




import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


UpperCamelCase__		=						{}
for task, _ in pipeline_test_mapping.items():
							UpperCamelCase__		=						{'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
UpperCamelCase__		=						{
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}






def  snake_case (							A      )							->							List[Any]:




	'''simple docstring'''


	if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "pt"
	elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "tf"
	elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "flax"
	else:
		return None






def  snake_case (							A						,       A      )							->							List[Any]:




	'''simple docstring'''


	if PIPELINE_TEST_MAPPING[task].get(A						,       A      ) is not None:
		return PIPELINE_TEST_MAPPING[task][framework]

	lowerCamelCase_							:		Union[str, Any]							       =							pipeline_test_mapping[task]['test']
	lowerCamelCase_							:		List[Any]							       =							None

	if framework == "pt":
		lowerCamelCase_							:		int							       =							getattr(A						,       'model_mapping'						,       A      )
	elif framework == "tf":
		lowerCamelCase_							:		Tuple							       =							getattr(A						,       'tf_model_mapping'						,       A      )

	if mapping is not None:
		lowerCamelCase_							:		Union[str, Any]							       =							dict(mapping.items()      )

	# cache the results
	lowerCamelCase_							:		int							       =							mapping
	return mapping






def  snake_case (							A						,       A      )							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		str							       =							get_framework(A      )
	if framework is None:
		return None
	lowerCamelCase_							:		List[str]							       =							get_mapping_for_task(A						,       A      )
	if mapping is None:
		return None

	lowerCamelCase_							:		Optional[Any]							       =							list({model_class.config_class for model_class in test_class.all_model_classes}      )
	if len(A      ) != 1:
		raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'      )

	# This could be a list/tuple of model classes, but it's rare.
	lowerCamelCase_							:		Dict							       =							mapping.get(config_classes[0]						,       A      )
	if isinstance(A						,       (tuple, list)      ):
		lowerCamelCase_							:		Optional[Any]							       =							sorted(A						,       key=lambda A      : x.__name__      )

	return model_class






def  snake_case (							A      )							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[Any]							       =							[(task, get_model_for_pipeline_test(A						,       A      )) for task in pipeline_test_mapping]
	lowerCamelCase_							:		int							       =							sorted([(task, model) for task, model in mapping if model is not None]						,       key=lambda A      : x[0]      )

	return dict(A      )






def  snake_case (							A      )							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		int							       =							get_framework(A      )
	if framework == "pt":
		lowerCamelCase_							:		List[str]							       =							'torch'
	lowerCamelCase_							:		List[Any]							       =							'{}'

	lowerCamelCase_							:		int							       =							get_pipeline_model_mapping(A      )
	if len(A      ) == 0:
		return ""

	lowerCamelCase_							:		Optional[Any]							       =							[]
	for task, model_classes in mapping.items():
		if isinstance(A						,       (tuple, list)      ):
			# A list/tuple of model classes
			lowerCamelCase_							:		Tuple							       =							'(' + ', '.join([x.__name__ for x in model_classes]      ) + ')'
		else:
			# A single model class
			lowerCamelCase_							:		Tuple							       =							model_classes.__name__
		texts.append(f"\"{task}\": {value}"      )
	lowerCamelCase_							:		Optional[Any]							       =							'{' + ', '.join(A      ) + '}'
	lowerCamelCase_							:		Tuple							       =							f"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"

	return text






def  snake_case (							A      )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		int							       =							{'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
	if not issubclass(A						,       unittest.TestCase      ):
		return False
	return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]      )      ) > 0






def  snake_case (							A      )							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		int							       =							[x for x in get_test_classes(A      ) if is_valid_test_class(A      )]

	lowerCamelCase_							:		Union[str, Any]							       =							None
	for test_class in test_classes:
		# If a test class has defined `pipeline_model_mapping`, let's take it
		if getattr(A						,       'pipeline_model_mapping'						,       A      ) is not None:
			lowerCamelCase_							:		Optional[int]							       =							test_class
			break
    # Take the test class with the shortest name (just a heuristic)
	if target_test_class is None and len(A      ) > 0:
		lowerCamelCase_							:		Optional[Any]							       =							sorted(A						,       key=lambda A      : (len(x.__name__      ), x.__name__)      )[0]

	return target_test_class






def  snake_case (							A						,       A						,       A      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							start_idx
	for idx, line in enumerate(lines[start_idx:]      ):
		lowerCamelCase_							:		Union[str, Any]							       =							len(A      ) - len(line.lstrip()      )
		if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
			lowerCamelCase_							:		Union[str, Any]							       =							start_idx + idx
		elif idx > 0 and indent <= indent_level:
			# Outside the definition block of `pipeline_model_mapping`
			break

	return end_idx






def  snake_case (							A						,       A=False      )							->							str:




	'''simple docstring'''


	if getattr(A						,       'pipeline_model_mapping'						,       A      ) is not None:
		if not overwrite:
			return "", -1

	lowerCamelCase_							:		str							       =							get_pipeline_model_mapping_string(A      )
	if len(A      ) == 0:
		return "", -1
	lowerCamelCase_							:		Dict							       =							line_to_add + '\n'

