



'''simple docstring'''





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCamelCase     :							str  							=				os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCamelCase     :							Optional[Any]  							=				"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"





class  __magic_name__   (							unittest.TestCase						):






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







								UpperCAmelCase         =     tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,		"models/bert/"      )      )
								UpperCAmelCase         =     self.transformer_dir
								shutil.copy(
								    os.path.join(UpperCamelCase__				,		"src/transformers/models/bert/modeling_bert.py"      )				,		os.path.join(self.transformer_dir				,		"models/bert/modeling_bert.py"      )				,		)






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







								UpperCAmelCase         =     "src/transformers"
								shutil.rmtree(self.transformer_dir      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]=None      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
														UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
								UpperCAmelCase         =     black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=1_19      )
								UpperCAmelCase         =     black.format_str(UpperCamelCase__				,		mode=UpperCamelCase__      )
								UpperCAmelCase         =     os.path.join(self.transformer_dir				,		"new_code.py"      )
								with open(UpperCamelCase__				,		"w"				,		newline="\n"      ) as f:
														f.write(UpperCamelCase__      )
								if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__      )      ) == 0      )
								else:
														check_copies.is_copy_consistent(f.name				,		overwrite=UpperCamelCase__      )
														with open(UpperCamelCase__				,		"r"      ) as f:
																				self.assertTrue(f.read()				,		UpperCamelCase__      )






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







								UpperCAmelCase         =     check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )






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







								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"				,		"BertLMPredictionHead"				,		REFERENCE_CODE + "\n"				,		)

								# With no empty line at the end
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"				,		"BertLMPredictionHead"				,		UpperCamelCase__				,		)

								# Copy consistency with rename
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"				,		"TestModelLMPredictionHead"				,		re.sub("Bert"				,		"TestModel"				,		UpperCamelCase__      )				,		)

								# Copy consistency with a really long name
								UpperCAmelCase         =     "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
								self.check_copy_consistency(
								    F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'				,		F'{long_class_name}LMPredictionHead'				,		re.sub("Bert"				,		UpperCamelCase__				,		UpperCamelCase__      )				,		)

								# Copy consistency with overwrite
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"				,		"TestModelLMPredictionHead"				,		UpperCamelCase__				,		overwrite_result=re.sub("Bert"				,		"TestModel"				,		UpperCamelCase__      )				,		)







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







								UpperCAmelCase         =     check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
								    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
								    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
								    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
								    " Luong, Quoc V. Le, Christopher D. Manning."
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
								    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
								    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
								    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
								    " Christopher D. Manning 发布。\n"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								self.assertFalse(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								# Check whether the number of models is equal to README.md after conversion.
								self.assertTrue(UpperCamelCase__      )

								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
								    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								# Check if the model link is synchronized.
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )








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__      )



