




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        __snake_case  :



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		int=13		,	_UpperCAmelCase      :		Union[str, Any]=30		,	_UpperCAmelCase      :		List[str]=2		,	_UpperCAmelCase      :		Tuple=3		,	_UpperCAmelCase      :		Any=True		,	_UpperCAmelCase      :		Tuple=True		,	_UpperCAmelCase      :		int=32		,	_UpperCAmelCase      :		List[Any]=5		,	_UpperCAmelCase      :		int=4		,	_UpperCAmelCase      :		Union[str, Any]=37		,	_UpperCAmelCase      :		Optional[Any]="gelu"		,	_UpperCAmelCase      :		Dict=0.1		,	_UpperCAmelCase      :		Optional[int]=0.1		,	_UpperCAmelCase      :		List[Any]=10		,	_UpperCAmelCase      :		List[str]=0.02		,	_UpperCAmelCase      :		Optional[int]=3		,	_UpperCAmelCase      :		Any=0.6		,	_UpperCAmelCase      :		Any=None		,	)  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      parent
								_lowerCAmelCase    :    Any										=      batch_size
								_lowerCAmelCase    :    List[str]										=      image_size
								_lowerCAmelCase    :    Tuple										=      patch_size
								_lowerCAmelCase    :    int										=      num_channels
								_lowerCAmelCase    :    Optional[int]										=      is_training
								_lowerCAmelCase    :    int										=      use_labels
								_lowerCAmelCase    :    List[str]										=      hidden_size
								_lowerCAmelCase    :    Optional[Any]										=      num_hidden_layers
								_lowerCAmelCase    :    Optional[int]										=      num_attention_heads
								_lowerCAmelCase    :    Any										=      intermediate_size
								_lowerCAmelCase    :    str										=      hidden_act
								_lowerCAmelCase    :    List[Any]										=      hidden_dropout_prob
								_lowerCAmelCase    :    List[str]										=      attention_probs_dropout_prob
								_lowerCAmelCase    :    str										=      type_sequence_label_size
								_lowerCAmelCase    :    Optional[Any]										=      initializer_range
								_lowerCAmelCase    :    Tuple										=      mask_ratio
								_lowerCAmelCase    :    Tuple										=      scope

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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								_lowerCAmelCase    :    int										=      None
								if self.use_labels:
												_lowerCAmelCase    :    Union[str, Any]										=      ids_tensor([self.batch_size]		,	self.type_sequence_label_size      )

								_lowerCAmelCase    :    Optional[int]										=      self.get_config()

								return config, pixel_values, labels



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Dict:





								'''simple docstring'''




								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       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Dict      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      ViTMAEModel(config=_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase      )
								self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Any      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      ViTMAEForPreTraining(_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    int										=      model(_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      (self.image_size // self.patch_size) ** 2
								_lowerCAmelCase    :    Union[str, Any]										=      self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, num_patches, expected_num_channels)      )

								# test greyscale images
								_lowerCAmelCase    :    Dict										=      1
								_lowerCAmelCase    :    Dict										=      ViTMAEForPreTraining(_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    Union[str, Any]										=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
								_lowerCAmelCase    :    str										=      model(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      self.patch_size**2
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, num_patches, expected_num_channels)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.prepare_config_and_inputs()
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    int										=      config_and_inputs
								_lowerCAmelCase    :    List[str]										=      {"""pixel_values""": pixel_values}
								return config, inputs_dict






@require_torch
class        __snake_case  (_a						,	_a						,	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       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      ViTMAEModelTester(self      )
								_lowerCAmelCase    :    int										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	has_text_modality=_UpperCAmelCase		,	hidden_size=37      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[int]:





								'''simple docstring'''




								self.config_tester.run_common_tests()



				@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs_for_common()

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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs_for_common()

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

												_lowerCAmelCase    :    Any										=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								np.random.seed(2      )

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

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								_lowerCAmelCase    :    Any										=      pt_noise

								super().check_pt_tf_models(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    str										=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCAmelCase    :    Optional[int]										=      model_class(_UpperCAmelCase      )
												model.to(_UpperCAmelCase      )
												model.eval()
												# make random mask reproducible
												torch.manual_seed(2      )
												with torch.no_grad():
																_lowerCAmelCase    :    List[str]										=      model(**self._prepare_for_class(_UpperCAmelCase		,	_UpperCAmelCase      )      )

												_lowerCAmelCase    :    List[Any]										=      outputs[0].cpu().numpy()
												_lowerCAmelCase    :    Union[str, Any]										=      0

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

																# Make sure we don't have nans
																_lowerCAmelCase    :    int										=      after_outputs[0].cpu().numpy()
																_lowerCAmelCase    :    Optional[Any]										=      0
																_lowerCAmelCase    :    List[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
    to get deterministic results."""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Tuple:





								'''simple docstring'''




								pass



				@unittest.skip(
				    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    str:





								'''simple docstring'''




								pass



				@unittest.skip(
				    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    List[Any]:





								'''simple docstring'''




								pass



				@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Tuple:





								'''simple docstring'''




								pass



				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Tuple:





								'''simple docstring'''




								pass



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Any:





								'''simple docstring'''




								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase    :    str										=      ViTMAEModel.from_pretrained(_UpperCAmelCase      )
												self.assertIsNotNone(_UpperCAmelCase      )






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    List[Any]										=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
				return image





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



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    int:





								'''simple docstring'''




								return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""      ) if is_vision_available() else None



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




								np.random.seed(2      )

								_lowerCAmelCase    :    str										=      ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""      ).to(_UpperCAmelCase      )