	# The code defined the class `test_class`
	lowerCamelCase_       ,      lowerCamelCase_							:		Optional[int]							       =							inspect.getsourcelines(A      )
	# `inspect` gives the code for an object, including decorator(s) if any.
	# We (only) need the exact line of the class definition.
	for idx, line in enumerate(A      ):
		if line.lstrip().startswith('class '      ):
			lowerCamelCase_							:		Union[str, Any]							       =							class_lines[idx:]
			class_start_line_no += idx
			break
	lowerCamelCase_							:		Optional[Any]							       =							class_start_line_no + len(A      ) - 1

	# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
	# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
	# block that appears in a `test_class`.
	lowerCamelCase_							:		Union[str, Any]							       =							None
	# The indent level of the line at `class_lines[start_idx]` (if defined)
	lowerCamelCase_							:		str							       =							0
	# To record if `pipeline_model_mapping` is found in `test_class`.
	lowerCamelCase_							:		str							       =							None
	for idx, line in enumerate(A      ):
		if line.strip().startswith('all_model_classes = '      ):
			lowerCamelCase_							:		Any							       =							len(A      ) - len(line.lstrip()      )
			lowerCamelCase_							:		Optional[Any]							       =							idx
		elif line.strip().startswith('all_generative_model_classes = '      ):
			lowerCamelCase_							:		Any							       =							len(A      ) - len(line.lstrip()      )
			lowerCamelCase_							:		str							       =							idx
		elif line.strip().startswith('pipeline_model_mapping = '      ):
			lowerCamelCase_							:		Any							       =							len(A      ) - len(line.lstrip()      )
			lowerCamelCase_							:		Dict							       =							idx
			lowerCamelCase_							:		Tuple							       =							line
			break

	if start_idx is None:
		return "", -1
	# Find the ending index (inclusive) of the above found block.
	lowerCamelCase_							:		str							       =							find_block_ending(A						,       A						,       A      )

	# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
	# `is_timm_available()` instead of `is_torch_available()`.
	# Keep leading and trailing whitespaces
	lowerCamelCase_							:		Any							       =							re.compile(R'\s(is_\S+?_available\(\))\s'      )
	for line in class_lines[start_idx : end_idx + 1]:
		lowerCamelCase_							:		Optional[Any]							       =							r.search(A      )
		if backend_condition is not None:
			# replace the leading and trailing whitespaces to the space character " ".
			lowerCamelCase_							:		Optional[Any]							       =							' ' + backend_condition[0][1:-1] + ' '
			lowerCamelCase_							:		List[str]							       =							r.sub(A						,       A      )
			break

	if def_line is None:
		# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
		# `all_model_classes` or `all_generative_model_classes`.
		lowerCamelCase_							:		Optional[int]							       =							end_idx
	else:
		# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
		lowerCamelCase_							:		List[str]							       =							start_idx - 1
		# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
		for idx in range(A						,       end_idx + 1      ):
			# These lines are going to be removed before writing to the test file.
			lowerCamelCase_							:		Any							       =							None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
	lowerCamelCase_							:		int							       =							[x.__name__ for x in test_class.__bases__]
	if "PipelineTesterMixin" not in parent_classes:
		# Put `PipelineTesterMixin` just before `unittest.TestCase`
		lowerCamelCase_							:		int							       =							[x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
		if "TestCase" in parent_classes:
			# Here we **assume** the original string is always with `unittest.TestCase`.
			_parent_classes.append('unittest.TestCase'      )
		lowerCamelCase_							:		Dict							       =							', '.join(_parent_classes      )
		for idx, line in enumerate(A      ):
			# Find the ending of the declaration of `test_class`
			if line.strip().endswith('):'      ):
				# mark the lines of the declaration of `test_class` to be removed
				for _idx in range(idx + 1      ):
					lowerCamelCase_							:		int							       =							None  # noqa
				break
        # Add the new, one-line, class declaration for `test_class`
		lowerCamelCase_							:		List[str]							       =							f"class {test_class.__name__}({parent_classes}):\n"

	# Add indentation
	lowerCamelCase_							:		Optional[int]							       =							' ' * indent_level + line_to_add
	# Insert `pipeline_model_mapping` to `class_lines`.
	# (The line at `target_idx` should be kept by definition!)
	lowerCamelCase_							:		int							       =							class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
	# Remove the lines that are marked to be removed
	lowerCamelCase_							:		Any							       =							[x for x in class_lines if x is not None]

	# Move from test class to module (in order to write to the test file)
	lowerCamelCase_							:		Optional[Any]							       =							inspect.getsourcelines(inspect.getmodule(A      )      )[0]
	# Be careful with the 1-off between line numbers and array indices
	lowerCamelCase_							:		int							       =							module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
	lowerCamelCase_							:		Union[str, Any]							       =							''.join(A      )

	lowerCamelCase_							:		int							       =							inspect.getsourcefile(A      )
	with open(A						,       'w'						,       encoding='UTF-8'						,       newline='\n'      ) as fp:
		fp.write(A      )

	return line_to_add






def  snake_case (							A						,       A=False      )							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							find_test_class(A      )
	if test_class:
		add_pipeline_model_mapping(A						,       overwrite=A      )


if __name__ == "__main__":
							UpperCamelCase__		=						argparse.ArgumentParser()
							parser.add_argument(
							    '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
							)
							parser.add_argument(
							    '--all',
							    action='store_true',
							    help='If to check and modify all test files.',
							)
							parser.add_argument(
							    '--overwrite',
							    action='store_true',
							    help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
							)
							UpperCamelCase__		=						parser.parse_args()

							if not args.all and not args.test_file:
														raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
							elif args.all and args.test_file:
														raise ValueError('Only one of `--test_file` and `--all` could be specified.')

