




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
UpperCamelCase__         =      '''src/diffusers'''
UpperCamelCase__         =      '''.'''


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







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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					__lowerCAmelCase           =	object_name.split("."					)
					__lowerCAmelCase           =	0

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

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

					# Now let's find the class / func in the code!
					__lowerCAmelCase           =	""""""
					__lowerCAmelCase           =	0
					for name in parts[i + 1 :]:
										while (
										    line_index < len(SCREAMING_SNAKE_CASE_					) 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(SCREAMING_SNAKE_CASE_					):
										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).
					__lowerCAmelCase           =	line_index
					while line_index < len(SCREAMING_SNAKE_CASE_					) and _should_continue(lines[line_index]				,				SCREAMING_SNAKE_CASE_					):
										line_index += 1
					# Clean up empty lines at the end (if any).
					while len(lines[line_index - 1]					) <= 1:
										line_index -= 1

					__lowerCAmelCase           =	lines[start_index:line_index]
					return "".join(SCREAMING_SNAKE_CASE_					)


UpperCamelCase__         =      re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
UpperCamelCase__         =      re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
UpperCamelCase__         =      re.compile(R"""<FILL\s+[^>]*>""")







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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Tuple					):
					__lowerCAmelCase           =	len(get_indent(SCREAMING_SNAKE_CASE_					)					) > 0
					if has_indent:
										__lowerCAmelCase           =	F"""class Bla:\n{code}"""
					__lowerCAmelCase           =	black.Mode(target_versions={black.TargetVersion.PYaa}				,				line_length=1_19				,				preview=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	black.format_str(SCREAMING_SNAKE_CASE_				,				mode=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	style_docstrings_in_code(SCREAMING_SNAKE_CASE_					)
					return result[len("class Bla:\n"					) :] if has_indent else result







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Any=False					):



					with open(SCREAMING_SNAKE_CASE_				,				"r"				,				encoding="utf-8"				,				newline="\n"					) as f:
										__lowerCAmelCase           =	f.readlines()
					__lowerCAmelCase           =	[]
					__lowerCAmelCase           =	0
					# Not a for loop cause `lines` is going to change (if `overwrite=True`).
					while line_index < len(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	_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.
										__lowerCAmelCase           =	search.groups()
										__lowerCAmelCase           =	find_code_in_diffusers(SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	get_indent(SCREAMING_SNAKE_CASE_					)

										__lowerCAmelCase           =	line_index + 1 if indent == theoretical_indent else line_index + 2
										__lowerCAmelCase           =	theoretical_indent
										__lowerCAmelCase           =	start_index

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

										__lowerCAmelCase           =	lines[start_index:line_index]
										__lowerCAmelCase           =	"""""".join(SCREAMING_SNAKE_CASE_					)

										# Remove any nested `Copied from` comments to avoid circular copies
										__lowerCAmelCase           =	[line for line in theoretical_code.split("\n"					) if _re_copy_warning.search(SCREAMING_SNAKE_CASE_					) is None]
										__lowerCAmelCase           =	"""\n""".join(SCREAMING_SNAKE_CASE_					)

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

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

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







def      _a				(						SCREAMING_SNAKE_CASE_							:    bool = False					):
					__lowerCAmelCase           =	glob.glob(os.path.join(SCREAMING_SNAKE_CASE_				,				"**/*.py"					)				,				recursive=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	[]
					for filename in all_files:
										__lowerCAmelCase           =	is_copy_consistent(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
										diffs += [F"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
					if not overwrite and len(SCREAMING_SNAKE_CASE_					) > 0:
										__lowerCAmelCase           =	"""\n""".join(SCREAMING_SNAKE_CASE_					)
										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__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					UpperCamelCase__         =      parser.parse_args()

					check_copies(args.fix_and_overwrite)










from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor
class       a__   :






						def __init__(       self		,      _A		,      _A=1_3		,      _A=3_0		,      _A=2		,      _A=3		,      _A=True		,      _A=True		,      _A=3_2		,      _A=2		,      _A=4		,      _A=3_7		,      _A="gelu"		,      _A=0.1		,      _A=0.1		,      _A=1_0		,      _A=0.02		,      _A=3		,      _A=None		,      _A=2		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	parent
											__lowerCAmelCase           =	batch_size
											__lowerCAmelCase           =	image_size
											__lowerCAmelCase           =	patch_size
											__lowerCAmelCase           =	num_channels
											__lowerCAmelCase           =	is_training
											__lowerCAmelCase           =	use_labels
											__lowerCAmelCase           =	hidden_size
											__lowerCAmelCase           =	num_hidden_layers
											__lowerCAmelCase           =	num_attention_heads
											__lowerCAmelCase           =	intermediate_size
											__lowerCAmelCase           =	hidden_act
											__lowerCAmelCase           =	hidden_dropout_prob
											__lowerCAmelCase           =	attention_probs_dropout_prob
											__lowerCAmelCase           =	type_sequence_label_size
											__lowerCAmelCase           =	initializer_range
											__lowerCAmelCase           =	scope
											__lowerCAmelCase           =	encoder_stride

											# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
											__lowerCAmelCase           =	(image_size // patch_size) ** 2
											__lowerCAmelCase           =	num_patches + 2






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

											__lowerCAmelCase           =	None
											if self.use_labels:
																__lowerCAmelCase           =	ids_tensor([self.batch_size]		,      self.type_sequence_label_size      )

											__lowerCAmelCase           =	self.get_config()

											return config, pixel_values, labels






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return DeiTConfig(
											    image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      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		,      is_decoder=_A		,      initializer_range=self.initializer_range		,      encoder_stride=self.encoder_stride		,      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTModel(config=_A      )
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTForMaskedImageModeling(config=_A      )
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(
											    result.reconstruction.shape		,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

											# test greyscale images
											__lowerCAmelCase           =	1
											__lowerCAmelCase           =	TFDeiTForMaskedImageModeling(_A      )

											__lowerCAmelCase           =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(result.reconstruction.shape		,      (self.batch_size, 1, self.image_size, self.image_size)      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.type_sequence_label_size
											__lowerCAmelCase           =	TFDeiTForImageClassification(_A      )
											__lowerCAmelCase           =	model(_A		,      labels=_A      )
											self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)      )

											# test greyscale images
											__lowerCAmelCase           =	1
											__lowerCAmelCase           =	TFDeiTForImageClassification(_A      )

											__lowerCAmelCase           =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
											__lowerCAmelCase           =	model(_A		,      labels=_A      )
											self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.prepare_config_and_inputs()
											__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	config_and_inputs
											__lowerCAmelCase           =	{"pixel_values": pixel_values}
											return config, inputs_dict






@require_tf
class       a__   ( snake_case__			,    snake_case__			,    unittest.TestCase				):
						_a							:   Optional[Any] =   (
						    (
						        TFDeiTModel,
						        TFDeiTForImageClassification,
						        TFDeiTForImageClassificationWithTeacher,
						        TFDeiTForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:   Optional[Any] =   (
						    {
						        """feature-extraction""": TFDeiTModel,
						        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
						    }
						    if is_tf_available()
						    else {}
						)

						_a							:   str =   False
						_a							:   str =   False
						_a							:   List[str] =   False
						_a							:   Optional[int] =   False






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTModelTester(self      )
											__lowerCAmelCase           =	ConfigTester(self		,      config_class=_A		,      has_text_modality=_A		,      hidden_size=3_7      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="DeiT does not use inputs_embeds"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase           =	model_class(_A      )
																self.assertIsInstance(model.get_input_embeddings()		,      (tf.keras.layers.Layer)      )
																__lowerCAmelCase           =	model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(_A		,      tf.keras.layers.Dense      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase           =	model_class(_A      )
																__lowerCAmelCase           =	inspect.signature(model.call      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																__lowerCAmelCase           =	[*signature.parameters.keys()]

																__lowerCAmelCase           =	["pixel_values"]
																self.assertListEqual(arg_names[:1]		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=False      ):




											"""simple docstring"""



											__lowerCAmelCase           =	super()._prepare_for_class(_A		,      _A		,      return_labels=_A      )

											if return_labels:
																if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
																					del inputs_dict["labels"]

											return inputs_dict






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	TFDeiTModel.from_pretrained(_A      )
																self.assertIsNotNone(_A      )







def      _a				(						):
					__lowerCAmelCase           =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


@require_tf
@require_vision
class       a__   ( unittest.TestCase				):






						@cached_property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return (
											    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
											    if is_vision_available()
											    else None
											)






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

											__lowerCAmelCase           =	self.default_image_processor
											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="tf"      )

											# forward pass
											__lowerCAmelCase           =	model(**_A      )

											# verify the logits
											__lowerCAmelCase           =	tf.TensorShape((1, 1_0_0_0)      )
											self.assertEqual(outputs.logits.shape		,      _A      )

											__lowerCAmelCase           =	tf.constant([-1.02_66, 0.19_12, -1.28_61]      )

											self.assertTrue(np.allclose(outputs.logits[0, :3]		,      _A		,      atol=1E-4      )      )




