





"""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 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""




				def __init__(  self	:int    ,     __lowercase	:List[str]    ,     __lowercase	:List[Any]=3    ,     __lowercase	:str=7    ,     __lowercase	:Any=True    ,     __lowercase	:str=True    ,     __lowercase	:str=False    ,     __lowercase	:Optional[int]=True    ,     __lowercase	:Any=99    ,     __lowercase	:Optional[int]=32    ,     __lowercase	:int=5    ,     __lowercase	:Optional[int]=4    ,     __lowercase	:Union[str, Any]=37    ,     __lowercase	:Optional[int]="gelu"    ,     __lowercase	:Optional[int]=0.1    ,     __lowercase	:Tuple=0.1    ,     __lowercase	:Any=512    ,     __lowercase	:str=16    ,     __lowercase	:int=2    ,     __lowercase	:int=0.02    ,     __lowercase	:Tuple=3    ,     __lowercase	:Tuple=4    ,     __lowercase	:List[str]=None    ,     ):
											__lowerCamelCase   :   Union[str, Any]       =parent
											__lowerCamelCase   :   Union[str, Any]       =batch_size
											__lowerCamelCase   :   str       =seq_length
											__lowerCamelCase   :   List[Any]       =is_training
											__lowerCamelCase   :   Dict       =use_input_mask
											__lowerCamelCase   :   List[str]       =use_token_type_ids
											__lowerCamelCase   :   Union[str, Any]       =use_labels
											__lowerCamelCase   :   Tuple       =vocab_size
											__lowerCamelCase   :   List[Any]       =hidden_size
											__lowerCamelCase   :   int       =num_hidden_layers
											__lowerCamelCase   :   Dict       =num_attention_heads
											__lowerCamelCase   :   Union[str, Any]       =intermediate_size
											__lowerCamelCase   :   str       =hidden_act
											__lowerCamelCase   :   Optional[int]       =hidden_dropout_prob
											__lowerCamelCase   :   Optional[int]       =attention_probs_dropout_prob
											__lowerCamelCase   :   Tuple       =max_position_embeddings
											__lowerCamelCase   :   List[Any]       =type_vocab_size
											__lowerCamelCase   :   List[str]       =type_sequence_label_size
											__lowerCamelCase   :   List[Any]       =initializer_range
											__lowerCamelCase   :   List[str]       =num_labels
											__lowerCamelCase   :   str       =num_choices
											__lowerCamelCase   :   int       =scope




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   List[str]       =ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size      )

											__lowerCamelCase   :   Union[str, Any]       =None
											if self.use_input_mask:
																		__lowerCamelCase   :   str       =random_attention_mask([self.batch_size, self.seq_length]      )

											__lowerCamelCase   :   int       =None

											__lowerCamelCase   :   int       =None
											__lowerCamelCase   :   List[Any]       =None
											__lowerCamelCase   :   Optional[int]       =None
											if self.use_labels:
																		__lowerCamelCase   :   int       =ids_tensor([self.batch_size]    ,     self.type_sequence_label_size      )
																		__lowerCamelCase   :   Dict       =ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels      )
																		__lowerCamelCase   :   Dict       =ids_tensor([self.batch_size]    ,     self.num_choices      )

											__lowerCamelCase   :   int       =self.get_config()

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




				def        __lowercase	(  self	:Tuple      ):
											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=A_    ,     initializer_range=self.initializer_range    ,     pad_token_id=1    ,     new_decoder_architecture=A_    ,     )




				def        __lowercase	(  self	:int    ,     __lowercase	:Optional[int]    ,     __lowercase	:str    ,     __lowercase	:Optional[Any]    ,     __lowercase	:int    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:Optional[int]    ,     __lowercase	:Optional[Any]      ):
											__lowerCamelCase   :   Optional[Any]       =FalconModel(config=A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   List[str]       =model(A_    ,     attention_mask=A_      )
											__lowerCamelCase   :   Union[str, Any]       =model(A_      )
											self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)      )




				def        __lowercase	(  self	:Dict    ,     __lowercase	:Tuple    ,     __lowercase	:Optional[int]    ,     __lowercase	:str    ,     __lowercase	:Dict    ,     __lowercase	:Dict    ,     __lowercase	:Optional[Any]    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:List[str]    ,     __lowercase	:List[Any]    ,     ):
											__lowerCamelCase   :   Optional[int]       =True
											__lowerCamelCase   :   Dict       =FalconModel(A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   int       =model(
											    A_    ,     attention_mask=A_    ,     encoder_hidden_states=A_    ,     encoder_attention_mask=A_    ,     )
											__lowerCamelCase   :   Union[str, Any]       =model(
											    A_    ,     attention_mask=A_    ,     encoder_hidden_states=A_    ,     )
											__lowerCamelCase   :   Optional[int]       =model(A_    ,     attention_mask=A_      )
											self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)      )