							UpperCamelCase__		=						[]
							if args.test_file:
														UpperCamelCase__		=						[args.test_file]
							else:
														UpperCamelCase__		=						os.path.join('tests', 'models', '**', 'test_modeling_*.py')
														for test_file in glob.glob(pattern):
																					# `Flax` is not concerned at this moment
																					if not test_file.startswith('test_modeling_flax_'):
																												test_files.append(test_file)

							for test_file in test_files:
														if test_file in TEST_FILE_TO_IGNORE:
																					print(F'[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.')
																					continue
														add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)






from __future__ import annotations

import unittest

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

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


if is_tf_available():
							import tensorflow as tf

							from transformers.models.mpnet.modeling_tf_mpnet import (
							    TFMPNetForMaskedLM,
							    TFMPNetForMultipleChoice,
							    TFMPNetForQuestionAnswering,
							    TFMPNetForSequenceClassification,
							    TFMPNetForTokenClassification,
							    TFMPNetModel,
							)


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=13			,	__lowercase=7			,	__lowercase=True			,	__lowercase=True			,	__lowercase=False			,	__lowercase=True			,	__lowercase=99			,	__lowercase=64			,	__lowercase=2			,	__lowercase=4			,	__lowercase=64			,	__lowercase="gelu"			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=512			,	__lowercase=16			,	__lowercase=2			,	__lowercase=0.02			,	__lowercase=3			,	__lowercase=4			,	__lowercase=None			,	):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size )

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

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

						lowerCamelCase_							:		Any							       =							MPNetConfig(
						    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			,	initializer_range=self.initializer_range			,	)
						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							TFMPNetModel(config=__lowercase )
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						lowerCamelCase_							:		Dict							       =							[input_ids, input_mask]
						lowerCamelCase_							:		Dict							       =							model(__lowercase )
						self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							TFMPNetForMaskedLM(config=__lowercase )
						lowerCamelCase_							:		List[Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		int							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							TFMPNetForQuestionAnswering(config=__lowercase )
						lowerCamelCase_							:		Tuple							       =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						lowerCamelCase_							:		Optional[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.start_logits.shape			,	(self.batch_size, self.seq_length) )
						self.parent.assertEqual(result.end_logits.shape			,	(self.batch_size, self.seq_length) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							self.num_labels
						lowerCamelCase_							:		Union[str, Any]							       =							TFMPNetForSequenceClassification(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		List[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.num_choices
						lowerCamelCase_							:		Tuple							       =							TFMPNetForMultipleChoice(__lowercase )
						lowerCamelCase_							:		Dict							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Any							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Optional[Any]							       =							{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						lowerCamelCase_							:		Union[str, Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_choices) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.num_labels
						lowerCamelCase_							:		str							       =							TFMPNetForTokenClassification(__lowercase )
						lowerCamelCase_							:		Any							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels) )





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.prepare_config_and_inputs()
						((lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_))							:		Tuple							       =							config_and_inputs
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			(
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCamelCase		  =			(
					    {
					        """feature-extraction""": TFMPNetModel,
					        """fill-mask""": TFMPNetForMaskedLM,
					        """question-answering""": TFMPNetForQuestionAnswering,
					        """text-classification""": TFMPNetForSequenceClassification,
					        """token-classification""": TFMPNetForTokenClassification,
					        """zero-shot""": TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							TFMPNetModelTester(self )
						lowerCamelCase_							:		str							       =							ConfigTester(self			,	config_class=__lowercase			,	hidden_size=37 )

					def      lowercase_							(    self ):







						'''simple docstring'''




						self.config_tester.run_common_tests()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_model(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase )





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						for model_name in ["microsoft/mpnet-base"]:
							lowerCamelCase_							:		str							       =							TFMPNetModel.from_pretrained(__lowercase )
							self.assertIsNotNone(__lowercase )


@require_tf
class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							TFMPNetModel.from_pretrained('microsoft/mpnet-base' )
						lowerCamelCase_							:		Optional[int]							       =							tf.constant([[0, 1, 2, 3, 4, 5]] )
						lowerCamelCase_							:		str							       =							model(__lowercase )[0]

						lowerCamelCase_							:		List[Any]							       =							[1, 6, 768]
						self.assertEqual(output.shape			,	__lowercase )

						lowerCamelCase_							:		Any							       =							tf.constant(
						    [
						        [
						            [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
						            [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
						            [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
						        ]
						    ] )
						tf.debugging.assert_near(output[:, :3, :3]			,	__lowercase			,	atol=1E-4 )
