



import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		lowerCAmelCase_			:     Union[str, Any]									= int(__UpperCamelCase )
		lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= t // 3600, (t // 60) % 60, t % 60
		return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""


def 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		int=300 ):
		# docstyle-ignore
		return f"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """


def 			UpperCamelCase(  __UpperCamelCase							:		int ):
		lowerCAmelCase_			:     str									= '''<table border="1" class="dataframe">\n'''
		html_code += """  <thead>\n <tr style="text-align: left;">\n"""
		for i in items[0]:
				html_code += f"""      <th>{i}</th>\n"""
		html_code += "    </tr>\n  </thead>\n  <tbody>\n"
		for line in items[1:]:
				html_code += "    <tr>\n"
				for elt in line:
						lowerCAmelCase_			:     Optional[Any]									= f"""{elt:.6f}""" if isinstance(__UpperCamelCase     ,__UpperCamelCase ) else str(__UpperCamelCase )
						html_code += f"""      <td>{elt}</td>\n"""
				html_code += "    </tr>\n"
		html_code += "  </tbody>\n</table><p>"
		return html_code



class 				__snake_case						:
							_a			      = 5
							_a			      = 0.2




							def __init__(       self     :       Optional[Any]				,				A_     :       int				,				A_     :       Optional[str] = None				,				A_     :       bool = True				,				A_     :       Optional["NotebookTrainingTracker"] = None				,				A_     :       int = 3_0_0				,				):
									lowerCAmelCase_			:     Union[str, Any]									= total
									lowerCAmelCase_			:     int									= '''''' if prefix is None else prefix
									lowerCAmelCase_			:     Optional[Any]									= leave
									lowerCAmelCase_			:     Union[str, Any]									= parent
									lowerCAmelCase_			:     Any									= width
									lowerCAmelCase_			:     Optional[Any]									= None
									lowerCAmelCase_			:     Tuple									= None
									lowerCAmelCase_			:     str									= None




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       int				,				A_     :       bool = False				,				A_     :       str = None):
									lowerCAmelCase_			:     Dict									= value
									if comment is not None:
											lowerCAmelCase_			:     List[str]									= comment
									if self.last_value is None:
											lowerCAmelCase_			:     List[str]									= time.time()
											lowerCAmelCase_			:     Dict									= value
											lowerCAmelCase_			:     Union[str, Any]									= None
											lowerCAmelCase_			:     str									= self.warmup
											lowerCAmelCase_			:     List[Any]									= 1
											self.update_bar(A_)
									elif value <= self.last_value and not force_update:
											return
									elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for				,				self.total):
											if self.first_calls > 0:
													self.first_calls -= 1
											lowerCAmelCase_			:     Tuple									= time.time()
											lowerCAmelCase_			:     List[str]									= current_time - self.start_time
											# We could have value = self.start_value if the update is called twixe with the same start value.
											if value > self.start_value:
													lowerCAmelCase_			:     int									= self.elapsed_time / (value - self.start_value)
											else:
													lowerCAmelCase_			:     str									= None
											if value >= self.total:
													lowerCAmelCase_			:     int									= self.total
													lowerCAmelCase_			:     List[str]									= None
													if not self.leave:
															self.close()
											elif self.average_time_per_item is not None:
													lowerCAmelCase_			:     List[str]									= self.average_time_per_item * (self.total - value)
											self.update_bar(A_)
											lowerCAmelCase_			:     Tuple									= value
											lowerCAmelCase_			:     Optional[int]									= current_time
											if self.average_time_per_item is None:
													lowerCAmelCase_			:     List[Any]									= 1
											else:
													lowerCAmelCase_			:     Any									= max(int(self.update_every / self.average_time_per_item)				,				1)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				A_     :       List[str]=None):
									lowerCAmelCase_			:     str									= ''' ''' * (len(str(self.total)) - len(str(A_))) + str(A_)
									if self.elapsed_time is None:
											lowerCAmelCase_			:     str									= F"""[{spaced_value}/{self.total} : < :"""
									elif self.predicted_remaining is None:
											lowerCAmelCase_			:     Tuple									= F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)}"""
									else:
											lowerCAmelCase_			:     Any									= (
											    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <"""
											    F""" {format_time(self.predicted_remaining)}"""
											)
											self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
									self.label += "]" if self.comment is None or len(self.comment) == 0 else F""", {self.comment}]"""
									self.display()




