


import collections
import os
import re
from pathlib import Path


lowerCAmelCase__			:      List[str]								='''src/transformers'''


# Matches is_xxx_available()
lowerCAmelCase__			:      str								=re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__			:      int								=re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCAmelCase__			:      Optional[Any]								=re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
lowerCAmelCase__			:      Union[str, Any]								=re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
lowerCAmelCase__			:      Union[str, Any]								=re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCAmelCase__			:      int								=re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCAmelCase__			:      Any								=re.compile(R'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCAmelCase__			:      Optional[int]								=re.compile(R'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
lowerCAmelCase__			:      Dict								=re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
lowerCAmelCase__			:      Any								=re.compile(R'^\s*try:')
# Catches a line with else:
lowerCAmelCase__			:      Optional[Any]								=re.compile(R'^\s*else:')






def 						a__			(				A__				):






			if _re_test_backend.search(_lowercase				) is None:
						return None
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[b[0] for b in _re_backend.findall(_lowercase				)]
			backends.sort()
			return "_and_".join(_lowercase				)






def 						a__			(				A__				):






			with open(_lowercase,      'r',      encoding='utf-8',      newline='\n'				) as f:
						SCREAMING_SNAKE_CASE_		:     Any					=					f.readlines()

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					0
			while line_index < len(_lowercase				) and not lines[line_index].startswith('_import_structure = {'				):
						line_index += 1

			# If this is a traditional init, just return.
			if line_index >= len(_lowercase				):
						return None

			# First grab the objects without a specific backend in _import_structure
			SCREAMING_SNAKE_CASE_		:     str					=					[]
			while not lines[line_index].startswith('if TYPE_CHECKING'				) and find_backend(lines[line_index]				) is None:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					lines[line_index]
						# If we have everything on a single line, let's deal with it.
						if _re_one_line_import_struct.search(_lowercase				):
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					_re_one_line_import_struct.search(_lowercase				).groups()[0]
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					re.findall(r'\[([^\]]+)\]',      _lowercase				)
									for imp in imports:
												objects.extend([obj[1:-1] for obj in imp.split(', '				)]				)
									line_index += 1
									continue
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					_re_import_struct_key_value.search(_lowercase				)
						if single_line_import_search is not None:
									SCREAMING_SNAKE_CASE_		:     List[str]					=					[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '				) if len(_lowercase				) > 0]
									objects.extend(_lowercase				)
						elif line.startswith(' ' * 8 + '\"'				):
									objects.append(line[9:-3]				)
						line_index += 1

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					{"none": objects}
			# Let's continue with backend-specific objects in _import_structure
			while not lines[line_index].startswith('if TYPE_CHECKING'				):
						# If the line is an if not is_backend_available, we grab all objects associated.
						SCREAMING_SNAKE_CASE_		:     Dict					=					find_backend(lines[line_index]				)
						# Check if the backend declaration is inside a try block:
						if _re_try.search(lines[line_index - 1]				) is None:
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

						if backend is not None:
									line_index += 1

									# Scroll until we hit the else block of try-except-else
									while _re_else.search(lines[line_index]				) is None:
												line_index += 1

									line_index += 1

									SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]				) <= 1 or lines[line_index].startswith(' ' * 4				):
												SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					lines[line_index]
												if _re_import_struct_add_one.search(_lowercase				) is not None:
															objects.append(_re_import_struct_add_one.search(_lowercase				).groups()[0]				)
												elif _re_import_struct_add_many.search(_lowercase				) is not None:
															SCREAMING_SNAKE_CASE_		:     List[str]					=					_re_import_struct_add_many.search(_lowercase				).groups()[0].split(', '				)
															SCREAMING_SNAKE_CASE_		:     Tuple					=					[obj[1:-1] for obj in imports if len(_lowercase				) > 0]
															objects.extend(_lowercase				)
												elif _re_between_brackets.search(_lowercase				) is not None:
															SCREAMING_SNAKE_CASE_		:     List[str]					=					_re_between_brackets.search(_lowercase				).groups()[0].split(', '				)
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					[obj[1:-1] for obj in imports if len(_lowercase				) > 0]
															objects.extend(_lowercase				)
												elif _re_quote_object.search(_lowercase				) is not None:
															objects.append(_re_quote_object.search(_lowercase				).groups()[0]				)
												elif line.startswith(' ' * 8 + '\"'				):
															objects.append(line[9:-3]				)
												elif line.startswith(' ' * 1_2 + '\"'				):
															objects.append(line[1_3:-3]				)
												line_index += 1