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:Tuple    ,     __lowercase	:Dict    ,     __lowercase	:int    ,     __lowercase	:Optional[int]    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:str    ,     __lowercase	:List[str]    ,     ):
											__lowerCamelCase   :   List[str]       =FalconForCausalLM(config=A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   int       =model(A_    ,     attention_mask=A_    ,     labels=A_      )
											self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)      )




				def        __lowercase	(  self	:Optional[Any]    ,     __lowercase	:Optional[Any]    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:Dict    ,     __lowercase	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:Tuple    ,     __lowercase	:List[Any]    ,     __lowercase	:List[Any]    ,     __lowercase	:Optional[int]    ,     ):
											__lowerCamelCase   :   List[Any]       =True
											__lowerCamelCase   :   str       =True
											__lowerCamelCase   :   List[str]       =FalconForCausalLM(config=A_      )
											model.to(A_      )
											model.eval()

											# first forward pass
											__lowerCamelCase   :   Optional[int]       =model(
											    A_    ,     attention_mask=A_    ,     encoder_hidden_states=A_    ,     encoder_attention_mask=A_    ,     use_cache=A_    ,     )
											__lowerCamelCase   :   Tuple       =outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											__lowerCamelCase   :   List[str]       =ids_tensor((self.batch_size, 3)    ,     config.vocab_size      )
											__lowerCamelCase   :   Tuple       =ids_tensor((self.batch_size, 3)    ,     vocab_size=2      )

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

											__lowerCamelCase   :   Optional[Any]       =model(
											    A_    ,     attention_mask=A_    ,     encoder_hidden_states=A_    ,     encoder_attention_mask=A_    ,     output_hidden_states=A_    ,     )['''hidden_states'''][0]
											__lowerCamelCase   :   str       =model(
											    A_    ,     attention_mask=A_    ,     encoder_hidden_states=A_    ,     encoder_attention_mask=A_    ,     past_key_values=A_    ,     output_hidden_states=A_    ,     )['''hidden_states'''][0]

											# select random slice
											__lowerCamelCase   :   Tuple       =ids_tensor((1,)    ,     output_from_past.shape[-1]      ).item()
											__lowerCamelCase   :   Dict       =output_from_no_past[:, -3:, random_slice_idx].detach()
											__lowerCamelCase   :   Tuple       =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(A_    ,     A_    ,     atol=1e-3      )      )




				def        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase   :   Any       =self.prepare_config_and_inputs()
											(
											    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    (
											    __lowerCamelCase		
											)     ,    		
											)   :   str       =config_and_inputs
											__lowerCamelCase   :   Optional[Any]       ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict

@require_torch
class 		SCREAMING_SNAKE_CASE_				(     __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Dict									=		(
				    (
				        FalconModel,
				        FalconForCausalLM,
				        FalconForSequenceClassification,
				        FalconForTokenClassification,
				        FalconForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				__snake_case :							Dict									=		(FalconForCausalLM,) if is_torch_available() else ()
				__snake_case :							List[Any]									=		(
				    {
				        """feature-extraction""": FalconModel,
				        """text-classification""": FalconForSequenceClassification,
				        """text-generation""": FalconForCausalLM,
				        """question-answering""": FalconForQuestionAnswering,
				        """token-classification""": FalconForTokenClassification,
				        """zero-shot""": FalconForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__snake_case :							List[Any]									=		False
				__snake_case :							Tuple									=		False




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Dict       =FalconModelTester(self      )
											__lowerCamelCase   :   Optional[int]       =ConfigTester(self    ,     config_class=A_    ,     hidden_size=37      )




				def        __lowercase	(  self	:Dict      ):
											self.config_tester.run_common_tests()




				def        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   int       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_      )




