



import argparse
import os
import re


__lowerCamelCase     :							Union[str, Any]  							=				"src/diffusers"

# Pattern that looks at the indentation in a line.
__lowerCamelCase     :							Any  							=				re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCamelCase     :							Union[str, Any]  							=				re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCamelCase     :							Optional[int]  							=				re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCamelCase     :							Any  							=				re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCamelCase     :							Tuple  							=				re.compile(r"\[([^\]]+)\]")

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[Any]:
						UpperCAmelCase         =     _re_indent.search(lowerCamelCase_    )
						return "" if search is None else search.groups()[0]

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_=""    ,   lowerCamelCase_=None    ,   lowerCamelCase_=None    ) ->       Union[str, Any]:
						UpperCAmelCase         =     0
						UpperCAmelCase         =     code.split("\n"    )
						if start_prompt is not None:
												while not lines[index].startswith(lowerCamelCase_    ):
																		index += 1
												UpperCAmelCase         =     ["\n".join(lines[:index]    )]
						else:
												UpperCAmelCase         =     []

						# We split into blocks until we get to the `end_prompt` (or the end of the block).
						UpperCAmelCase         =     [lines[index]]
						index += 1
						while index < len(lowerCamelCase_    ) and (end_prompt is None or not lines[index].startswith(lowerCamelCase_    )):
												if len(lines[index]    ) > 0 and get_indent(lines[index]    ) == indent_level:
																		if len(lowerCamelCase_    ) > 0 and get_indent(current_block[-1]    ).startswith(indent_level + " "    ):
																								current_block.append(lines[index]    )
																								blocks.append("\n".join(lowerCamelCase_    )    )
																								if index < len(lowerCamelCase_    ) - 1:
																														UpperCAmelCase         =     [lines[index + 1]]
																														index += 1
																								else:
																														UpperCAmelCase         =     []
																		else:
																								blocks.append("\n".join(lowerCamelCase_    )    )
																								UpperCAmelCase         =     [lines[index]]
												else:
																		current_block.append(lines[index]    )
												index += 1

						# Adds current block if it's nonempty.
						if len(lowerCamelCase_    ) > 0:
												blocks.append("\n".join(lowerCamelCase_    )    )

						# Add final block after end_prompt if provided.
						if end_prompt is not None and index < len(lowerCamelCase_    ):
												blocks.append("\n".join(lines[index:]    )    )

						return blocks

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[Any]:

						def _inner(lowerCamelCase_    ):
												return key(lowerCamelCase_    ).lower().replace("_"    ,   ""    )

						return _inner

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_=None    ) ->       Optional[int]:

						# If no key is provided, we use a noop.
						def noop(lowerCamelCase_    ):
												return x

						if key is None:
												UpperCAmelCase         =     noop
						# Constants are all uppercase, they go first.
						UpperCAmelCase         =     [obj for obj in objects if key(lowerCamelCase_    ).isupper()]
						# Classes are not all uppercase but start with a capital, they go second.
						UpperCAmelCase         =     [obj for obj in objects if key(lowerCamelCase_    )[0].isupper() and not key(lowerCamelCase_    ).isupper()]
						# Functions begin with a lowercase, they go last.
						UpperCAmelCase         =     [obj for obj in objects if not key(lowerCamelCase_    )[0].isupper()]

						UpperCAmelCase         =     ignore_underscore(lowerCamelCase_    )
						return sorted(lowerCamelCase_    ,   key=lowerCamelCase_    ) + sorted(lowerCamelCase_    ,   key=lowerCamelCase_    ) + sorted(lowerCamelCase_    ,   key=lowerCamelCase_    )

def    lowerCamelCase_(lowerCamelCase_    ) ->       Union[str, Any]:

						# This inner function sort imports between [ ].
						def _replace(lowerCamelCase_    ):
												UpperCAmelCase         =     match.groups()[0]
												if "," not in imports:
																		return F'[{imports}]'
												UpperCAmelCase         =     [part.strip().replace("\""    ,   ""    ) for part in imports.split(","    )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]    ) == 0:
																		UpperCAmelCase         =     keys[:-1]
												return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowerCamelCase_    )]    ) + "]"

						UpperCAmelCase         =     import_statement.split("\n"    )
						if len(lowerCamelCase_    ) > 3:
												# Here we have to sort internal imports that are on several lines (one per name):
												# key: [
												#     "object1",
												#     "object2",
												#     ...
												# ]

