




'''simple docstring'''




import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     Tuple:
		UpperCamelCase__				:					Optional[Any]					=					None
		if token is not None:
				UpperCamelCase__				:					str					=					{'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

		UpperCamelCase__				:					int					=					f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
		UpperCamelCase__				:					List[str]					=					requests.get(lowerCamelCase_     ,      headers=lowerCamelCase_).json()
		UpperCamelCase__				:					Tuple					=					{}

		try:
				job_links.update({job['name']: job['html_url'] for job in result['jobs']})
				UpperCamelCase__				:					Optional[Any]					=					math.ceil((result['total_count'] - 100) / 100)

				for i in range(lowerCamelCase_):
						UpperCamelCase__				:					Optional[Any]					=					requests.get(url + f'&page={i + 2}'     ,      headers=lowerCamelCase_).json()
						job_links.update({job['name']: job['html_url'] for job in result['jobs']})

				return job_links
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}')

		return {}







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     Any:
		UpperCamelCase__				:					Optional[int]					=					None
		if token is not None:
				UpperCamelCase__				:					Any					=					{'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

		UpperCamelCase__				:					Tuple					=					f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
		UpperCamelCase__				:					Dict					=					requests.get(lowerCamelCase_     ,      headers=lowerCamelCase_).json()
		UpperCamelCase__				:					Tuple					=					{}

		try:
				artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']})
				UpperCamelCase__				:					Dict					=					math.ceil((result['total_count'] - 100) / 100)

				for i in range(lowerCamelCase_):
						UpperCamelCase__				:					Any					=					requests.get(url + f'&page={i + 2}'     ,      headers=lowerCamelCase_).json()
						artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']})

				return artifacts
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}')

		return {}







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					List[Any]					=					None
		if token is not None:
				UpperCamelCase__				:					Union[str, Any]					=					{'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

		UpperCamelCase__				:					Optional[Any]					=					requests.get(lowerCamelCase_     ,      headers=lowerCamelCase_     ,      allow_redirects=lowerCamelCase_)
		UpperCamelCase__				:					Tuple					=					result.headers['Location']
		UpperCamelCase__				:					Tuple					=					requests.get(lowerCamelCase_     ,      allow_redirects=lowerCamelCase_)
		UpperCamelCase__				:					List[Any]					=					os.path.join(lowerCamelCase_     ,      f'{artifact_name}.zip')
		with open(lowerCamelCase_     ,      'wb') as fp:
				fp.write(response.content)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     List[str]:
		UpperCamelCase__				:					List[Any]					=					[]
		UpperCamelCase__				:					List[str]					=					[]
		UpperCamelCase__				:					Optional[int]					=					None

		with zipfile.ZipFile(lowerCamelCase_) as z:
				for filename in z.namelist():
						if not os.path.isdir(lowerCamelCase_):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(lowerCamelCase_) as f:
												for line in f:
														UpperCamelCase__				:					Optional[Any]					=					line.decode('UTF-8').strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		UpperCamelCase__				:					List[Any]					=					line[: line.index(': ')]
																		UpperCamelCase__				:					Tuple					=					line[line.index(': ') + len(': ') :]
																		errors.append([error_line, error])
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith('FAILED '):
																# `test` is the test method that failed
																UpperCamelCase__				:					List[str]					=					line[len('FAILED ') :]
																failed_tests.append(lowerCamelCase_)
														elif filename == "job_name.txt":
																UpperCamelCase__				:					Tuple					=					line

		if len(lowerCamelCase_) != len(lowerCamelCase_):
				raise ValueError(
				    f'`errors` and `failed_tests` should have the same number of elements. Got {len(lowerCamelCase_)} for `errors` '
				    f'and {len(lowerCamelCase_)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
				    ' problem.')

