

											__snake_case				=		unmasker("My name is <mask>"  ,     targets=[" Patrick", " Clara", " Teven"]  ,     top_k=3    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE  ,     decimals=6    )  ,     [
											        {"sequence": "My name is Clara", "score": 2e-0_5, "token": 1_3_6_0_6, "token_str": " Clara"},
											        {"sequence": "My name is Patrick", "score": 2e-0_5, "token": 3_4_9_9, "token_str": " Patrick"},
											        {"sequence": "My name is Te", "score": 1.9e-0_5, "token": 2_9_4_1, "token_str": " Te"},
											    ]  ,     )


					@require_torch
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		pipeline(task="fill-mask"  ,     model="sshleifer/tiny-distilroberta-base"  ,     top_k=2  ,     framework="pt"    )

											__snake_case				=		unmasker("My name is <mask>"    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE  ,     decimals=6    )  ,     [
											        {"sequence": "My name is Maul", "score": 2.2e-0_5, "token": 3_5_6_7_6, "token_str": " Maul"},
											        {"sequence": "My name isELS", "score": 2.2e-0_5, "token": 1_6_4_1_6, "token_str": "ELS"},
											    ]  ,     )

											__snake_case				=		unmasker("The largest city in France is <mask>"    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE  ,     decimals=6    )  ,     [
											        {
											            "sequence": "The largest city in France is Maul",
											            "score": 2.2e-0_5,
											            "token": 3_5_6_7_6,
											            "token_str": " Maul",
											        },
											        {"sequence": "The largest city in France isELS", "score": 2.2e-0_5, "token": 1_6_4_1_6, "token_str": "ELS"},
											    ]  ,     )

											__snake_case				=		unmasker("My name is <mask>"  ,     targets=[" Patrick", " Clara", " Teven"]  ,     top_k=3    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE  ,     decimals=6    )  ,     [
											        {"sequence": "My name is Patrick", "score": 2.1e-0_5, "token": 3_4_9_9, "token_str": " Patrick"},
											        {"sequence": "My name is Te", "score": 2e-0_5, "token": 2_9_4_1, "token_str": " Te"},
											        {"sequence": "My name is Clara", "score": 2e-0_5, "token": 1_3_6_0_6, "token_str": " Clara"},
											    ]  ,     )

											__snake_case				=		unmasker("My name is <mask> <mask>"  ,     top_k=2    )

											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE  ,     decimals=6    )  ,     [
											        [
											            {
											                "score": 2.2e-0_5,
											                "token": 3_5_6_7_6,
											                "token_str": " Maul",
											                "sequence": "<s>My name is Maul<mask></s>",
											            },
											            {"score": 2.2e-0_5, "token": 1_6_4_1_6, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
											        ],
											        [
											            {
											                "score": 2.2e-0_5,
											                "token": 3_5_6_7_6,
											                "token_str": " Maul",
											                "sequence": "<s>My name is<mask> Maul</s>",
											            },
											            {"score": 2.2e-0_5, "token": 1_6_4_1_6, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
											        ],
											    ]  ,     )


					@require_torch_gpu
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		pipeline("fill-mask"  ,     model="hf-internal-testing/tiny-random-distilbert"  ,     device=0  ,     framework="pt"    )

											# convert model to fp16
											pipe.model.half()

											__snake_case				=		pipe("Paris is the [MASK] of France."    )
											# We actually don't care about the result, we just want to make sure
											# it works, meaning the float16 tensor got casted back to float32
											# for postprocessing.
											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					@slow
					@require_torch
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		pipeline(task="fill-mask"  ,     model="distilroberta-base"  ,     top_k=2  ,     framework="pt"    )
											self.run_large_test(SCREAMING_SNAKE_CASE    )


					@slow
					@require_tf
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		pipeline(task="fill-mask"  ,     model="distilroberta-base"  ,     top_k=2  ,     framework="tf"    )
											self.run_large_test(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						str:




											'''simple docstring'''

											__snake_case				=		unmasker("My name is <mask>"    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE    )  ,     [
											        {"sequence": "My name is John", "score": 0.008, "token": 6_1_0, "token_str": " John"},
											        {"sequence": "My name is Chris", "score": 0.007, "token": 1_5_7_3, "token_str": " Chris"},
											    ]  ,     )
											__snake_case				=		unmasker("The largest city in France is <mask>"    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE    )  ,     [
											        {
											            "sequence": "The largest city in France is Paris",
											            "score": 0.251,
											            "token": 2_2_0_1,
											            "token_str": " Paris",
											        },
											        {
											            "sequence": "The largest city in France is Lyon",
											            "score": 0.214,
											            "token": 1_2_7_9_0,
											            "token_str": " Lyon",
											        },
											    ]  ,     )

											__snake_case				=		unmasker("My name is <mask>"  ,     targets=[" Patrick", " Clara", " Teven"]  ,     top_k=3    )
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE    )  ,     [
											        {"sequence": "My name is Patrick", "score": 0.005, "token": 3_4_9_9, "token_str": " Patrick"},
											        {"sequence": "My name is Clara", "score": 0.000, "token": 1_3_6_0_6, "token_str": " Clara"},
											        {"sequence": "My name is Te", "score": 0.000, "token": 2_9_4_1, "token_str": " Te"},
											    ]  ,     )


					@require_torch
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						str:




											'''simple docstring'''

											__snake_case				=		pipeline(task="fill-mask"  ,     model="sshleifer/tiny-distilroberta-base"  ,     framework="pt"    )
											__snake_case				=		None
											__snake_case				=		None
											self.run_pipeline_test(SCREAMING_SNAKE_CASE  ,     []    )