												# We may have to ignore one or two lines on each side.
												UpperCAmelCase         =     2 if lines[1].strip() == "[" else 1
												UpperCAmelCase         =     [(i, _re_strip_line.search(lowerCamelCase_    ).groups()[0]) for i, line in enumerate(lines[idx:-idx]    )]
												UpperCAmelCase         =     sort_objects(lowerCamelCase_    ,   key=lambda lowerCamelCase_    : x[1]    )
												UpperCAmelCase         =     [lines[x[0] + idx] for x in sorted_indices]
												return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]    )
						elif len(lowerCamelCase_    ) == 3:
												# Here we have to sort internal imports that are on one separate line:
												# key: [
												#     "object1", "object2", ...
												# ]
												if _re_bracket_content.search(lines[1]    ) is not None:
																		UpperCAmelCase         =     _re_bracket_content.sub(_replace    ,   lines[1]    )
												else:
																		UpperCAmelCase         =     [part.strip().replace("\""    ,   ""    ) for part in lines[1].split(","    )]
																		# We will have a final empty element if the line finished with a comma.
																		if len(keys[-1]    ) == 0:
																								UpperCAmelCase         =     keys[:-1]
																		UpperCAmelCase         =     get_indent(lines[1]    ) + ", ".join([F'"{k}"' for k in sort_objects(lowerCamelCase_    )]    )
												return "\n".join(lowerCamelCase_    )
						else:
												# Finally we have to deal with imports fitting on one line
												UpperCAmelCase         =     _re_bracket_content.sub(_replace    ,   lowerCamelCase_    )
												return import_statement

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_=True    ) ->       Optional[Any]:
						with open(lowerCamelCase_    ,   "r"    ) as f:
												UpperCAmelCase         =     f.read()

						if "_import_structure" not in code:
												return

						# Blocks of indent level 0
						UpperCAmelCase         =     split_code_in_indented_blocks(
						    lowerCamelCase_    ,   start_prompt="_import_structure = {"    ,   end_prompt="if TYPE_CHECKING:"    )

						# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
						for block_idx in range(1    ,   len(lowerCamelCase_    ) - 1    ):
												# Check if the block contains some `_import_structure`s thingy to sort.
												UpperCAmelCase         =     main_blocks[block_idx]
												UpperCAmelCase         =     block.split("\n"    )

												# Get to the start of the imports.
												UpperCAmelCase         =     0
												while line_idx < len(lowerCamelCase_    ) and "_import_structure" not in block_lines[line_idx]:
																		# Skip dummy import blocks
																		if "import dummy" in block_lines[line_idx]:
																								UpperCAmelCase         =     len(lowerCamelCase_    )
																		else:
																								line_idx += 1
												if line_idx >= len(lowerCamelCase_    ):
																		continue

												# Ignore beginning and last line: they don't contain anything.
												UpperCAmelCase         =     "\n".join(block_lines[line_idx:-1]    )
												UpperCAmelCase         =     get_indent(block_lines[1]    )
												# Slit the internal block into blocks of indent level 1.
												UpperCAmelCase         =     split_code_in_indented_blocks(lowerCamelCase_    ,   indent_level=lowerCamelCase_    )
												# We have two categories of import key: list or _import_structure[key].append/extend
												UpperCAmelCase         =     _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
												# Grab the keys, but there is a trap: some lines are empty or just comments.
												UpperCAmelCase         =     [(pattern.search(lowerCamelCase_    ).groups()[0] if pattern.search(lowerCamelCase_    ) is not None else None) for b in internal_blocks]
												# We only sort the lines with a key.
												UpperCAmelCase         =     [(i, key) for i, key in enumerate(lowerCamelCase_    ) if key is not None]
												UpperCAmelCase         =     [x[0] for x in sorted(lowerCamelCase_    ,   key=lambda lowerCamelCase_    : x[1]    )]

												# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
												UpperCAmelCase         =     0
												UpperCAmelCase         =     []
												for i in range(len(lowerCamelCase_    )    ):
																		if keys[i] is None:
																								reordered_blocks.append(internal_blocks[i]    )
																		else:
																								UpperCAmelCase         =     sort_objects_in_import(internal_blocks[sorted_indices[count]]    )
																								reordered_blocks.append(lowerCamelCase_    )
																								count += 1

        # And we put our main block back together with its first and last line.
												UpperCAmelCase         =     "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]    )

						if code != "\n".join(lowerCamelCase_    ):
												if check_only:
																		return True
												else:
																		print(F'Overwriting {file}.'    )
																		with open(lowerCamelCase_    ,   "w"    ) as f:
																								f.write("\n".join(lowerCamelCase_    )    )

def    lowerCamelCase_(lowerCamelCase_=True    ) ->       Dict:
						UpperCAmelCase         =     []
						for root, _, files in os.walk(lowerCamelCase_    ):
												if "__init__.py" in files:
																		UpperCAmelCase         =     sort_imports(os.path.join(lowerCamelCase_    ,   "__init__.py"    )    ,   check_only=lowerCamelCase_    )
																		if result:
																								UpperCAmelCase         =     [os.path.join(lowerCamelCase_    ,   "__init__.py"    )]
						if len(lowerCamelCase_    ) > 0:
												raise ValueError(F'Would overwrite {len(lowerCamelCase_    )} files, run `make style`.'    )


