
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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):
	lowerCAmelCase__         = MODEL_FOR_MASKED_LM_MAPPING
	lowerCAmelCase__         = TF_MODEL_FOR_MASKED_LM_MAPPING



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					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 )				->       Tuple:
					lowerCamelCase_						=			pipeline(task="fill-mask"						,  model="sshleifer/tiny-distilroberta-base"						,  top_k=2						,  framework="tf" )
					lowerCamelCase_						=			unmasker("My name is <mask>" )
					self.assertEqual(
					    nested_simplify(lowercase						,  decimals=6 )						,  [
					        {"sequence": "My name is grouped", "score": 2.1e-05, "token": 38015, "token_str": " grouped"},
					        {"sequence": "My name is accuser", "score": 2.1e-05, "token": 25506, "token_str": " accuser"},
					    ]						,  )

					lowerCamelCase_						=			unmasker("The largest city in France is <mask>" )
					self.assertEqual(
					    nested_simplify(lowercase						,  decimals=6 )						,  [
					        {
					            "sequence": "The largest city in France is grouped",
					            "score": 2.1e-05,
					            "token": 38015,
					            "token_str": " grouped",
					        },
					        {
					            "sequence": "The largest city in France is accuser",
					            "score": 2.1e-05,
					            "token": 25506,
					            "token_str": " accuser",
					        },
					    ]						,  )

					lowerCamelCase_						=			unmasker("My name is <mask>"						,  targets=[" Patrick", " Clara", " Teven"]						,  top_k=3 )
					self.assertEqual(
					    nested_simplify(lowercase						,  decimals=6 )						,  [
					        {"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
					        {"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
					        {"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
					    ]						,  )



	@require_torch
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			pipeline(task="fill-mask"						,  model="sshleifer/tiny-distilroberta-base"						,  top_k=2						,  framework="pt" )

					lowerCamelCase_						=			unmasker("My name is <mask>" )
					self.assertEqual(
					    nested_simplify(lowercase						,  decimals=6 )						,  [
					        {"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
					        {"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
					    ]						,  )

					lowerCamelCase_						=			unmasker("The largest city in France is <mask>" )
					self.assertEqual(
					    nested_simplify(lowercase						,  decimals=6 )						,  [
					        {
					            "sequence": "The largest city in France is Maul",
					            "score": 2.2e-05,
					            "token": 35676,
					            "token_str": " Maul",
					        },
					        {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
					    ]						,  )

					lowerCamelCase_						=			unmasker("My name is <mask>"						,  targets=[" Patrick", " Clara", " Teven"]						,  top_k=3 )
					self.assertEqual(
					    nested_simplify(lowercase						,  decimals=6 )						,  [
					        {"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
					        {"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
					        {"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
					    ]						,  )

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

					self.assertEqual(
					    nested_simplify(lowercase						,  decimals=6 )						,  [
					        [
					            {
					                "score": 2.2e-05,
					                "token": 35676,
					                "token_str": " Maul",
					                "sequence": "<s>My name is Maul<mask></s>",
					            },
					            {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
					        ],
					        [
					            {
					                "score": 2.2e-05,
					                "token": 35676,
					                "token_str": " Maul",
					                "sequence": "<s>My name is<mask> Maul</s>",
					            },
					            {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
					        ],
					    ]						,  )



	@require_torch_gpu
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			pipeline("fill-mask"						,  model="hf-internal-testing/tiny-random-distilbert"						,  device=0						,  framework="pt" )

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

					lowerCamelCase_						=			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(lowercase						,  lowercase )



	@slow
	@require_torch
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			pipeline(task="fill-mask"						,  model="distilroberta-base"						,  top_k=2						,  framework="pt" )
					self.run_large_test(lowercase )



	@slow
	@require_tf
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			pipeline(task="fill-mask"						,  model="distilroberta-base"						,  top_k=2						,  framework="tf" )
					self.run_large_test(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       int:
					lowerCamelCase_						=			unmasker("My name is <mask>" )
					self.assertEqual(
					    nested_simplify(lowercase )						,  [
					        {"sequence": "My name is John", "score": 0.0_0_8, "token": 610, "token_str": " John"},
					        {"sequence": "My name is Chris", "score": 0.0_0_7, "token": 1573, "token_str": " Chris"},
					    ]						,  )
					lowerCamelCase_						=			unmasker("The largest city in France is <mask>" )
					self.assertEqual(
					    nested_simplify(lowercase )						,  [
					        {
					            "sequence": "The largest city in France is Paris",
					            "score": 0.2_5_1,
					            "token": 2201,
					            "token_str": " Paris",
					        },
					        {
					            "sequence": "The largest city in France is Lyon",
					            "score": 0.2_1_4,
					            "token": 12790,
					            "token_str": " Lyon",
					        },
					    ]						,  )