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Union[str, Any]									= html_progress_bar(self.value				,				self.total				,				self.prefix				,				self.label				,				self.width)
									if self.parent is not None:
											# If this is a child bar, the parent will take care of the display.
											self.parent.display()
											return
									if self.output is None:
											lowerCAmelCase_			:     int									= disp.display(disp.HTML(self.html_code)				,				display_id=A_)
									else:
											self.output.update(disp.HTML(self.html_code))







							def    UpperCAmelCase__			(       self     :       Optional[int]):
									if self.parent is None and self.output is not None:
											self.output.update(disp.HTML(''''''))





class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Union[str, Any]				,				A_     :       Optional[int]				,				A_     :       Dict=None):
									super().__init__(A_)
									lowerCAmelCase_			:     int									= None if column_names is None else [column_names]
									lowerCAmelCase_			:     Optional[int]									= None




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Optional[Any]									= html_progress_bar(self.value				,				self.total				,				self.prefix				,				self.label				,				self.width)
									if self.inner_table is not None:
											self.html_code += text_to_html_table(self.inner_table)
									if self.child_bar is not None:
											self.html_code += self.child_bar.html_code
									if self.output is None:
											lowerCAmelCase_			:     Optional[Any]									= disp.display(disp.HTML(self.html_code)				,				display_id=A_)
									else:
											self.output.update(disp.HTML(self.html_code))




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[int]):
									if self.inner_table is None:
											lowerCAmelCase_			:     List[Any]									= [list(values.keys()), list(values.values())]
									else:
											lowerCAmelCase_			:     List[Any]									= self.inner_table[0]
											if len(self.inner_table) == 1:
													# We give a chance to update the column names at the first iteration
													for key in values.keys():
															if key not in columns:
																	columns.append(A_)
													lowerCAmelCase_			:     str									= columns
											self.inner_table.append([values[c] for c in columns])




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Optional[Any]				,				A_     :       Any=None				,				A_     :       Optional[int]=3_0_0):
									lowerCAmelCase_			:     Any									= NotebookProgressBar(A_				,				prefix=A_				,				parent=self				,				width=A_)
									return self.child_bar







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Dict									= None
									self.display()





class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Dict):
									lowerCAmelCase_			:     str									= None
									lowerCAmelCase_			:     List[str]									= None
									lowerCAmelCase_			:     Tuple									= False




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[Any]				,				A_     :       List[Any]				,				A_     :       Union[str, Any]				,				**A_     :       str):
									lowerCAmelCase_			:     str									= '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
									lowerCAmelCase_			:     Optional[int]									= 0
									lowerCAmelCase_			:     Any									= 0
									lowerCAmelCase_			:     List[str]									= [self.first_column] + ['''Training Loss''']
									if args.evaluation_strategy != IntervalStrategy.NO:
											column_names.append('''Validation Loss''')
									lowerCAmelCase_			:     str									= NotebookTrainingTracker(state.max_steps				,				A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Optional[Any]				,				A_     :       Optional[int]				,				A_     :       List[str]				,				**A_     :       List[Any]):
									lowerCAmelCase_			:     int									= int(state.epoch) if int(state.epoch) == state.epoch else F"""{state.epoch:.2f}"""
									self.training_tracker.update(
									    state.global_step + 1				,				comment=F"""Epoch {epoch}/{state.num_train_epochs}"""				,				force_update=self._force_next_update				,				)
									lowerCAmelCase_			:     Dict									= False