if __name__ == "__main__":
		__lowerCamelCase     :							Dict  							=				argparse.ArgumentParser()
		parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
		__lowerCamelCase     :							Any  							=				parser.parse_args()

		sort_imports_in_all_inits(check_only=args.check_only)








import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    MODEL_MAPPING,
		    BeitForImageClassification,
		    BeitForMaskedImageModeling,
		    BeitForSemanticSegmentation,
		    BeitModel,
		)
		from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		import PIL
		from PIL import Image

		from transformers import BeitImageProcessor





class  __magic_name__   :






		def __init__(						self      :     Any				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str=1_00				,		UpperCamelCase__      :     List[str]=13				,		UpperCamelCase__      :     Optional[int]=30				,		UpperCamelCase__      :     Union[str, Any]=2				,		UpperCamelCase__      :     List[str]=3				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Tuple=True				,		UpperCamelCase__      :     List[Any]=32				,		UpperCamelCase__      :     Any=4				,		UpperCamelCase__      :     Optional[Any]=4				,		UpperCamelCase__      :     Tuple=37				,		UpperCamelCase__      :     Any="gelu"				,		UpperCamelCase__      :     Any=0.1				,		UpperCamelCase__      :     Optional[Any]=0.1				,		UpperCamelCase__      :     int=10				,		UpperCamelCase__      :     Tuple=0.02				,		UpperCamelCase__      :     str=3				,		UpperCamelCase__      :     Optional[int]=None				,		UpperCamelCase__      :     Dict=[0, 1, 2, 3]				,		)				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     1_00
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     image_size
								UpperCAmelCase         =     patch_size
								UpperCAmelCase         =     num_channels
								UpperCAmelCase         =     is_training
								UpperCAmelCase         =     use_labels
								UpperCAmelCase         =     hidden_size
								UpperCAmelCase         =     num_hidden_layers
								UpperCAmelCase         =     num_attention_heads
								UpperCAmelCase         =     intermediate_size
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     hidden_dropout_prob
								UpperCAmelCase         =     attention_probs_dropout_prob
								UpperCAmelCase         =     type_sequence_label_size
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     scope
								UpperCAmelCase         =     out_indices
								UpperCAmelCase         =     num_labels

								# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase         =     (image_size // patch_size) ** 2
								UpperCAmelCase         =     num_patches + 1






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







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

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

								UpperCAmelCase         =     self.get_config()

								return config, pixel_values, labels, pixel_labels






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
								'''simple docstring'''







								return BeitConfig(
								    vocab_size=self.vocab_size				,		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=UpperCamelCase__				,		initializer_range=self.initializer_range				,		out_indices=self.out_indices				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     List[str]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitModel(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__      )
								self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForMaskedImageModeling(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length - 1, self.vocab_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.type_sequence_label_size
								UpperCAmelCase         =     BeitForImageClassification(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)      )

								# test greyscale images
								UpperCAmelCase         =     1
								UpperCAmelCase         =     BeitForImageClassification(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     BeitForSemanticSegmentation(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__      )
								self.parent.assertEqual(
								    result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
								UpperCAmelCase         =     model(UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(
								    result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
								'''simple docstring'''







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





@require_torch
class  __magic_name__   (							A__,  A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =(
		    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		lowercase      :       Optional[Any]          =(
		    {
		        '''feature-extraction''': BeitModel,
		        '''image-classification''': BeitForImageClassification,
		        '''image-segmentation''': BeitForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowercase      :       Dict          =False
		lowercase      :       int          =False
		lowercase      :       Union[str, Any]          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     BeitModelTester(self      )
								UpperCAmelCase         =     ConfigTester(self				,		config_class=UpperCamelCase__				,		has_text_modality=UpperCamelCase__				,		hidden_size=37      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
								'''simple docstring'''







								self.config_tester.run_common_tests()






		@unittest.skip(reason="BEiT does not use inputs_embeds"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								pass






		@require_torch_multi_gpu
		@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCAmelCase         =     model_class(UpperCamelCase__      )
														self.assertIsInstance(model.get_input_embeddings()				,		(nn.Module)      )
														UpperCAmelCase         =     model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(UpperCamelCase__				,		nn.Linear      )      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCAmelCase         =     ["pixel_values"]
														self.assertListEqual(arg_names[:1]				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					int:
								'''simple docstring'''







								if not self.model_tester.is_training:
														return

								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase         =     True

								for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if model_class in [*get_values(UpperCamelCase__      ), BeitForMaskedImageModeling]:
																				continue

