



import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

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 EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
			from transformers.models.esm.modeling_esm import (
			    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    EsmEmbeddings,
			    create_position_ids_from_input_ids,
			)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=7	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=33	,__lowerCamelCase=32	,__lowerCamelCase=5	,__lowerCamelCase=4	,__lowerCamelCase=37	,__lowerCamelCase="gelu"	,__lowerCamelCase=0.1	,__lowerCamelCase=0.1	,__lowerCamelCase=5_12	,__lowerCamelCase=16	,__lowerCamelCase=2	,__lowerCamelCase=0.02	,__lowerCamelCase=3	,__lowerCamelCase=4	,__lowerCamelCase=None	,)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  parent
						lowerCAmelCase__  :       Union[str, Any]				=  batch_size
						lowerCAmelCase__  :       List[Any]				=  seq_length
						lowerCAmelCase__  :       str				=  is_training
						lowerCAmelCase__  :       Tuple				=  use_input_mask
						lowerCAmelCase__  :       Dict				=  use_token_type_ids
						lowerCAmelCase__  :       List[Any]				=  use_labels
						lowerCAmelCase__  :       int				=  vocab_size
						lowerCAmelCase__  :       Tuple				=  hidden_size
						lowerCAmelCase__  :       List[str]				=  num_hidden_layers
						lowerCAmelCase__  :       List[Any]				=  num_attention_heads
						lowerCAmelCase__  :       Dict				=  intermediate_size
						lowerCAmelCase__  :       str				=  hidden_act
						lowerCAmelCase__  :       Optional[int]				=  hidden_dropout_prob
						lowerCAmelCase__  :       Dict				=  attention_probs_dropout_prob
						lowerCAmelCase__  :       Union[str, Any]				=  max_position_embeddings
						lowerCAmelCase__  :       Union[str, Any]				=  type_vocab_size
						lowerCAmelCase__  :       List[Any]				=  type_sequence_label_size
						lowerCAmelCase__  :       List[str]				=  initializer_range
						lowerCAmelCase__  :       str				=  num_labels
						lowerCAmelCase__  :       Optional[int]				=  num_choices
						lowerCAmelCase__  :       int				=  scope



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size				)

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

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

						lowerCAmelCase__  :       Union[str, Any]				=  self.get_config()

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



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						return EsmConfig(
						    vocab_size=self.vocab_size	,hidden_size=self.hidden_size	,pad_token_id=1	,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	,initializer_range=self.initializer_range	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  EsmModel(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       str				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase				)

						self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)				)
						self.parent.assertEqual(result.pooler_output.shape	,(self.batch_size, self.hidden_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  EsmForMaskedLM(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.num_labels
						lowerCAmelCase__  :       Optional[int]				=  EsmForTokenClassification(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.num_labels)				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.prepare_config_and_inputs()
						(
						    lowerCAmelCase__	
						)  :       List[str]				=  config_and_inputs
						lowerCAmelCase__  :       str				=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
						return config, inputs_dict



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=False

		snake_case_			=(
		    (
		        EsmForMaskedLM,
		        EsmModel,
		        EsmForSequenceClassification,
		        EsmForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		snake_case_			=()
		snake_case_			=(
		    {
		        """feature-extraction""": EsmModel,
		        """fill-mask""": EsmForMaskedLM,
		        """text-classification""": EsmForSequenceClassification,
		        """token-classification""": EsmForTokenClassification,
		        """zero-shot""": EsmForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		snake_case_			=True



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

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  EsmModelTester(self				)
						lowerCAmelCase__  :       int				=  ConfigTester(self	,config_class=__lowerCamelCase	,hidden_size=37				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase__  :       List[Any]				=  type
										self.model_tester.create_and_check_model(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__  :       int				=  EsmModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()[0]
						lowerCAmelCase__  :       int				=  EsmEmbeddings(config=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[int]				=  torch.as_tensor([[12, 31, 13, model.padding_idx]]				)
						lowerCAmelCase__  :       str				=  torch.as_tensor(
						    [
						        [
						            0 + model.padding_idx + 1,
						            1 + model.padding_idx + 1,
						            2 + model.padding_idx + 1,
						            model.padding_idx,
						        ]
						    ]				)
						lowerCAmelCase__  :       List[Any]				=  create_position_ids_from_input_ids(__lowerCamelCase	,model.padding_idx				)
						self.assertEqual(position_ids.shape	,expected_positions.shape				)
						self.assertTrue(torch.all(torch.eq(__lowerCamelCase	,__lowerCamelCase				)				)				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs()[0]
						lowerCAmelCase__  :       Union[str, Any]				=  EsmEmbeddings(config=__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  torch.empty(2	,4	,30				)
						lowerCAmelCase__  :       Tuple				=  [
						    0 + embeddings.padding_idx + 1,
						    1 + embeddings.padding_idx + 1,
						    2 + embeddings.padding_idx + 1,
						    3 + embeddings.padding_idx + 1,
						]
						lowerCAmelCase__  :       Tuple				=  torch.as_tensor([expected_single_positions, expected_single_positions]				)
						lowerCAmelCase__  :       Optional[int]				=  embeddings.create_position_ids_from_inputs_embeds(__lowerCamelCase				)
						self.assertEqual(position_ids.shape	,expected_positions.shape				)
						self.assertTrue(torch.all(torch.eq(__lowerCamelCase	,__lowerCamelCase				)				)				)



		@unittest.skip('''Esm does not support embedding resizing'''				)
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						pass



		@unittest.skip('''Esm does not support embedding resizing'''				)
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						pass



		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						pass



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						with torch.no_grad():
										lowerCAmelCase__  :       Tuple				=  EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''				)
										model.eval()
										lowerCAmelCase__  :       Optional[Any]				=  torch.tensor([[0, 1, 2, 3, 4, 5]]				)
										lowerCAmelCase__  :       List[str]				=  model(__lowerCamelCase				)[0]

										lowerCAmelCase__  :       Optional[int]				=  33

										lowerCAmelCase__  :       Tuple				=  torch.Size((1, 6, vocab_size)				)
										self.assertEqual(output.shape	,__lowerCamelCase				)

										lowerCAmelCase__  :       List[Any]				=  torch.tensor(
										    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]				)
										self.assertTrue(torch.allclose(output[:, :3, :3]	,__lowerCamelCase	,atol=1e-4				)				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						with torch.no_grad():
										lowerCAmelCase__  :       Optional[int]				=  EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''				)
										model.eval()

										lowerCAmelCase__  :       Optional[Any]				=  torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]				)
										lowerCAmelCase__  :       Tuple				=  model(__lowerCamelCase				)[0]
										# compare the actual values for a slice.
										lowerCAmelCase__  :       int				=  torch.tensor(
										    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]				)
										self.assertTrue(torch.allclose(output[:, :3, :3]	,__lowerCamelCase	,atol=1e-4				)				)






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

