


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
_snake_case  :	List[Any]			      =       "src/diffusers"
_snake_case  :	str			      =       "."


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







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







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    str											=     object_name.split("."			)
							__snake_case	:    Any											=     0

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

							with open(os.path.join(__lowerCamelCase							,			F'{module}.py'			)							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    Optional[Any]											=     f.readlines()

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

							__snake_case	:    Dict											=     lines[start_index:line_index]
							return "".join(__lowerCamelCase			)


_snake_case  :	Any			      =       re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
_snake_case  :	Union[str, Any]			      =       re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
_snake_case  :	Optional[int]			      =       re.compile(R"<FILL\s+[^>]*>")







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Dict											=     code.split("\n"			)
							__snake_case	:    List[str]											=     0
							while idx < len(__lowerCamelCase			) and len(lines[idx]			) == 0:
														idx += 1
							if idx < len(__lowerCamelCase			):
														return re.search(R"^(\s*)\S"							,			lines[idx]			).groups()[0]
							return ""







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    List[str]											=     len(get_indent(__lowerCamelCase			)			) > 0
							if has_indent:
														__snake_case	:    str											=     F'class Bla:\n{code}'
							__snake_case	:    List[Any]											=     black.Mode(target_versions={black.TargetVersion.PYaa}							,			line_length=1_1_9							,			preview=__lowerCamelCase			)
							__snake_case	:    int											=     black.format_str(__lowerCamelCase							,			mode=__lowerCamelCase			)
							__snake_case	:    List[str]											=     style_docstrings_in_code(__lowerCamelCase			)
							return result[len("class Bla:\n"			) :] if has_indent else result







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							with open(__lowerCamelCase							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    List[Any]											=     f.readlines()
							__snake_case	:    int											=     []
							__snake_case	:    Optional[Any]											=     0
							# Not a for loop cause `lines` is going to change (if `overwrite=True`).
							while line_index < len(__lowerCamelCase			):
														__snake_case	:    List[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.
														__snake_case	:    List[str]											=     search.groups()
														__snake_case	:    List[str]											=     find_code_in_diffusers(__lowerCamelCase			)
														__snake_case	:    str											=     get_indent(__lowerCamelCase			)

														__snake_case	:    Optional[Any]											=     line_index + 1 if indent == theoretical_indent else line_index + 2
														__snake_case	:    Tuple											=     theoretical_indent
														__snake_case	:    str											=     start_index

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

														__snake_case	:    int											=     lines[start_index:line_index]
														__snake_case	:    int											=     "".join(__lowerCamelCase			)

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

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

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

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







def        lowerCAmelCase_  (      __lowerCamelCase = False			):
							__snake_case	:    Optional[Any]											=     glob.glob(os.path.join(__lowerCamelCase							,			"**/*.py"			)							,			recursive=__lowerCamelCase			)
							__snake_case	:    Dict											=     []
							for filename in all_files:
														__snake_case	:    Union[str, Any]											=     is_copy_consistent(__lowerCamelCase							,			__lowerCamelCase			)
														diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
							if not overwrite and len(__lowerCamelCase			) > 0:
														__snake_case	:    Optional[Any]											=     "\n".join(__lowerCamelCase			)
														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__":
			_snake_case  :	Optional[int]			      =       argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_snake_case  :	Union[str, Any]			      =       parser.parse_args()

			check_copies(args.fix_and_overwrite)










import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 CvtForImageClassification, CvtModel
			from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    Any											=     self.config_class(**self.inputs_dict       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"embed_dim"       )       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"num_heads"       )       )

class        a	:




							"""simple docstring"""


							def __init__(							self :						str					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						Any=13					,			lowerCamelCase :						Any=64					,			lowerCamelCase :						int=3					,			lowerCamelCase :						Tuple=[16, 48, 96]					,			lowerCamelCase :						Optional[int]=[1, 3, 6]					,			lowerCamelCase :						List[str]=[1, 2, 10]					,			lowerCamelCase :						Any=[7, 3, 3]					,			lowerCamelCase :						Any=[4, 2, 2]					,			lowerCamelCase :						Optional[Any]=[2, 1, 1]					,			lowerCamelCase :						str=[2, 2, 2]					,			lowerCamelCase :						Dict=[False, False, True]					,			lowerCamelCase :						Dict=[0.0, 0.0, 0.0]					,			lowerCamelCase :						Optional[Any]=0.02					,			lowerCamelCase :						Tuple=1E-12					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						List[str]=2					,			)			->					Optional[int]:
														__snake_case	:    Tuple											=     parent
														__snake_case	:    List[str]											=     batch_size
														__snake_case	:    Optional[int]											=     image_size
														__snake_case	:    Optional[int]											=     patch_sizes
														__snake_case	:    Union[str, Any]											=     patch_stride
														__snake_case	:    int											=     patch_padding
														__snake_case	:    Optional[Any]											=     is_training
														__snake_case	:    Optional[Any]											=     use_labels
														__snake_case	:    Tuple											=     num_labels
														__snake_case	:    Union[str, Any]											=     num_channels
														__snake_case	:    Tuple											=     embed_dim
														__snake_case	:    List[str]											=     num_heads
														__snake_case	:    Dict											=     stride_kv
														__snake_case	:    Optional[int]											=     depth
														__snake_case	:    List[Any]											=     cls_token
														__snake_case	:    List[Any]											=     attention_drop_rate
														__snake_case	:    Any											=     initializer_range
														__snake_case	:    str											=     layer_norm_eps

							def 					__snake_case      (							self :						List[str]       )			->					str:
														__snake_case	:    str											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