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase     ,    *__lowerCamelCase   :   List[Any]       =self.model_tester.prepare_config_and_inputs()
											for alibi in [True, False]:
																		__lowerCamelCase   :   Optional[int]       =alibi
																		self.model_tester.create_and_check_model(A_    ,     *A_      )




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase   :   str       =3
											__lowerCamelCase   :   Tuple       =input_dict['''input_ids''']
											__lowerCamelCase   :   List[Any]       =input_ids.ne(1      ).to(A_      )
											__lowerCamelCase   :   List[Any]       =ids_tensor([self.model_tester.batch_size]    ,     self.model_tester.type_sequence_label_size      )
											__lowerCamelCase   :   Union[str, Any]       =FalconForSequenceClassification(A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   Dict       =model(A_    ,     attention_mask=A_    ,     labels=A_      )
											self.assertEqual(result.logits.shape    ,     (self.model_tester.batch_size, self.model_tester.num_labels)      )




				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase   :   Tuple       =3
											__lowerCamelCase   :   Optional[Any]       ='''single_label_classification'''
											__lowerCamelCase   :   Union[str, Any]       =input_dict['''input_ids''']
											__lowerCamelCase   :   Tuple       =input_ids.ne(1      ).to(A_      )
											__lowerCamelCase   :   int       =ids_tensor([self.model_tester.batch_size]    ,     self.model_tester.type_sequence_label_size      )
											__lowerCamelCase   :   int       =FalconForSequenceClassification(A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   Dict       =model(A_    ,     attention_mask=A_    ,     labels=A_      )
											self.assertEqual(result.logits.shape    ,     (self.model_tester.batch_size, self.model_tester.num_labels)      )




				def        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   Any       =self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase   :   Dict       =input_dict['''input_ids''']
											__lowerCamelCase   :   Union[str, Any]       =FalconForCausalLM(A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   Dict       =model(A_    ,     use_cache=A_      )
											__lowerCamelCase   :   List[str]       =input_ids.shape[0]
											__lowerCamelCase   :   str       =model._convert_to_rw_cache(result.past_key_values      )
											__lowerCamelCase   :   Dict       =model._convert_cache_to_standard_format(A_    ,     A_      )
											for layer in range(len(A_      )      ):
																		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        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   Union[str, Any]       =self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase   :   Dict       =3
											__lowerCamelCase   :   List[Any]       ='''multi_label_classification'''
											__lowerCamelCase   :   List[str]       =input_dict['''input_ids''']
											__lowerCamelCase   :   Dict       =input_ids.ne(1      ).to(A_      )
											__lowerCamelCase   :   Dict       =ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]    ,     self.model_tester.type_sequence_label_size      ).to(torch.float      )
											__lowerCamelCase   :   List[Any]       =FalconForSequenceClassification(A_      )
											model.to(A_      )
											model.eval()
											__lowerCamelCase   :   Any       =model(A_    ,     attention_mask=A_    ,     labels=A_      )
											self.assertEqual(result.logits.shape    ,     (self.model_tester.batch_size, self.model_tester.num_labels)      )




				def        __lowercase	(  self	:Tuple      ):

											for model_class in self.all_generative_model_classes:
																		__lowerCamelCase     ,    __lowerCamelCase   :   Union[str, Any]       =self.model_tester.prepare_config_and_inputs_for_common()

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

																		__lowerCamelCase   :   Dict       =model_class(A_      ).to(A_      )
																		if "use_cache" not in inputs:
																									__lowerCamelCase   :   Dict       =True
																		__lowerCamelCase   :   List[str]       =model(**A_      )

																		# 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

																		__lowerCamelCase   :   Optional[int]       =(
																		    getattr(A_    ,     '''decoder_layers'''    ,     A_      )
																		    or getattr(A_    ,     '''num_decoder_layers'''    ,     A_      )
																		    or config.num_hidden_layers
																		)
																		__lowerCamelCase   :   Optional[int]       =getattr(A_    ,     '''num_kv_heads'''    ,     config.num_attention_heads      )
																		__lowerCamelCase   :   Any       =getattr(A_    ,     '''d_model'''    ,     config.hidden_size      )
																		__lowerCamelCase   :   Any       =embed_dim // num_attention_heads