								_lowerCAmelCase    :    Union[str, Any]										=      self.default_image_processor
								_lowerCAmelCase    :    Optional[int]										=      prepare_img()
								_lowerCAmelCase    :    Any										=      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)
								_lowerCAmelCase    :    Optional[int]										=      ViTMAEConfig()
								_lowerCAmelCase    :    Any										=      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2      )
								_lowerCAmelCase    :    List[Any]										=      np.random.uniform(size=(1, num_patches)      )

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

								# verify the logits
								_lowerCAmelCase    :    Optional[Any]										=      torch.Size((1, 196, 768)      )
								self.assertEqual(outputs.logits.shape		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      torch.tensor(
								    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]      )

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







import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


_lowerCamelCase      :  Optional[Any]      						=						TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


_lowerCamelCase      :  Optional[int]      						=						[]
_lowerCamelCase      :  Tuple      						=						[]

_lowerCamelCase      :  List[str]      						=						{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

_lowerCamelCase      :  Optional[Any]      						=						[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": F'''🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results''',
            "emoji": True,
        },
    }
]

_lowerCamelCase      :  str      						=						0
for log in Path().glob("*.log"):
	_lowerCamelCase      :  str      						=						0
	with open(log, "r") as f:
		for line in f:
			_lowerCamelCase      :  Optional[Any]      						=						json.loads(line)
			if line.get("nodeid", "") != "":
				_lowerCamelCase      :  str      						=						line["nodeid"]
				if line.get("duration", None) is not None:
					_lowerCamelCase      :  str      						=						F'''{line['duration']:.4f}'''
					if line.get("outcome", "") == "failed":
						section_num_failed += 1
						failed.append([test, duration, log.name.split("_")[0]])
						total_num_failed += 1
	group_info.append([str(log), section_num_failed, failed])
	_lowerCamelCase      :  Optional[int]      						=						[]
	log.unlink()

_lowerCamelCase      :  str      						=						""
_lowerCamelCase      :  Optional[Any]      						=						[]
if total_num_failed > 0:
	for name, num_failed, failed_tests in group_info:
		if num_failed > 0:
			if num_failed == 1:
				message += F"*{name[1:]}: {num_failed} failed test*\n"
			else:
				message += F"*{name[1:]}: {num_failed} failed tests*\n"
			_lowerCamelCase      :  Any      						=						[]
			_lowerCamelCase      :  Dict      						=						{}
			for test in failed_tests:
				_lowerCamelCase      :  Union[str, Any]      						=						test[0].split("::")
				_lowerCamelCase      :  str      						=						data[0].split("/")[-1]
				if data[0] not in filesafailed:
					_lowerCamelCase      :  List[str]      						=						[data[1:]]
				else:
					filesafailed[data[0]] += [data[1:]]
				failed_table.append(data)

			_lowerCamelCase      :  List[Any]      						=						[test[0] for test in failed_table]
			_lowerCamelCase      :  Optional[Any]      						=						list(set(files))
			# Count number of instances in failed_tests
			_lowerCamelCase      :  Union[str, Any]      						=						[]
			for file in individual_files:
				table.append([file, len(filesafailed[file])])

			_lowerCamelCase      :  Optional[Any]      						=						tabulate(
			    table,
			    headers=["Test Location", "Num Failed"],
			    tablefmt=hf_table_format,
			    stralign="right",
			)
			message += F"\n```\n{failed_table}\n```"
			all_filesafailed.append(filesafailed)
	if len(message) > 3_0_0_0:
		_lowerCamelCase      :  List[str]      						=						"Too many failed tests, please see the full report in the Action results."
		_lowerCamelCase      :  Tuple      						=						len(err) + 1_0
		_lowerCamelCase      :  Union[str, Any]      						=						message[: 3_0_0_0 - offset] + F'''\n...\n```\n{err}'''
	print(F'''### {message}''')
else:
	_lowerCamelCase      :  List[str]      						=						"No failed tests! 🤗"
	print(F'''## {message}''')
	payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
	from slack_sdk import WebClient

	_lowerCamelCase      :  Optional[int]      						=						WebClient(token=os.environ["SLACK_API_TOKEN"])
	if message != "No failed tests! 🤗":
		_lowerCamelCase      :  int      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": message,
		    },
		}
		payload.append(md_report)
		_lowerCamelCase      :  List[Any]      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": "*For more details:*",
		    },
		    "accessory": {
		        "type": "button",
		        "text": {
		            "type": "plain_text",
		            "text": "Check Action results",
		            "emoji": True,
		        },
		        "url": F'''https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
		    },
		}
		payload.append(action_button)
		_lowerCamelCase      :  Union[str, Any]      						=						{
		    "type": "context",
		    "elements": [
		        {
		            "type": "plain_text",
		            "text": F'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
		        }
		    ],
		}
		payload.append(date_report)
	_lowerCamelCase      :  int      						=						client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
	_lowerCamelCase      :  List[Any]      						=						response.data["ts"]
	for failed_file in all_filesafailed:
		for test_location, test_failures in failed_file.items():
			# Keep only the first instance of the test name
			_lowerCamelCase      :  Dict      						=						""
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					_lowerCamelCase      :  Optional[int]      						=						row[0]
				else:
					_lowerCamelCase      :  List[str]      						=						""

			_lowerCamelCase      :  int      						=						{
			    "type": "section",
			    "text": {
			        "type": "mrkdwn",
			        "text": F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```''',
			    },
			}

			client.chat_postMessage(
			    channel="#accelerate-ci-daily",
			    thread_ts=ts,
			    blocks=[payload],
			)