		UpperCamelCase__				:					Dict					=					None
		if job_name and job_links:
				UpperCamelCase__				:					List[str]					=					job_links.get(lowerCamelCase_     ,      lowerCamelCase_)

		# A list with elements of the form (line of error, error, failed test)
		UpperCamelCase__				:					Union[str, Any]					=					[x + [y] + [job_link] for x, y in zip(lowerCamelCase_     ,      lowerCamelCase_)]

		return result







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     Any:
		UpperCamelCase__				:					int					=					[]

		UpperCamelCase__				:					Dict					=					[os.path.join(lowerCamelCase_     ,      lowerCamelCase_) for p in os.listdir(lowerCamelCase_) if p.endswith('.zip')]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(lowerCamelCase_     ,      job_links=lowerCamelCase_))

		return errors







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     Any:
		UpperCamelCase__				:					Optional[int]					=					Counter()
		counter.update([x[1] for x in logs])
		UpperCamelCase__				:					Dict					=					counter.most_common()
		UpperCamelCase__				:					str					=					{}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						UpperCamelCase__				:					List[Any]					=					{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

		UpperCamelCase__				:					str					=					dict(sorted(r.items()     ,      key=lambda lowerCamelCase_: item[1]["count"]     ,      reverse=lowerCamelCase_))
		return r







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		UpperCamelCase__				:					List[str]					=					test.split('::')[0]
		if test.startswith('tests/models/'):
				UpperCamelCase__				:					Optional[int]					=					test.split('/')[2]
		else:
				UpperCamelCase__				:					int					=					None

		return test







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     int:
		UpperCamelCase__				:					Tuple					=					[(x[0], x[1], get_model(x[2])) for x in logs]
		UpperCamelCase__				:					Tuple					=					[x for x in logs if x[2] is not None]
		UpperCamelCase__				:					List[Any]					=					{x[2] for x in logs}

		UpperCamelCase__				:					Union[str, Any]					=					{}
		for test in tests:
				UpperCamelCase__				:					List[Any]					=					Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test])
				UpperCamelCase__				:					int					=					counter.most_common()
				UpperCamelCase__				:					List[Any]					=					{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				UpperCamelCase__				:					Tuple					=					sum(error_counts.values())
				if n_errors > 0:
						UpperCamelCase__				:					int					=					{'count': n_errors, 'errors': error_counts}

		UpperCamelCase__				:					str					=					dict(sorted(r.items()     ,      key=lambda lowerCamelCase_: item[1]["count"]     ,      reverse=lowerCamelCase_))
		return r







def       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		UpperCamelCase__				:					Optional[int]					=					'| no. | error | status |'
		UpperCamelCase__				:					Dict					=					'|-:|:-|:-|'
		UpperCamelCase__				:					str					=					[header, sep]
		for error in reduced_by_error:
				UpperCamelCase__				:					Optional[int]					=					reduced_by_error[error]['count']
				UpperCamelCase__				:					str					=					f'| {count} | {error[:100]} |  |'
				lines.append(lowerCamelCase_)

		return "\n".join(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Any:
		UpperCamelCase__				:					Union[str, Any]					=					'| model | no. of errors | major error | count |'
		UpperCamelCase__				:					Dict					=					'|-:|-:|-:|-:|'
		UpperCamelCase__				:					Optional[Any]					=					[header, sep]
		for model in reduced_by_model:
				UpperCamelCase__				:					Any					=					reduced_by_model[model]['count']
				UpperCamelCase__				:					int					=					list(reduced_by_model[model]['errors'].items())[0]
				UpperCamelCase__				:					str					=					f'| {model} | {count} | {error[:60]} | {_count} |'
				lines.append(lowerCamelCase_)

		return "\n".join(lowerCamelCase_)


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
		parser.add_argument(
		    '--output_dir',
		    type=str,
		    required=True,
		    help='Where to store the downloaded artifacts and other result files.',
		)
		parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
		lowerCAmelCase__          =						parser.parse_args()

		os.makedirs(args.output_dir, exist_ok=True)

		lowerCAmelCase__          =						get_job_links(args.workflow_run_id, token=args.token)
		lowerCAmelCase__          =						{}
		# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
		# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
		if _job_links:
				for k, v in _job_links.items():
						# This is how GitHub actions combine job names.
						if " / " in k:
								lowerCAmelCase__          =						k.find(' / ')
								lowerCAmelCase__          =						k[index + len(' / ') :]
						lowerCAmelCase__          =						v
		with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
				json.dump(job_links, fp, ensure_ascii=False, indent=4)