																		__lowerCamelCase   :   Any       =outputs['''past_key_values''']
																		self.assertEqual(len(A_      )    ,     A_      )

																		__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =inputs['''input_ids'''].shape
																		for i in range(A_      ):
																									if config.new_decoder_architecture:
																																__lowerCamelCase   :   int       =config.num_attention_heads
																									elif config.multi_query:
																																__lowerCamelCase   :   Union[str, Any]       =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 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Union[str, Any]       =AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b'''      )
											__lowerCamelCase   :   Optional[int]       =FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b'''      )
											model.eval()
											model.to(A_      )
											__lowerCamelCase   :   str       =tokenizer('''My favorite food is'''    ,     return_tensors='''pt'''      ).to(A_      )

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

											__lowerCamelCase   :   Dict       =model.generate(**A_    ,     do_sample=A_    ,     max_new_tokens=19      )
											__lowerCamelCase   :   Union[str, Any]       =tokenizer.batch_decode(A_      )[0]

											self.assertEqual(A_    ,     A_      )




				@slow
				def        __lowercase	(  self	:Optional[int]      ):

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

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




				@slow
				def        __lowercase	(  self	:str      ):

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

																									# Test results are the same with and without cache
																									__lowerCamelCase   :   str       =model.generate(**A_    ,     do_sample=A_    ,     max_new_tokens=20    ,     use_cache=A_      )
																									__lowerCamelCase   :   Union[str, Any]       =model.generate(**A_    ,     do_sample=A_    ,     max_new_tokens=20    ,     use_cache=A_      )
																									self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0      )











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 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=3   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=False   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=16   ,				A_=2   ,				A_=0.02   ,				A_=3   ,				A_=4   ,				A_=None   ,				):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

        SCREAMING_SNAKE_CASE__											=			None

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

        SCREAMING_SNAKE_CASE__											=			self.get_config()

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



    def  lowercase_					(       self     ):





        '''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=A_   ,				initializer_range=self.initializer_range   ,				pad_token_id=1   ,				new_decoder_architecture=A_   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(config=A_     )
        model.to(A_     )
        model.eval()

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

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

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

        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				output_hidden_states=A_   ,				)['''hidden_states'''][0]
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				past_key_values=A_   ,				output_hidden_states=A_   ,				)['''hidden_states'''][0]

        # select random slice
        SCREAMING_SNAKE_CASE__											=			ids_tensor((1,)   ,				output_from_past.shape[-1]     ).item()
        SCREAMING_SNAKE_CASE__											=			output_from_no_past[:, -3:, random_slice_idx].detach()
        SCREAMING_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(A_   ,				A_   ,				atol=1E-3     )     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

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





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	(
        (
            FalconModel,
            FalconForCausalLM,
            FalconForSequenceClassification,
            FalconForTokenClassification,
            FalconForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase__    :		Dict				=	(FalconForCausalLM,) if is_torch_available() else ()
    lowerCamelCase__    :		List[Any]				=	(
        {
            """feature-extraction""": FalconModel,
            """text-classification""": FalconForSequenceClassification,
            """text-generation""": FalconForCausalLM,
            """question-answering""": FalconForQuestionAnswering,
            """token-classification""": FalconForTokenClassification,
            """zero-shot""": FalconForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase__    :		List[Any]				=	False
    lowerCamelCase__    :		Tuple				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FalconModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				hidden_size=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				use_cache=A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.shape[0]
        SCREAMING_SNAKE_CASE__											=			model._convert_to_rw_cache(result.past_key_values     )
        SCREAMING_SNAKE_CASE__											=			model._convert_cache_to_standard_format(A_   ,				A_     )
        for layer in range(len(A_     )     ):
            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  lowercase_					(       self     ):





        '''simple docstring'''

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




    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

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

            # 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

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

            SCREAMING_SNAKE_CASE__											=			outputs['''past_key_values''']
            self.assertEqual(len(A_     )   ,				A_     )

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			inputs['''input_ids'''].shape
            for i in range(A_     ):
                if config.new_decoder_architecture:
                    SCREAMING_SNAKE_CASE__											=			config.num_attention_heads
                elif config.multi_query:
                    SCREAMING_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 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

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

        self.assertEqual(A_   ,				A_     )



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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