





"""simple docstring"""




import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a		:					Optional[Any]	      =     '''src/diffusers'''
a		:					Optional[Any]	      =     '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
a		:					Dict	      =     importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a		:					str	      =     spec.loader.load_module()






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[Any]       ,			_lowercase		:       Any   )					->Optional[int]:






	'''simple docstring'''






	return line.startswith(_lowercase   ) or len(_lowercase   ) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$"       ,			_lowercase   ) is not None






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							object_name.split("."   )
	a :						str      =							0

	# First let's find the module where our object lives.
	a :						List[Any]      =							parts[i]
	while i < len(_lowercase   ) and not os.path.isfile(os.path.join(_lowercase       ,			F"""{module}.py"""   )   ):
		i += 1
		if i < len(_lowercase   ):
			a :						List[Any]      =							os.path.join(_lowercase       ,			parts[i]   )
	if i >= len(_lowercase   ):
		raise ValueError(F"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""   )

	with open(os.path.join(_lowercase       ,			F"""{module}.py"""   )       ,			"r"       ,			encoding="utf-8"       ,			newline="\n"   ) as f:
		a :						Dict      =							f.readlines()

	# Now let's find the class / func in the code!
	a :						Any      =							""
	a :						Tuple      =							0
	for name in parts[i + 1 :]:
		while (
		    line_index < len(_lowercase   ) and re.search(RF"""^{indent}(class|def)\s+{name}(\(|\:)"""       ,			lines[line_index]   ) is None
		):
			line_index += 1
		indent += "    "
		line_index += 1

	if line_index >= len(_lowercase   ):
		raise ValueError(F""" {object_name} does not match any function or class in {module}."""   )

	# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
	a :						List[Any]      =							line_index
	while line_index < len(_lowercase   ) and _should_continue(lines[line_index]       ,			_lowercase   ):
		line_index += 1
	# Clean up empty lines at the end (if any).
	while len(lines[line_index - 1]   ) <= 1:
		line_index -= 1

	a :						str      =							lines[start_index:line_index]
	return "".join(_lowercase   )


a		:					str	      =     re.compile(R'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
a		:					Optional[Any]	      =     re.compile(R'''^\s*(\S+)->(\S+)(\s+.*|$)''')
a		:					List[Any]	      =     re.compile(R'''<FILL\s+[^>]*>''')






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]   )					->Dict:






	'''simple docstring'''






	a :						Optional[int]      =							code.split("\n"   )
	a :						Optional[int]      =							0
	while idx < len(_lowercase   ) and len(lines[idx]   ) == 0:
		idx += 1
	if idx < len(_lowercase   ):
		return re.search(R"^(\s*)\S"       ,			lines[idx]   ).groups()[0]
	return ""






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]   )					->str:






	'''simple docstring'''






	a :						List[Any]      =							len(get_indent(_lowercase   )   ) > 0
	if has_indent:
		a :						Optional[int]      =							F"""class Bla:\n{code}"""
	a :						List[Any]      =							black.Mode(target_versions={black.TargetVersion.PYaa}       ,			line_length=119       ,			preview=_lowercase   )
	a :						int      =							black.format_str(_lowercase       ,			mode=_lowercase   )
	a,			a :						Tuple      =							style_docstrings_in_code(_lowercase   )
	return result[len("class Bla:\n"   ) :] if has_indent else result






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]       ,			_lowercase		:       List[Any]=False   )					->Union[str, Any]:






	'''simple docstring'''






	with open(_lowercase       ,			"r"       ,			encoding="utf-8"       ,			newline="\n"   ) as f:
		a :						int      =							f.readlines()
	a :						Optional[int]      =							[]
	a :						Optional[int]      =							0
	# Not a for loop cause `lines` is going to change (if `overwrite=True`).
	while line_index < len(_lowercase   ):
		a :						Union[str, Any]      =							_re_copy_warning.search(lines[line_index]   )
		if search is None:
			line_index += 1
			continue

		# There is some copied code here, let's retrieve the original.
		a,			a,			a :						Dict      =							search.groups()
		a :						Optional[int]      =							find_code_in_diffusers(_lowercase   )
		a :						List[str]      =							get_indent(_lowercase   )

		a :						Any      =							line_index + 1 if indent == theoretical_indent else line_index + 2
		a :						Union[str, Any]      =							theoretical_indent
		a :						Optional[Any]      =							start_index

		# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
		a :						List[Any]      =							True
		while line_index < len(_lowercase   ) and should_continue:
			line_index += 1
			if line_index >= len(_lowercase   ):
				break
			a :						Optional[int]      =							lines[line_index]
			a :						Tuple      =							_should_continue(_lowercase       ,			_lowercase   ) and re.search(F"""^{indent}# End copy"""       ,			_lowercase   ) is None
		# Clean up empty lines at the end (if any).
		while len(lines[line_index - 1]   ) <= 1:
			line_index -= 1

		a :						Optional[int]      =							lines[start_index:line_index]
		a :						Tuple      =							"".join(_lowercase   )

		# Remove any nested `Copied from` comments to avoid circular copies
		a :						Union[str, Any]      =							[line for line in theoretical_code.split("\n"   ) if _re_copy_warning.search(_lowercase   ) is None]
		a :						List[Any]      =							"\n".join(_lowercase   )

		# Before comparing, use the `replace_pattern` on the original code.
		if len(_lowercase   ) > 0:
			a :						Optional[int]      =							replace_pattern.replace("with"       ,			""   ).split(","   )
			a :						Optional[int]      =							[_re_replace_pattern.search(_lowercase   ) for p in patterns]
			for pattern in patterns:
				if pattern is None:
					continue
				a,			a,			a :						List[str]      =							pattern.groups()
				a :						Optional[int]      =							re.sub(_lowercase       ,			_lowercase       ,			_lowercase   )
				if option.strip() == "all-casing":
					a :						int      =							re.sub(obja.lower()       ,			obja.lower()       ,			_lowercase   )
					a :						int      =							re.sub(obja.upper()       ,			obja.upper()       ,			_lowercase   )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
			a :						List[str]      =							blackify(lines[start_index - 1] + theoretical_code   )
			a :						str      =							theoretical_code[len(lines[start_index - 1]   ) :]

		# Test for a diff and act accordingly.
		if observed_code != theoretical_code:
			diffs.append([object_name, start_index]   )
			if overwrite:
				a :						List[Any]      =							lines[:start_index] + [theoretical_code] + lines[line_index:]
				a :						Tuple      =							start_index + 1

	if overwrite and len(_lowercase   ) > 0:
		# Warn the user a file has been modified.
		print(F"""Detected changes, rewriting {filename}."""   )
		with open(_lowercase       ,			"w"       ,			encoding="utf-8"       ,			newline="\n"   ) as f:
			f.writelines(_lowercase   )
	return diffs






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       bool = False   )					->int:






	'''simple docstring'''






	a :						Optional[int]      =							glob.glob(os.path.join(_lowercase       ,			"**/*.py"   )       ,			recursive=_lowercase   )
	a :						Any      =							[]
	for filename in all_files:
		a :						List[Any]      =							is_copy_consistent(_lowercase       ,			_lowercase   )
		diffs += [F"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
	if not overwrite and len(_lowercase   ) > 0:
		a :						List[str]      =							"\n".join(_lowercase   )
		raise Exception(
		    "Found the following copy inconsistencies:\n"
		    + diff
		    + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."   )


if __name__ == "__main__":
		a		:					List[Any]	      =     argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		a		:					Optional[Any]	      =     parser.parse_args()

		check_copies(args.fix_and_overwrite)









"""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 ) )