		lowerCAmelCase__          =						get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
				json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		for idx, (name, url) in enumerate(artifacts.items()):
				download_artifact(name, url, args.output_dir, args.token)
				# Be gentle to GitHub
				time.sleep(1)

		lowerCAmelCase__          =						get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		lowerCAmelCase__          =						Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		lowerCAmelCase__          =						counter.most_common(30)
		for item in most_common:
				print(item)

		with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
				json.dump(errors, fp, ensure_ascii=False, indent=4)

		lowerCAmelCase__          =						reduce_by_error(errors)
		lowerCAmelCase__          =						reduce_by_model(errors)

		lowerCAmelCase__          =						make_github_table(reduced_by_error)
		lowerCAmelCase__          =						make_github_table_per_model(reduced_by_model)

		with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)
		with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)












'''simple docstring'''




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMAEForPreTraining, ViTMAEModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor







class       __lowercase					:

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						Optional[int]=13  ,					UpperCAmelCase_   :						Tuple=30  ,					UpperCAmelCase_   :						Dict=2  ,					UpperCAmelCase_   :						Dict=3  ,					UpperCAmelCase_   :						Tuple=True  ,					UpperCAmelCase_   :						str=True  ,					UpperCAmelCase_   :						Tuple=32  ,					UpperCAmelCase_   :						List[str]=5  ,					UpperCAmelCase_   :						str=4  ,					UpperCAmelCase_   :						Optional[int]=37  ,					UpperCAmelCase_   :						str="gelu"  ,					UpperCAmelCase_   :						List[str]=0.1  ,					UpperCAmelCase_   :						Dict=0.1  ,					UpperCAmelCase_   :						Dict=10  ,					UpperCAmelCase_   :						Optional[int]=0.02  ,					UpperCAmelCase_   :						Union[str, Any]=3  ,					UpperCAmelCase_   :						Any=0.6  ,					UpperCAmelCase_   :						Dict=None  ,					):
			UpperCamelCase__				:					Tuple					=					parent
			UpperCamelCase__				:					List[str]					=					batch_size
			UpperCamelCase__				:					Optional[Any]					=					image_size
			UpperCamelCase__				:					Optional[Any]					=					patch_size
			UpperCamelCase__				:					List[str]					=					num_channels
			UpperCamelCase__				:					Union[str, Any]					=					is_training
			UpperCamelCase__				:					int					=					use_labels
			UpperCamelCase__				:					Optional[int]					=					hidden_size
			UpperCamelCase__				:					Any					=					num_hidden_layers
			UpperCamelCase__				:					str					=					num_attention_heads
			UpperCamelCase__				:					str					=					intermediate_size
			UpperCamelCase__				:					Union[str, Any]					=					hidden_act
			UpperCamelCase__				:					Optional[int]					=					hidden_dropout_prob
			UpperCamelCase__				:					Tuple					=					attention_probs_dropout_prob
			UpperCamelCase__				:					Any					=					type_sequence_label_size
			UpperCamelCase__				:					int					=					initializer_range
			UpperCamelCase__				:					Optional[int]					=					mask_ratio
			UpperCamelCase__				:					int					=					scope

			# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
			# (we add 1 for the [CLS] token)
			UpperCamelCase__				:					str					=					(image_size // patch_size) ** 2
			UpperCamelCase__				:					Dict					=					int(math.ceil((1 - mask_ratio) * (num_patches + 1)))

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			UpperCamelCase__				:					List[str]					=					None
			if self.use_labels:
					UpperCamelCase__				:					Dict					=					ids_tensor([self.batch_size]  ,					self.type_sequence_label_size)

			UpperCamelCase__				:					Any					=					self.get_config()

			return config, pixel_values, labels

	def      __UpperCamelCase							(      self   :						List[Any]):
			return ViTMAEConfig(
			    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  ,					mask_ratio=self.mask_ratio  ,					)

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Dict					=					ViTMAEModel(config=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Optional[int]					=					model(UpperCAmelCase_)
			self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size))

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Tuple):
			UpperCamelCase__				:					List[Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Dict					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					(self.image_size // self.patch_size) ** 2
			UpperCamelCase__				:					Optional[int]					=					self.patch_size**2 * self.num_channels
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))

