


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
			import torch

			from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class        a	:




							"""simple docstring"""


							def __init__(							self :						Optional[int]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[Any]=None					,			lowerCamelCase :						List[str]=None					,			lowerCamelCase :						List[str]=None					,			lowerCamelCase :						List[str]="resnet50"					,			lowerCamelCase :						Union[str, Any]=3					,			lowerCamelCase :						Union[str, Any]=32					,			lowerCamelCase :						Optional[int]=3					,			lowerCamelCase :						Any=True					,			lowerCamelCase :						Tuple=True					,			)			->					Any:
														__snake_case	:    Optional[Any]											=     parent
														__snake_case	:    Optional[int]											=     out_indices if out_indices is not None else [4]
														__snake_case	:    Tuple											=     stage_names
														__snake_case	:    str											=     out_features
														__snake_case	:    str											=     backbone
														__snake_case	:    List[str]											=     batch_size
														__snake_case	:    Dict											=     image_size
														__snake_case	:    int											=     num_channels
														__snake_case	:    Tuple											=     use_pretrained_backbone
														__snake_case	:    Optional[Any]											=     is_training

							def 					__snake_case      (							self :						Dict       )			->					Optional[int]:
														__snake_case	:    Any											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
														__snake_case	:    Union[str, Any]											=     self.get_config()

														return config, pixel_values

							def 					__snake_case      (							self :						int       )			->					Dict:
														return TimmBackboneConfig(
														    image_size=self.image_size					,			num_channels=self.num_channels					,			out_features=self.out_features					,			out_indices=self.out_indices					,			stage_names=self.stage_names					,			use_pretrained_backbone=self.use_pretrained_backbone					,			backbone=self.backbone					,			)

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						str       )			->					Any:
														__snake_case	:    Dict											=     TimmBackbone(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														with torch.no_grad():
																					__snake_case	:    Optional[Any]											=     model(lowerCamelCase       )
														self.parent.assertEqual(
														    result.feature_map[-1].shape					,			(self.batch_size, model.channels[-1], 14, 14)					,			)






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

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




							"""simple docstring"""


							__UpperCAmelCase					:							Dict  						=						(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase					:							Tuple  						=						{"feature-extraction": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase					:							str  						=						False
							__UpperCAmelCase					:							Dict  						=						False
							__UpperCAmelCase					:							Any  						=						False
							__UpperCAmelCase					:							Any  						=						False

							def 					__snake_case      (							self :						List[Any]       )			->					Optional[Any]:
														__snake_case	:    int											=     TimmBackboneModelTester(self       )
														__snake_case	:    int											=     ConfigTester(self					,			config_class=lowerCamelCase					,			has_text_modality=lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[Any]       )			->					Dict:
														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 :						Optional[Any]       )			->					Optional[Any]:
														__snake_case	:    Any											=     "resnet18"
														__snake_case	:    Optional[Any]											=     "microsoft/resnet-18"

														__snake_case	:    Tuple											=     AutoBackbone.from_pretrained(lowerCamelCase					,			use_timm_backbone=lowerCamelCase       )
														__snake_case	:    Dict											=     AutoBackbone.from_pretrained(lowerCamelCase       )

														self.assertEqual(len(timm_model.out_features       )					,			len(transformers_model.out_features       )       )
														self.assertEqual(len(timm_model.stage_names       )					,			len(transformers_model.stage_names       )       )
														self.assertEqual(timm_model.channels					,			transformers_model.channels       )
														# Out indices are set to the last layer by default. For timm models, we don't know
														# the number of layers in advance, so we set it to (-1,), whereas for transformers
														# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
														self.assertEqual(timm_model.out_indices					,			(-1,)       )
														self.assertEqual(transformers_model.out_indices					,			[len(timm_model.stage_names       ) - 1]       )

														__snake_case	:    int											=     AutoBackbone.from_pretrained(lowerCamelCase					,			use_timm_backbone=lowerCamelCase					,			out_indices=[1, 2, 3]       )
														__snake_case	:    Optional[Any]											=     AutoBackbone.from_pretrained(lowerCamelCase					,			out_indices=[1, 2, 3]       )

														self.assertEqual(timm_model.out_indices					,			transformers_model.out_indices       )
														self.assertEqual(len(timm_model.out_features       )					,			len(transformers_model.out_features       )       )
														self.assertEqual(timm_model.channels					,			transformers_model.channels       )