					lowerCamelCase_						=			unmasker("My name is <mask>"						,  targets=[" Patrick", " Clara", " Teven"]						,  top_k=3 )
					self.assertEqual(
					    nested_simplify(lowercase )						,  [
					        {"sequence": "My name is Patrick", "score": 0.0_0_5, "token": 3499, "token_str": " Patrick"},
					        {"sequence": "My name is Clara", "score": 0.0_0_0, "token": 13606, "token_str": " Clara"},
					        {"sequence": "My name is Te", "score": 0.0_0_0, "token": 2941, "token_str": " Te"},
					    ]						,  )



	@require_torch
	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			pipeline(task="fill-mask"						,  model="sshleifer/tiny-distilroberta-base"						,  framework="pt" )
					lowerCamelCase_						=			None
					lowerCamelCase_						=			None
					self.run_pipeline_test(lowercase						,  [] )



	@require_tf
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			pipeline(task="fill-mask"						,  model="sshleifer/tiny-distilroberta-base"						,  framework="tf" )
					lowerCamelCase_						=			None
					lowerCamelCase_						=			None
					self.run_pipeline_test(lowercase						,  [] )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       Union[str, Any]:
					if tokenizer is None or tokenizer.mask_token_id is None:
									self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)" )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Dict:
					lowerCamelCase_						=			fill_masker.tokenizer
					lowerCamelCase_						=			fill_masker.model

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

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

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

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

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



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

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

					# Score equivalence
					lowerCamelCase_						=			fill_masker(f'This is a {tokenizer.mask_token}'						,  targets=lowercase )
					lowerCamelCase_						=			[top_mask["token_str"] for top_mask in outputs]
					lowerCamelCase_						=			[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(lowercase ) == set(lowercase ):
									lowerCamelCase_						=			fill_masker(f'This is a {tokenizer.mask_token}'						,  targets=lowercase )
									lowerCamelCase_						=			[top_mask["score"] for top_mask in unmasked_targets]
									self.assertEqual(nested_simplify(lowercase )						,  nested_simplify(lowercase ) )

					# Raises with invalid
					with self.assertRaises(lowercase ):
									lowerCamelCase_						=			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(lowercase ):
													lowerCamelCase_						=			fill_masker(f'This is a {tokenizer.mask_token}'						,  targets=[""] )
									with self.assertRaises(lowercase ):
													lowerCamelCase_						=			fill_masker(f'This is a {tokenizer.mask_token}'						,  targets="" )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       List[Any]:
					lowerCamelCase_						=			FillMaskPipeline(model=lowercase						,  tokenizer=lowercase						,  top_k=2 )
					lowerCamelCase_						=			fill_masker(f'This is a {tokenizer.mask_token}' )
					self.assertEqual(
					    lowercase						,  [
					        {"sequence": ANY(lowercase ), "score": ANY(lowercase ), "token": ANY(lowercase ), "token_str": ANY(lowercase )},
					        {"sequence": ANY(lowercase ), "score": ANY(lowercase ), "token": ANY(lowercase ), "token_str": ANY(lowercase )},
					    ]						,  )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       List[Any]:
					lowerCamelCase_						=			tokenizer.get_vocab()
					lowerCamelCase_						=			FillMaskPipeline(model=lowercase						,  tokenizer=lowercase )

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

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       List[Any]:
					lowerCamelCase_						=			FillMaskPipeline(model=lowercase						,  tokenizer=lowercase )
					lowerCamelCase_						=			tokenizer.get_vocab()
					# String duplicates + id duplicates
					lowerCamelCase_						=			sorted(vocab.keys() )[:3]
					lowerCamelCase_						=			[targets[0], targets[1], targets[0], targets[2], targets[1]]
					lowerCamelCase_						=			fill_masker(f'My name is {tokenizer.mask_token}'						,  targets=lowercase						,  top_k=10 )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Dict:
					lowerCamelCase_						=			FillMaskPipeline(model=lowercase						,  tokenizer=lowercase )

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




from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import numpy
			import tensorflow as tf

			from transformers.models.esm.modeling_tf_esm import (
			    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFEsmForMaskedLM,
			    TFEsmForSequenceClassification,
			    TFEsmForTokenClassification,
			    TFEsmModel,
			)




