



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VivitImageProcessor





class  __magic_name__   (							unittest.TestCase						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Any=7				,		UpperCamelCase__      :     List[Any]=3				,		UpperCamelCase__      :     Optional[int]=10				,		UpperCamelCase__      :     int=18				,		UpperCamelCase__      :     Any=30				,		UpperCamelCase__      :     Dict=4_00				,		UpperCamelCase__      :     Tuple=True				,		UpperCamelCase__      :     str=None				,		UpperCamelCase__      :     Optional[Any]=True				,		UpperCamelCase__      :     List[str]=[0.5, 0.5, 0.5]				,		UpperCamelCase__      :     Optional[Any]=[0.5, 0.5, 0.5]				,		UpperCamelCase__      :     Dict=None				,		)				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     size if size is not None else {"shortest_edge": 18}
								UpperCAmelCase         =     crop_size if crop_size is not None else {"height": 18, "width": 18}

								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     num_channels
								UpperCAmelCase         =     num_frames
								UpperCAmelCase         =     image_size
								UpperCAmelCase         =     min_resolution
								UpperCAmelCase         =     max_resolution
								UpperCAmelCase         =     do_resize
								UpperCAmelCase         =     size
								UpperCAmelCase         =     do_normalize
								UpperCAmelCase         =     image_mean
								UpperCAmelCase         =     image_std
								UpperCAmelCase         =     crop_size







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







								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}





@require_torch
@require_vision
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       str          =VivitImageProcessor if is_vision_available() else None






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







								UpperCAmelCase         =     VivitImageProcessingTester(self      )






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







								return self.image_processor_tester.prepare_image_processor_dict()






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







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"image_mean"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"image_std"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"do_normalize"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"do_center_crop"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"size"      )      )






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







								UpperCAmelCase         =     self.image_processing_class.from_dict(self.image_processor_dict      )
								self.assertEqual(image_processor.size				,		{"shortest_edge": 18}      )
								self.assertEqual(image_processor.crop_size				,		{"height": 18, "width": 18}      )

								UpperCAmelCase         =     self.image_processing_class.from_dict(self.image_processor_dict				,		size=42				,		crop_size=84      )
								self.assertEqual(image_processor.size				,		{"shortest_edge": 42}      )
								self.assertEqual(image_processor.crop_size				,		{"height": 84, "width": 84}      )






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







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								# create random PIL videos
								UpperCAmelCase         =     prepare_video_inputs(self.image_processor_tester				,		equal_resolution=UpperCamelCase__      )
								for video in video_inputs:
														self.assertIsInstance(UpperCamelCase__				,		UpperCamelCase__      )
														self.assertIsInstance(video[0]				,		Image.Image      )

								# Test not batched input
								UpperCAmelCase         =     image_processing(video_inputs[0]				,		return_tensors="pt"      ).pixel_values
								self.assertEqual(
								    encoded_videos.shape				,		(
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,		)

								# Test batched
								UpperCAmelCase         =     image_processing(UpperCamelCase__				,		return_tensors="pt"      ).pixel_values
								self.assertEqual(
								    encoded_videos.shape				,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,		)






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







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								UpperCAmelCase         =     prepare_video_inputs(self.image_processor_tester				,		equal_resolution=UpperCamelCase__				,		numpify=UpperCamelCase__      )
								for video in video_inputs:
														self.assertIsInstance(UpperCamelCase__				,		UpperCamelCase__      )
														self.assertIsInstance(video[0]				,		np.ndarray      )

								# Test not batched input
								UpperCAmelCase         =     image_processing(video_inputs[0]				,		return_tensors="pt"      ).pixel_values
								self.assertEqual(
								    encoded_videos.shape				,		(
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,		)

								# Test batched
								UpperCAmelCase         =     image_processing(UpperCamelCase__				,		return_tensors="pt"      ).pixel_values
								self.assertEqual(
								    encoded_videos.shape				,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,		)







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







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								UpperCAmelCase         =     prepare_video_inputs(self.image_processor_tester				,		equal_resolution=UpperCamelCase__				,		torchify=UpperCamelCase__      )
								for video in video_inputs:
														self.assertIsInstance(UpperCamelCase__				,		UpperCamelCase__      )
														self.assertIsInstance(video[0]				,		torch.Tensor      )

								# Test not batched input
								UpperCAmelCase         =     image_processing(video_inputs[0]				,		return_tensors="pt"      ).pixel_values
								self.assertEqual(
								    encoded_videos.shape				,		(
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,		)

								# Test batched
								UpperCAmelCase         =     image_processing(UpperCamelCase__				,		return_tensors="pt"      ).pixel_values
								self.assertEqual(
								    encoded_videos.shape				,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,		)








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__      )