														__snake_case	:    Tuple											=     None
														if self.use_labels:
																					__snake_case	:    Tuple											=     ids_tensor([self.batch_size]					,			self.num_labels       )

														__snake_case	:    Optional[Any]											=     self.get_config()
														return config, pixel_values, labels

							def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
														return CvtConfig(
														    image_size=self.image_size					,			num_labels=self.num_labels					,			num_channels=self.num_channels					,			embed_dim=self.embed_dim					,			num_heads=self.num_heads					,			patch_sizes=self.patch_sizes					,			patch_padding=self.patch_padding					,			patch_stride=self.patch_stride					,			stride_kv=self.stride_kv					,			depth=self.depth					,			cls_token=self.cls_token					,			attention_drop_rate=self.attention_drop_rate					,			initializer_range=self.initializer_range					,			)

							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						str       )			->					Tuple:
														__snake_case	:    int											=     CvtModel(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[str]											=     model(lowerCamelCase       )
														__snake_case	:    List[Any]											=     (self.image_size, self.image_size)
														__snake_case				,    __snake_case	:    List[str]											=     image_size[0], image_size[1]
														for i in range(len(self.depth       )       ):
																					__snake_case	:    int											=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
																					__snake_case	:    Union[str, Any]											=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
														self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.embed_dim[-1], height, width)       )

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						int       )			->					Optional[Any]:
														__snake_case	:    str											=     self.num_labels
														__snake_case	:    Any											=     CvtForImageClassification(lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[Any]											=     model(lowerCamelCase					,			labels=lowerCamelCase       )
														self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)       )






							def 					__snake_case      (							self :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.prepare_config_and_inputs()
														__snake_case				,    __snake_case				,    __snake_case	:    str											=     config_and_inputs
														__snake_case	:    List[str]											=     {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[str]  						=						(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							__UpperCAmelCase					:							List[str]  						=						(
							    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase					:							List[str]  						=						False
							__UpperCAmelCase					:							Tuple  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False
							__UpperCAmelCase					:							Optional[int]  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False

							def 					__snake_case      (							self :						str       )			->					List[str]:
														__snake_case	:    Tuple											=     CvtModelTester(self       )
														__snake_case	:    Tuple											=     ConfigTester(self					,			config_class=lowerCamelCase					,			has_text_modality=lowerCamelCase					,			hidden_size=37       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Union[str, Any]:
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()

							def 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
														return

							@unittest.skip(reason="Cvt does not output attentions"       )
							def 					__snake_case      (							self :						Optional[int]       )			->					List[str]:
														pass

							@unittest.skip(reason="Cvt does not use inputs_embeds"       )
							def 					__snake_case      (							self :						List[str]       )			->					Any:
														pass

							@unittest.skip(reason="Cvt does not support input and output embeddings"       )
							def 					__snake_case      (							self :						int       )			->					List[Any]:
														pass

							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case				,    __snake_case	:    Optional[int]											=     self.model_tester.prepare_config_and_inputs_for_common()

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

																					__snake_case	:    Dict											=     ["pixel_values"]
																					self.assertListEqual(arg_names[:1]					,			lowerCamelCase       )

							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase       )

							def 					__snake_case      (							self :						int       )			->					Optional[Any]:
														def check_hidden_states_output(lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[Any]       ):
																					__snake_case	:    Any											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.eval()

																					with torch.no_grad():
																												__snake_case	:    List[str]											=     model(**self._prepare_for_class(lowerCamelCase					,			lowerCamelCase       )       )

																					__snake_case	:    List[Any]											=     outputs.hidden_states

																					__snake_case	:    Optional[int]											=     len(self.model_tester.depth       )
																					self.assertEqual(len(lowerCamelCase       )					,			lowerCamelCase       )

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]       )					,			[
																					        self.model_tester.embed_dim[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]					,			)

														__snake_case				,    __snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    Union[str, Any]											=     True
																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					__snake_case	:    List[str]											=     True

																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[int]       )			->					Optional[Any]:
														__snake_case	:    Any											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase       )

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






							@slow
							def 					__snake_case      (							self :						Tuple       )			->					List[str]:
														for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case	:    int											=     CvtModel.from_pretrained(lowerCamelCase       )
																					self.assertIsNotNone(lowerCamelCase       )







def        lowerCAmelCase_  (      ):
							__snake_case	:    Any											=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image

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




							"""simple docstring"""


							@cached_property
							def 					__snake_case      (							self :						Optional[Any]       )			->					Union[str, Any]:
														return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )






							@slow
							def 					__snake_case      (							self :						int       )			->					Tuple:
														__snake_case	:    Optional[Any]											=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(lowerCamelCase       )

														__snake_case	:    Optional[int]											=     self.default_image_processor
														__snake_case	:    Union[str, Any]											=     prepare_img()
														__snake_case	:    Dict											=     image_processor(images=lowerCamelCase					,			return_tensors="pt"       ).to(lowerCamelCase       )

														# forward pass
														with torch.no_grad():
																					__snake_case	:    Any											=     model(**lowerCamelCase       )

														# verify the logits
														__snake_case	:    Any											=     torch.Size((1, 1000)       )
														self.assertEqual(outputs.logits.shape					,			lowerCamelCase       )

														__snake_case	:    Optional[Any]											=     torch.tensor([0.92_85, 0.90_15, -0.31_50]       ).to(lowerCamelCase       )

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