									SCREAMING_SNAKE_CASE_		:     Any					=					objects
						else:
									line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[]
			while (
			    line_index < len(_lowercase				)
			    and find_backend(lines[line_index]				) is None
			    and not lines[line_index].startswith('else'				)
			):
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					lines[line_index]
						SCREAMING_SNAKE_CASE_		:     Dict					=					_re_import.search(_lowercase				)
						if single_line_import_search is not None:
									objects.extend(single_line_import_search.groups()[0].split(', '				)				)
						elif line.startswith(' ' * 8				):
									objects.append(line[8:-2]				)
						line_index += 1

			SCREAMING_SNAKE_CASE_		:     Any					=					{"none": objects}
			# Let's continue with backend-specific objects
			while line_index < len(_lowercase				):
						# If the line is an if is_backend_available, we grab all objects associated.
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					find_backend(lines[line_index]				)
						# Check if the backend declaration is inside a try block:
						if _re_try.search(lines[line_index - 1]				) is None:
									SCREAMING_SNAKE_CASE_		:     Tuple					=					None

						if backend is not None:
									line_index += 1

									# Scroll until we hit the else block of try-except-else
									while _re_else.search(lines[line_index]				) is None:
												line_index += 1

									line_index += 1

									SCREAMING_SNAKE_CASE_		:     List[str]					=					[]
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]				) <= 1 or lines[line_index].startswith(' ' * 8				):
												SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					lines[line_index]
												SCREAMING_SNAKE_CASE_		:     Any					=					_re_import.search(_lowercase				)
												if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(', '				)				)
												elif line.startswith(' ' * 1_2				):
															objects.append(line[1_2:-2]				)
												line_index += 1

									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					objects
						else:
									line_index += 1

			return import_dict_objects, type_hint_objects






def 						a__			(				A__,      A__				):






			def find_duplicates(A__				):
						return [k for k, v in collections.Counter(_lowercase				).items() if v > 1]

			if list(import_dict_objects.keys()				) != list(type_hint_objects.keys()				):
						return ["Both sides of the init do not have the same backends!"]

			SCREAMING_SNAKE_CASE_		:     Tuple					=					[]
			for key in import_dict_objects.keys():
						SCREAMING_SNAKE_CASE_		:     Tuple					=					find_duplicates(import_dict_objects[key]				)
						if duplicate_imports:
									errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''				)
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					find_duplicates(type_hint_objects[key]				)
						if duplicate_type_hints:
									errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''				)

						if sorted(set(import_dict_objects[key]				)				) != sorted(set(type_hint_objects[key]				)				):
									SCREAMING_SNAKE_CASE_		:     int					=					"base imports" if key == "none" else F'''{key} backend'''
									errors.append(F'''Differences for {name}:'''				)
									for a in type_hint_objects[key]:
												if a not in import_dict_objects[key]:
															errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''				)
									for a in import_dict_objects[key]:
												if a not in type_hint_objects[key]:
															errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''				)
			return errors