			# test greyscale images
			UpperCamelCase__				:					List[Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					List[str]					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			UpperCamelCase__				:					Union[str, Any]					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					self.patch_size**2
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))





	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[str]					=					self.prepare_config_and_inputs()
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					config_and_inputs
			UpperCamelCase__				:					Any					=					{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	_lowerCamelCase              =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					List[str]					=					ViTMAEModelTester(self)
			UpperCamelCase__				:					Any					=					ConfigTester(self  ,					config_class=UpperCAmelCase_  ,					has_text_modality=UpperCAmelCase_  ,					hidden_size=37)

	def      __UpperCamelCase							(      self   :						Any):
			self.config_tester.run_common_tests()

	@unittest.skip(reason='ViTMAE does not use inputs_embeds')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					List[str]					=					model_class(UpperCAmelCase_)
					self.assertIsInstance(model.get_input_embeddings()  ,					(nn.Module))
					UpperCamelCase__				:					Optional[Any]					=					model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(UpperCAmelCase_  ,					nn.Linear))

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__,	UpperCamelCase__				:					Dict					=					self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCamelCase__				:					Optional[int]					=					['pixel_values']
					self.assertListEqual(arg_names[:1]  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Union[str, Any]):
			# make masks reproducible
			np.random.seed(2)

			UpperCamelCase__				:					str					=					int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
			UpperCamelCase__				:					Tuple					=					np.random.uniform(size=(self.model_tester.batch_size, num_patches))
			UpperCamelCase__				:					Optional[Any]					=					torch.from_numpy(UpperCAmelCase_)

			# Add `noise` argument.
			# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
			UpperCamelCase__				:					List[str]					=					pt_noise

			super().check_pt_tf_models(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					Optional[Any]					=					model_class(UpperCAmelCase_)
					model.to(UpperCAmelCase_)
					model.eval()
					# make random mask reproducible
					torch.manual_seed(2)
					with torch.no_grad():
							UpperCamelCase__				:					Tuple					=					model(**self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_))

					UpperCamelCase__				:					Dict					=					outputs[0].cpu().numpy()
					UpperCamelCase__				:					Optional[int]					=					0

					with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(UpperCAmelCase_)
							UpperCamelCase__				:					str					=					model_class.from_pretrained(UpperCAmelCase_)
							model.to(UpperCAmelCase_)
							# make random mask reproducible
							torch.manual_seed(2)
							with torch.no_grad():
									UpperCamelCase__				:					List[str]					=					model(**self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_))

							# Make sure we don't have nans
							UpperCamelCase__				:					Tuple					=					after_outputs[0].cpu().numpy()
							UpperCamelCase__				:					Any					=					0
							UpperCamelCase__				:					Union[str, Any]					=					np.amax(np.abs(out_a - out_a))
							self.assertLessEqual(UpperCAmelCase_  ,					1e-5)

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Optional[int]):
			pass

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
	def      __UpperCamelCase							(      self   :						Optional[int]):
			pass





	@slow
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCamelCase__				:					Tuple					=					ViTMAEModel.from_pretrained(UpperCAmelCase_)
					self.assertIsNotNone(UpperCAmelCase_)







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					int					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
		return image







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

	@cached_property
	def      __UpperCamelCase							(      self   :						int):
			return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None





	@slow
	def      __UpperCamelCase							(      self   :						str):
			# make random mask reproducible across the PT and TF model
			np.random.seed(2)

			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.default_image_processor
			UpperCamelCase__				:					Dict					=					prepare_img()
			UpperCamelCase__				:					Optional[int]					=					image_processor(images=UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# prepare a noise vector that will be also used for testing the TF model
			# (this way we can ensure that the PT and TF models operate on the same inputs)
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEConfig()
			UpperCamelCase__				:					int					=					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
			UpperCamelCase__				:					Any					=					np.random.uniform(size=(1, num_patches))

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Dict					=					model(**UpperCAmelCase_  ,					noise=torch.from_numpy(UpperCAmelCase_).to(device=UpperCAmelCase_))

			# verify the logits
			UpperCamelCase__				:					Tuple					=					torch.Size((1, 196, 768))
			self.assertEqual(outputs.logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					torch.tensor(
			    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

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