					@require_tf
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		pipeline(task="fill-mask"  ,     model="sshleifer/tiny-distilroberta-base"  ,     framework="tf"    )
											__snake_case				=		None
											__snake_case				=		None
											self.run_pipeline_test(SCREAMING_SNAKE_CASE  ,     []    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						int:




											'''simple docstring'''

											if tokenizer is None or tokenizer.mask_token_id is None:
																	self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"    )

											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE    )
											__snake_case				=		[
											    f'''This is another {tokenizer.mask_token} test''',
											]
											return fill_masker, examples


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		fill_masker.tokenizer
											__snake_case				=		fill_masker.model

											__snake_case				=		fill_masker(
											    f'''This is a {tokenizer.mask_token}'''  ,     )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											    ]  ,     )

											__snake_case				=		fill_masker([f'''This is a {tokenizer.mask_token}''']    )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											    ]  ,     )

											__snake_case				=		fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.''']    )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        [
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        ],
											        [
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        ],
											    ]  ,     )

											with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																	fill_masker([None]    )
											# No mask_token is not supported
											with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																	fill_masker("This is"    )

											self.run_test_top_k(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
											self.run_test_targets(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
											self.run_test_top_k_targets(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
											self.fill_mask_with_duplicate_targets_and_top_k(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
											self.fill_mask_with_multiple_masks(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		tokenizer.get_vocab()
											__snake_case				=		sorted(vocab.keys()    )[:2]
											# Pipeline argument
											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE  ,     targets=SCREAMING_SNAKE_CASE    )
											__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''    )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											    ]  ,     )
											__snake_case				=		{vocab[el] for el in targets}
											self.assertEqual({el["token"] for el in outputs}  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		[tokenizer.decode([x]    ) for x in target_ids]
											self.assertEqual({el["token_str"] for el in outputs}  ,     set(SCREAMING_SNAKE_CASE    )    )

											# Call argument
											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE    )
											__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     targets=SCREAMING_SNAKE_CASE    )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											    ]  ,     )
											__snake_case				=		{vocab[el] for el in targets}
											self.assertEqual({el["token"] for el in outputs}  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		[tokenizer.decode([x]    ) for x in target_ids]
											self.assertEqual({el["token_str"] for el in outputs}  ,     set(SCREAMING_SNAKE_CASE    )    )

											# Score equivalence
											__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     targets=SCREAMING_SNAKE_CASE    )
											__snake_case				=		[top_mask["token_str"] for top_mask in outputs]
											__snake_case				=		[top_mask["score"] for top_mask in outputs]

											# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
											if set(SCREAMING_SNAKE_CASE    ) == set(SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     targets=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		[top_mask["score"] for top_mask in unmasked_targets]
																	self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE    )  ,     nested_simplify(SCREAMING_SNAKE_CASE    )    )

											# Raises with invalid
											with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     targets=[]    )
											# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
											if "" not in tokenizer.get_vocab():
																	with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																							__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     targets=[""]    )
																	with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																							__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     targets=""    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		List[str]    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE  ,     top_k=2    )
											__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''    )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											    ]  ,     )

											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE    )
											__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     top_k=2    )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											    ]  ,     )
											self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE    )  ,     nested_simplify(SCREAMING_SNAKE_CASE    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		tokenizer.get_vocab()
											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE    )

											# top_k=2, ntargets=3
											__snake_case				=		sorted(vocab.keys()    )[:3]
											__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     top_k=2  ,     targets=SCREAMING_SNAKE_CASE    )

											# If we use the most probably targets, and filter differently, we should still
											# have the same results
											__snake_case				=		[el["token_str"] for el in sorted(SCREAMING_SNAKE_CASE  ,     key=lambda SCREAMING_SNAKE_CASE    : x["score"]  ,     reverse=SCREAMING_SNAKE_CASE    )]
											# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
											if set(SCREAMING_SNAKE_CASE    ).issubset(SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		fill_masker(f'''This is a {tokenizer.mask_token}'''  ,     top_k=3  ,     targets=SCREAMING_SNAKE_CASE    )
																	# They should yield exactly the same result
																	self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE    )  ,     nested_simplify(SCREAMING_SNAKE_CASE    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Dict    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE    )
											__snake_case				=		tokenizer.get_vocab()
											# String duplicates + id duplicates
											__snake_case				=		sorted(vocab.keys()    )[:3]
											__snake_case				=		[targets[0], targets[1], targets[0], targets[2], targets[1]]
											__snake_case				=		fill_masker(f'''My name is {tokenizer.mask_token}'''  ,     targets=SCREAMING_SNAKE_CASE  ,     top_k=1_0    )

											# The target list contains duplicates, so we can't output more
											# than them
											self.assertEqual(len(SCREAMING_SNAKE_CASE    )  ,     3    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		FillMaskPipeline(model=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE    )

											__snake_case				=		fill_masker(
											    f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''  ,     top_k=2    )
											self.assertEqual(
											    SCREAMING_SNAKE_CASE  ,     [
											        [
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        ],
											        [
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        ],
											        [
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											            {"sequence": ANY(SCREAMING_SNAKE_CASE    ), "score": ANY(SCREAMING_SNAKE_CASE    ), "token": ANY(SCREAMING_SNAKE_CASE    ), "token_str": ANY(SCREAMING_SNAKE_CASE    )},
											        ],
											    ]  ,     )