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     str					=					[]
			for root, _, files in os.walk(_lowercase				):
						if "__init__.py" in files:
									SCREAMING_SNAKE_CASE_		:     List[str]					=					os.path.join(_lowercase,      '__init__.py'				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					parse_init(_lowercase				)
									if objects is not None:
												SCREAMING_SNAKE_CASE_		:     Dict					=					analyze_results(*_lowercase				)
												if len(_lowercase				) > 0:
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
															failures.append('\n'.join(_lowercase				)				)
			if len(_lowercase				) > 0:
						raise ValueError('\n\n'.join(_lowercase				)				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     str					=					[]
			for path, directories, files in os.walk(_lowercase				):
						for folder in directories:
									# Ignore private modules
									if folder.startswith('_'				):
												directories.remove(_lowercase				)
												continue
									# Ignore leftovers from branches (empty folders apart from pycache)
									if len(list((Path(_lowercase				) / folder).glob('*.py'				)				)				) == 0:
												continue
									SCREAMING_SNAKE_CASE_		:     Dict					=					str((Path(_lowercase				) / folder).relative_to(_lowercase				)				)
									SCREAMING_SNAKE_CASE_		:     str					=					short_path.replace(os.path.sep,      '.'				)
									submodules.append(_lowercase				)
						for fname in files:
									if fname == "__init__.py":
												continue
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					str((Path(_lowercase				) / fname).relative_to(_lowercase				)				)
									SCREAMING_SNAKE_CASE_		:     Tuple					=					short_path.replace('.py',      ''				).replace(os.path.sep,      '.'				)
									if len(submodule.split('.'				)				) == 1:
												submodules.append(_lowercase				)
			return submodules


lowerCAmelCase__			:      Dict								=[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]






def 						a__			(				):
			from transformers.utils import direct_transformers_import

			SCREAMING_SNAKE_CASE_		:     List[str]					=					direct_transformers_import(_lowercase				)

			SCREAMING_SNAKE_CASE_		:     Any					=					set(transformers._import_structure.keys()				)
			# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
			# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
			# (potentiall re-) add them.
			with open(os.path.join(_lowercase,      '__init__.py'				),      'r'				) as f:
						SCREAMING_SNAKE_CASE_		:     Any					=					f.read()
			import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]',      _lowercase				)				)				)

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[
			    module
			    for module in get_transformers_submodules()
			    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
			]

			if len(_lowercase				) > 0:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					"\n".join(F'''- {module}''' for module in module_not_registered				)
						raise ValueError(
						    'The following submodules are not properly registed in the main init of Transformers:\n'
						    F'''{list_of_modules}\n'''
						    'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'				)


if __name__ == "__main__":
				check_all_inits()
				check_submodules()












"""simple docstring"""




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 			__UpperCamelCase							:






							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__=512 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=None ,   )      ->					List[str]:
								a :						List[str]      =							parent
								a :						Optional[int]      =							batch_size
								a :						Optional[int]      =							seq_length
								a :						Tuple      =							is_training
								a :						Optional[int]      =							use_input_mask
								a :						List[Any]      =							use_token_type_ids
								a :						Tuple      =							use_labels
								a :						Union[str, Any]      =							vocab_size
								a :						str      =							hidden_size
								a :						List[Any]      =							num_hidden_layers
								a :						Dict      =							num_attention_heads
								a :						int      =							intermediate_size
								a :						str      =							hidden_act
								a :						Optional[int]      =							hidden_dropout_prob
								a :						str      =							attention_probs_dropout_prob
								a :						str      =							max_position_embeddings
								a :						List[str]      =							type_vocab_size
								a :						List[str]      =							type_sequence_label_size
								a :						Union[str, Any]      =							initializer_range
								a :						int      =							num_labels
								a :						Optional[int]      =							num_choices
								a :						Union[str, Any]      =							scope






							def        __a  (  self )      ->					Optional[Any]:
								a :						str      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

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

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

								a :						str      =							self.get_config()

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






							def        __a  (  self )      ->					Optional[int]:
								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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						Union[str, Any]      =							EsmModel(config=lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()
								a :						Optional[int]      =							model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ )
								a :						List[str]      =							model(lowerCAmelCase__ )
								a :						Optional[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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Tuple:
								a :						int      =							EsmForMaskedLM(config=lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()
								a :						Union[str, Any]      =							model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ,   labels=lowerCAmelCase__ )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[Any]:
								a :						str      =							self.num_labels
								a :						int      =							EsmForTokenClassification(config=lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()
								a :						int      =							model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ,   labels=lowerCAmelCase__ )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels) )