														UpperCAmelCase         =     model_class(UpperCamelCase__      )
														model.to(UpperCamelCase__      )
														model.train()
														UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__				,		return_labels=UpperCamelCase__      )
														UpperCAmelCase         =     model(**UpperCamelCase__      ).loss
														loss.backward()






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
														return

								UpperCAmelCase         =     False
								UpperCAmelCase         =     True

								for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if (
														    model_class in [*get_values(UpperCamelCase__      ), BeitForMaskedImageModeling]
														    or not model_class.supports_gradient_checkpointing
														):
																				continue

														UpperCAmelCase         =     model_class(UpperCamelCase__      )
														model.gradient_checkpointing_enable()
														model.to(UpperCamelCase__      )
														model.train()
														UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__				,		return_labels=UpperCamelCase__      )
														UpperCAmelCase         =     model(**UpperCamelCase__      ).loss
														loss.backward()






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase         =     _config_zero_init(UpperCamelCase__      )
								for model_class in self.all_model_classes:
														UpperCAmelCase         =     model_class(config=UpperCamelCase__      )
														for name, param in model.named_parameters():
																				# we skip lambda parameters as these require special initial values
																				# determined by config.layer_scale_init_value
																				if "lambda" in name:
																										continue
																				if param.requires_grad:
																										self.assertIn(
																										    ((param.data.mean() * 1e9).round() / 1e9).item()				,		[0.0, 1.0]				,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,		)







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCAmelCase         =     BeitModel.from_pretrained(UpperCamelCase__      )
														self.assertIsNotNone(UpperCamelCase__      )

def    lowerCamelCase_() ->       str:
						UpperCAmelCase         =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image





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






		@cached_property
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[Any]:
								'''simple docstring'''







								return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      ).to(UpperCamelCase__      )

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).pixel_values.to(UpperCamelCase__      )

								# prepare bool_masked_pos
								UpperCAmelCase         =     torch.ones((1, 1_96)				,		dtype=torch.bool      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(pixel_values=UpperCamelCase__				,		bool_masked_pos=UpperCamelCase__      )
								UpperCAmelCase         =     outputs.logits

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 1_96, 81_92)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     torch.tensor(
								    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      ).to(UpperCamelCase__      )

								self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]				,		UpperCamelCase__				,		atol=1e-2      )      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      ).to(UpperCamelCase__      )

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )
								UpperCAmelCase         =     outputs.logits

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 10_00)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     torch.tensor([-1.23_85, -1.09_87, -1.01_08]      ).to(UpperCamelCase__      )

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

								UpperCAmelCase         =     2_81
								self.assertEqual(logits.argmax(-1      ).item()				,		UpperCamelCase__      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      ).to(
								    UpperCamelCase__      )

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )
								UpperCAmelCase         =     outputs.logits

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 2_18_41)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     torch.tensor([1.68_81, -0.27_87, 0.59_01]      ).to(UpperCamelCase__      )

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

								UpperCAmelCase         =     23_96
								self.assertEqual(logits.argmax(-1      ).item()				,		UpperCamelCase__      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
								UpperCAmelCase         =     model.to(UpperCamelCase__      )

								UpperCAmelCase         =     BeitImageProcessor(do_resize=UpperCamelCase__				,		size=6_40				,		do_center_crop=UpperCamelCase__      )

								UpperCAmelCase         =     load_dataset("hf-internal-testing/fixtures_ade20k"				,		split="test"      )
								UpperCAmelCase         =     Image.open(ds[0]["file"]      )
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )
								UpperCAmelCase         =     outputs.logits

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 1_50, 1_60, 1_60)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     version.parse(PIL.__version__      ) < version.parse("9.0.0"      )

								if is_pillow_less_than_a:
														UpperCAmelCase         =     torch.tensor(
														    [
														        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
														        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
														        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
														    ]				,		device=UpperCamelCase__				,		)
								else:
														UpperCAmelCase         =     torch.tensor(
														    [
														        [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
														        [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
														        [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
														    ]				,		device=UpperCamelCase__				,		)

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







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
								UpperCAmelCase         =     model.to(UpperCamelCase__      )

								UpperCAmelCase         =     BeitImageProcessor(do_resize=UpperCamelCase__				,		size=6_40				,		do_center_crop=UpperCamelCase__      )

								UpperCAmelCase         =     load_dataset("hf-internal-testing/fixtures_ade20k"				,		split="test"      )
								UpperCAmelCase         =     Image.open(ds[0]["file"]      )
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )

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

								UpperCAmelCase         =     image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__				,		target_sizes=[(5_00, 3_00)]      )
								UpperCAmelCase         =     torch.Size((5_00, 3_00)      )
								self.assertEqual(segmentation[0].shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__      )
								UpperCAmelCase         =     torch.Size((1_60, 1_60)      )
								self.assertEqual(segmentation[0].shape				,		UpperCamelCase__      )