							@unittest.skip("TimmBackbone doesn't support feed forward chunking"       )
							def 					__snake_case      (							self :						Dict       )			->					Dict:
														pass

							@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"       )
							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														pass

							@unittest.skip("TimmBackbone initialization is managed on the timm side"       )
							def 					__snake_case      (							self :						List[Any]       )			->					Dict:
														pass

							@unittest.skip("TimmBackbone models doesn't have inputs_embeds"       )
							def 					__snake_case      (							self :						Optional[int]       )			->					int:
														pass

							@unittest.skip("TimmBackbone models doesn't have inputs_embeds"       )
							def 					__snake_case      (							self :						List[str]       )			->					List[str]:
														pass

							@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"       )
							def 					__snake_case      (							self :						List[str]       )			->					int:
														pass

							@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"       )
							def 					__snake_case      (							self :						Union[str, Any]       )			->					Union[str, Any]:
														pass

							@unittest.skip("model weights aren't tied in TimmBackbone."       )
							def 					__snake_case      (							self :						List[str]       )			->					Any:
														pass

							@unittest.skip("model weights aren't tied in TimmBackbone."       )
							def 					__snake_case      (							self :						Optional[Any]       )			->					Any:
														pass

							@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"       )
							def 					__snake_case      (							self :						Optional[int]       )			->					Union[str, Any]:
														pass

							@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"       )
							def 					__snake_case      (							self :						List[str]       )			->					List[str]:
														pass

							@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."       )
							def 					__snake_case      (							self :						Optional[Any]       )			->					str:
														pass

							@unittest.skip("TimmBackbone doesn't support output_attentions."       )
							def 					__snake_case      (							self :						Dict       )			->					Optional[int]:
														pass

							@unittest.skip("Safetensors is not supported by timm."       )
							def 					__snake_case      (							self :						Any       )			->					Optional[int]:
														pass

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

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

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

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

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

														# no need to test all models as different heads yield the same functionality
														__snake_case	:    Optional[Any]											=     self.all_model_classes[0]
														__snake_case	:    Union[str, Any]											=     model_class(lowerCamelCase       )
														model.to(lowerCamelCase       )

														__snake_case	:    Tuple											=     self._prepare_for_class(lowerCamelCase					,			lowerCamelCase       )
														__snake_case	:    int											=     model(**lowerCamelCase       )
														__snake_case	:    List[str]											=     outputs[0][-1]

														# Encoder-/Decoder-only models
														__snake_case	:    Optional[int]											=     outputs.hidden_states[0]
														hidden_states.retain_grad()

														if self.has_attentions:
																					__snake_case	:    Optional[int]											=     outputs.attentions[0]
																					attentions.retain_grad()

														output.flatten()[0].backward(retain_graph=lowerCamelCase       )

														self.assertIsNotNone(hidden_states.grad       )

														if self.has_attentions:
																					self.assertIsNotNone(attentions.grad       )






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

														for model_class in self.all_model_classes:
																					__snake_case	:    Optional[Any]											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.eval()
																					__snake_case	:    List[Any]											=     model(**lowerCamelCase       )

																					self.assertEqual(len(result.feature_maps       )					,			len(config.out_indices       )       )
																					self.assertEqual(len(model.channels       )					,			len(config.out_indices       )       )

																					# Check output of last stage is taken if out_features=None, out_indices=None
																					__snake_case	:    Optional[int]											=     copy.deepcopy(lowerCamelCase       )
																					__snake_case	:    Dict											=     None
																					__snake_case	:    str											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.eval()
																					__snake_case	:    Tuple											=     model(**lowerCamelCase       )

																					self.assertEqual(len(result.feature_maps       )					,			1       )
																					self.assertEqual(len(model.channels       )					,			1       )

																					# Check backbone can be initialized with fresh weights
																					__snake_case	:    int											=     copy.deepcopy(lowerCamelCase       )
																					__snake_case	:    int											=     False
																					__snake_case	:    Any											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.eval()
																					__snake_case	:    Dict											=     model(**lowerCamelCase       )





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				,    __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				,    __snake_case				,    __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				,    __snake_case				,    __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)