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       List[str]				,				A_     :       Optional[Any]				,				A_     :       int				,				A_     :       List[str]=None				,				**A_     :       List[str]):
									if not has_length(A_):
											return
									if self.prediction_bar is None:
											if self.training_tracker is not None:
													lowerCAmelCase_			:     int									= self.training_tracker.add_child(len(A_))
											else:
													lowerCAmelCase_			:     List[str]									= NotebookProgressBar(len(A_))
											self.prediction_bar.update(1)
									else:
											self.prediction_bar.update(self.prediction_bar.value + 1)




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Optional[Any]				,				A_     :       List[str]				,				A_     :       Dict				,				**A_     :       Tuple):
									if self.prediction_bar is not None:
											self.prediction_bar.close()
									lowerCAmelCase_			:     List[Any]									= None




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Optional[Any]				,				A_     :       str				,				A_     :       Any				,				A_     :       Tuple=None				,				**A_     :       str):
									# Only for when there is no evaluation
									if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
											lowerCAmelCase_			:     List[str]									= {'''Training Loss''': logs['''loss''']}
											# First column is necessarily Step sine we're not in epoch eval strategy
											lowerCAmelCase_			:     int									= state.global_step
											self.training_tracker.write_line(A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Union[str, Any]				,				A_     :       Tuple				,				A_     :       Tuple				,				A_     :       Optional[Any]=None				,				**A_     :       Optional[Any]):
									if self.training_tracker is not None:
											lowerCAmelCase_			:     Any									= {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
											for log in reversed(state.log_history):
													if "loss" in log:
															lowerCAmelCase_			:     Dict									= log['''loss''']
															break

											if self.first_column == "Epoch":
													lowerCAmelCase_			:     str									= int(state.epoch)
											else:
													lowerCAmelCase_			:     Optional[int]									= state.global_step
											lowerCAmelCase_			:     Optional[Any]									= '''eval'''
											for k in metrics:
													if k.endswith('''_loss'''):
															lowerCAmelCase_			:     str									= re.sub(r'''\_loss$'''				,				''''''				,				A_)
											lowerCAmelCase_			:     Any									= metrics.pop('''total_flos'''				,				A_)
											lowerCAmelCase_			:     List[Any]									= metrics.pop('''epoch'''				,				A_)
											lowerCAmelCase_			:     Optional[Any]									= metrics.pop(F"""{metric_key_prefix}_runtime"""				,				A_)
											lowerCAmelCase_			:     List[Any]									= metrics.pop(F"""{metric_key_prefix}_samples_per_second"""				,				A_)
											lowerCAmelCase_			:     List[Any]									= metrics.pop(F"""{metric_key_prefix}_steps_per_second"""				,				A_)
											lowerCAmelCase_			:     Tuple									= metrics.pop(F"""{metric_key_prefix}_jit_compilation_time"""				,				A_)
											for k, v in metrics.items():
													if k == F"""{metric_key_prefix}_loss""":
															lowerCAmelCase_			:     Optional[int]									= v
													else:
															lowerCAmelCase_			:     str									= k.split('''_''')
															lowerCAmelCase_			:     List[str]									= ''' '''.join([part.capitalize() for part in splits[1:]])
															lowerCAmelCase_			:     List[Any]									= v
											self.training_tracker.write_line(A_)
											self.training_tracker.remove_child()
											lowerCAmelCase_			:     Any									= None
											# Evaluation takes a long time so we should force the next update.
											lowerCAmelCase_			:     str									= True







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Optional[int]				,				A_     :       Union[str, Any]				,				A_     :       Any				,				**A_     :       int):
									self.training_tracker.update(
									    state.global_step				,				comment=F"""Epoch {int(state.epoch)}/{state.num_train_epochs}"""				,				force_update=A_)
									lowerCAmelCase_			:     str									= None







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 ) )