							def        __a  (  self )      ->					Union[str, Any]:
								a :						str      =							self.prepare_config_and_inputs()
								(
								    (
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			
								) :						Optional[int]      =							config_and_inputs
								a :						int      =							{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			__UpperCamelCase							(		a__						,			a__						,			unittest.TestCase		):
							lowerCamelCase  :   Optional[Any]    =False

							lowerCamelCase  :   Tuple    =(
							    (
							        EsmForMaskedLM,
							        EsmModel,
							        EsmForSequenceClassification,
							        EsmForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase  :   Tuple    =()
							lowerCamelCase  :   Optional[int]    =(
							    {
							        """feature-extraction""": EsmModel,
							        """fill-mask""": EsmForMaskedLM,
							        """text-classification""": EsmForSequenceClassification,
							        """token-classification""": EsmForTokenClassification,
							        """zero-shot""": EsmForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase  :   Any    =True






							def        __a  (  self )      ->					Tuple:
								a :						str      =							EsmModelTester(self )
								a :						Union[str, Any]      =							ConfigTester(self ,   config_class=lowerCAmelCase__ ,   hidden_size=37 )






							def        __a  (  self )      ->					Dict:
								self.config_tester.run_common_tests()






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Tuple      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					List[str]:
								a :						Dict      =							self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									a :						List[str]      =							type
									self.model_tester.create_and_check_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[Any]:
								a :						Dict      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								a :						int      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )






							@slow
							def        __a  (  self )      ->					Optional[Any]:
								for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						Tuple      =							EsmModel.from_pretrained(lowerCAmelCase__ )
									self.assertIsNotNone(lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								a :						List[str]      =							self.model_tester.prepare_config_and_inputs()[0]
								a :						str      =							EsmEmbeddings(config=lowerCAmelCase__ )

								a :						Optional[Any]      =							torch.as_tensor([[12, 31, 13, model.padding_idx]] )
								a :						Tuple      =							torch.as_tensor(
								    [
								        [
								            0 + model.padding_idx + 1,
								            1 + model.padding_idx + 1,
								            2 + model.padding_idx + 1,
								            model.padding_idx,
								        ]
								    ] )
								a :						str      =							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        __a  (  self )      ->					List[Any]:
								a :						int      =							self.model_tester.prepare_config_and_inputs()[0]
								a :						Dict      =							EsmEmbeddings(config=lowerCAmelCase__ )

								a :						Any      =							torch.empty(2 ,   4 ,   30 )
								a :						str      =							[
								    0 + embeddings.padding_idx + 1,
								    1 + embeddings.padding_idx + 1,
								    2 + embeddings.padding_idx + 1,
								    3 + embeddings.padding_idx + 1,
								]
								a :						Optional[Any]      =							torch.as_tensor([expected_single_positions, expected_single_positions] )
								a :						str      =							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        __a  (  self )      ->					Any:
								pass






							@unittest.skip("Esm does not support embedding resizing" )
							def        __a  (  self )      ->					Dict:
								pass






							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
							def        __a  (  self )      ->					str:
								pass


@require_torch
class 			__UpperCamelCase							(		a__		):






							@slow
							def        __a  (  self )      ->					int:
								with torch.no_grad():
									a :						str      =							EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
									model.eval()
									a :						List[str]      =							torch.tensor([[0, 1, 2, 3, 4, 5]] )
									a :						Any      =							model(lowerCAmelCase__ )[0]

									a :						Union[str, Any]      =							33

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

									a :						Optional[int]      =							torch.tensor(
									    [[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
									self.assertTrue(torch.allclose(output[:, :3, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )






							@slow
							def        __a  (  self )      ->					str:
								with torch.no_grad():
									a :						Optional[int]      =							EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
									model.eval()

									a :						str      =							torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
									a :						Tuple      =							model(lowerCAmelCase__ )[0]
									# compare the actual values for a slice.
									a :						Union[str, Any]      =							torch.tensor(
									    [[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
									self.assertTrue(torch.allclose(output[:, :3, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )


