

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											=     'src/diffusers'
A											=     '.'


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





def a(lowercase__			, lowercase__	):





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





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= object_name.split('.'	)
	snake_case_      		= 0

	# First let's find the module where our object lives.
	snake_case_      		= parts[i]
	while i < len(lowercase__	) and not os.path.isfile(os.path.join(lowercase__			, f"""{module}.py"""	)	):
		i += 1
		if i < len(lowercase__	):
			snake_case_      		= 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:
		snake_case_      		= f.readlines()

	# Now let's find the class / func in the code!
	snake_case_      		= ''
	snake_case_      		= 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).
	snake_case_      		= 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

	snake_case_      		= lines[start_index:line_index]
	return "".join(lowercase__	)


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





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= code.split('\n'	)
	snake_case_      		= 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 a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= len(get_indent(lowercase__	)	) > 0
	if has_indent:
		snake_case_      		= f"""class Bla:\n{code}"""
	snake_case_      		= black.Mode(target_versions={black.TargetVersion.PYaa}			, line_length=119			, preview=lowercase__	)
	snake_case_      		= black.format_str(lowercase__			, mode=lowercase__	)
	snake_case_		,       snake_case_      		= style_docstrings_in_code(lowercase__	)
	return result[len('class Bla:\n'	) :] if has_indent else result





def a(lowercase__			, lowercase__=False	):





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

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

		# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
		snake_case_      		= True
		while line_index < len(lowercase__	) and should_continue:
			line_index += 1
			if line_index >= len(lowercase__	):
				break
			snake_case_      		= lines[line_index]
			snake_case_      		= _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

		snake_case_      		= lines[start_index:line_index]
		snake_case_      		= ''.join(lowercase__	)

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

		# Before comparing, use the `replace_pattern` on the original code.
		if len(lowercase__	) > 0:
			snake_case_      		= replace_pattern.replace('with'			, ''	).split(','	)
			snake_case_      		= [_re_replace_pattern.search(lowercase__	) for p in patterns]
			for pattern in patterns:
				if pattern is None:
					continue
				snake_case_		,       snake_case_		,       snake_case_      		= pattern.groups()
				snake_case_      		= re.sub(lowercase__			, lowercase__			, lowercase__	)
				if option.strip() == "all-casing":
					snake_case_      		= re.sub(obja.lower()			, obja.lower()			, lowercase__	)
					snake_case_      		= 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
			snake_case_      		= blackify(lines[start_index - 1] + theoretical_code	)
			snake_case_      		= 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:
				snake_case_      		= lines[:start_index] + [theoretical_code] + lines[line_index:]
				snake_case_      		= 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 a(lowercase__ = False	):





	'''simple docstring'''
	snake_case_      		= glob.glob(os.path.join(lowercase__			, '**/*.py'	)			, recursive=lowercase__	)
	snake_case_      		= []
	for filename in all_files:
		snake_case_      		= 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:
		snake_case_      		= '\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											=     argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		A											=     parser.parse_args()

		check_copies(args.fix_and_overwrite)



import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
		from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.config_class(**self.inputs_dict							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'hidden_sizes'							)							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'neck_hidden_sizes'							)							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'num_attention_heads'							)							)







class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=32 ,				__UpperCamelCase=2 ,				__UpperCamelCase=3 ,				__UpperCamelCase=6_40 ,				__UpperCamelCase=4 ,				__UpperCamelCase="silu" ,				__UpperCamelCase=3 ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=10 ,				__UpperCamelCase=None ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= patch_size
				snake_case_      		= num_channels
				snake_case_      		= last_hidden_size
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_act
				snake_case_      		= conv_kernel_size
				snake_case_      		= output_stride
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= classifier_dropout_prob
				snake_case_      		= use_labels
				snake_case_      		= is_training
				snake_case_      		= num_labels
				snake_case_      		= initializer_range
				snake_case_      		= scope


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_      		= None
				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.num_labels							)
					snake_case_      		= ids_tensor([self.batch_size, self.image_size, self.image_size] ,				self.num_labels							)

				snake_case_      		= self.get_config()

				return config, pixel_values, labels, pixel_labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return MobileViTConfig(
				    image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				num_attention_heads=self.num_attention_heads ,				hidden_act=self.hidden_act ,				conv_kernel_size=self.conv_kernel_size ,				output_stride=self.output_stride ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				classifier_dropout_prob=self.classifier_dropout_prob ,				initializer_range=self.initializer_range ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MobileViTModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,				(
				        self.batch_size,
				        self.last_hidden_size,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    ) ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MobileViTForImageClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MobileViTForSemanticSegmentation(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.logits.shape ,				(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    ) ,				)
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(
				    result.logits.shape ,				(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    ) ,				)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				snake_case_		,       snake_case_		,       snake_case_		,       snake_case_      		= config_and_inputs
				snake_case_      		= {'pixel_values': pixel_values}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (
			    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__A						  =  (
			    {
			        """feature-extraction""": MobileViTModel,
			        """image-classification""": MobileViTForImageClassification,
			        """image-segmentation""": MobileViTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTModelTester(self							)
				snake_case_      		= MobileViTConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			@unittest.skip(reason='MobileViT does not use inputs_embeds'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='MobileViT does not support input and output embeddings'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='MobileViT does not output attentions'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				def check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()

					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

					snake_case_      		= outputs.hidden_states

					snake_case_      		= 5
					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

					# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
					# with the width and height being successively divided by 2.
					snake_case_      		= 2
					for i in range(len(__UpperCamelCase							)							):
						self.assertListEqual(
						    list(hidden_states[i].shape[-2:]							) ,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,				)
						divisor *= 2

					self.assertEqual(self.model_tester.output_stride ,				divisor // 2							)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= True
					check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					snake_case_      		= True

					check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= MobileViTModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







def a():





	'''simple docstring'''
	snake_case_      		= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'							) if is_vision_available() else None


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'							).to(__UpperCamelCase							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= torch.Size((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor([-1.9364, -1.2327, -0.4653]							).to(__UpperCamelCase							)

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


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)
				snake_case_      		= model.to(__UpperCamelCase							)

				snake_case_      		= MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)

				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)
				snake_case_      		= outputs.logits

				# verify the logits
				snake_case_      		= torch.Size((1, 21, 32, 32)							)
				self.assertEqual(logits.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [
				        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
				        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
				        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
				    ] ,				device=__UpperCamelCase ,				)

				self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)
				snake_case_      		= model.to(__UpperCamelCase							)

				snake_case_      		= MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)

				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				snake_case_      		= outputs.logits.detach().cpu()

				snake_case_      		= image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase ,				target_sizes=[(50, 60)]							)
				snake_case_      		= torch.Size((50, 60)							)
				self.assertEqual(segmentation[0].shape ,				__UpperCamelCase							)

				snake_case_      		= image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase							)
				snake_case_      		= torch.Size((32, 32)							)
				self.assertEqual(segmentation[0].shape ,				__UpperCamelCase							)