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  )				->       Optional[int]:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			13
					lowerCamelCase_						=			7
					lowerCamelCase_						=			True
					lowerCamelCase_						=			True
					lowerCamelCase_						=			True
					lowerCamelCase_						=			99
					lowerCamelCase_						=			32
					lowerCamelCase_						=			2
					lowerCamelCase_						=			4
					lowerCamelCase_						=			37
					lowerCamelCase_						=			"gelu"
					lowerCamelCase_						=			0.1
					lowerCamelCase_						=			0.1
					lowerCamelCase_						=			512
					lowerCamelCase_						=			16
					lowerCamelCase_						=			2
					lowerCamelCase_						=			0.0_2
					lowerCamelCase_						=			3
					lowerCamelCase_						=			4
					lowerCamelCase_						=			None



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )

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

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

					lowerCamelCase_						=			EsmConfig(
					    vocab_size=self.vocab_size						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  pad_token_id=1						,  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						,  )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			self.prepare_config_and_inputs()

					lowerCamelCase_						=			True
					lowerCamelCase_						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  vocab_size=2 )

					return (
					    config,
					    input_ids,
					    input_mask,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			TFEsmModel(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			[input_ids, input_mask]
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			model(lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       Tuple:
					lowerCamelCase_						=			True

					lowerCamelCase_						=			TFEsmModel(config=lowercase )
					lowerCamelCase_						=			{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "encoder_hidden_states": encoder_hidden_states,
					    "encoder_attention_mask": encoder_attention_mask,
					}
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			[input_ids, input_mask]
					lowerCamelCase_						=			model(lowercase						,  encoder_hidden_states=lowercase )

					# Also check the case where encoder outputs are not passed
					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Optional[int]:
					lowerCamelCase_						=			TFEsmForMaskedLM(config=lowercase )
					lowerCamelCase_						=			model([input_ids, input_mask] )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					lowerCamelCase_						=			self.num_labels
					lowerCamelCase_						=			TFEsmForTokenClassification(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			self.prepare_config_and_inputs()
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			config_and_inputs
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict




@require_tf
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (
	    (
	        TFEsmModel,
	        TFEsmForMaskedLM,
	        TFEsmForSequenceClassification,
	        TFEsmForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase__         = (
	    {
	        'feature-extraction': TFEsmModel,
	        'fill-mask': TFEsmForMaskedLM,
	        'text-classification': TFEsmForSequenceClassification,
	        'token-classification': TFEsmForTokenClassification,
	        'zero-shot': TFEsmForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			TFEsmModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  hidden_size=37 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					self.config_tester.run_common_tests()



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*lowercase )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			TFEsmModel.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )



	@unittest.skip("Protein models do not support embedding resizing." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					pass



	@unittest.skip("Protein models do not support embedding resizing." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					pass



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowerCamelCase_						=			model_class(lowercase )
									assert isinstance(model.get_input_embeddings()						,  tf.keras.layers.Layer )
									if model_class is TFEsmForMaskedLM:
													# Output embedding test differs from the main test because they're a matrix, not a layer
													lowerCamelCase_						=			model.get_bias()
													assert isinstance(lowercase						,  lowercase )
													for k, v in name.items():
																	assert isinstance(lowercase						,  tf.Variable )
									else:
													lowerCamelCase_						=			model.get_output_embeddings()
													assert x is None
													lowerCamelCase_						=			model.get_bias()
													assert name is None




@require_tf
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )

					lowerCamelCase_						=			tf.constant([[0, 1, 2, 3, 4, 5]] )
					lowerCamelCase_						=			model(lowercase )[0]
					lowerCamelCase_						=			[1, 6, 33]
					self.assertEqual(list(output.numpy().shape )						,  lowercase )
					# compare the actual values for a slice.
					lowerCamelCase_						=			tf.constant(
					    [
					        [
					            [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
					            [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
					            [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
					        ]
					    ] )
					self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,  expected_slice.numpy()						,  atol=1e-2 ) )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )

					lowerCamelCase_						=			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
					lowerCamelCase_						=			model(lowercase )[0]
					# compare the actual values for a slice.
					lowerCamelCase_						=			tf.constant(
					    [
					        [
					            [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
					            [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
					            [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
					        ]
					    ] )
					self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,  expected_slice.numpy()						,  atol=1e-4 ) )


