
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    LayoutLMvaConfig,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				)
				from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import LayoutLMvaImageProcessor







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=2				,       UpperCamelCase__=3				,       UpperCamelCase__=4				,       UpperCamelCase__=2				,       UpperCamelCase__=7				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=99				,       UpperCamelCase__=36				,       UpperCamelCase__=3				,       UpperCamelCase__=4				,       UpperCamelCase__=37				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=512				,       UpperCamelCase__=16				,       UpperCamelCase__=2				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=6				,       UpperCamelCase__=6				,       UpperCamelCase__=3				,       UpperCamelCase__=4				,       UpperCamelCase__=None				,       UpperCamelCase__=1000				,       ):
					A__  :	List[Any]         =							parent
					A__  :	Dict         =							batch_size
					A__  :	Optional[Any]         =							num_channels
					A__  :	Optional[Any]         =							image_size
					A__  :	Optional[Any]         =							patch_size
					A__  :	List[Any]         =							text_seq_length
					A__  :	List[Any]         =							is_training
					A__  :	Optional[int]         =							use_input_mask
					A__  :	Dict         =							use_token_type_ids
					A__  :	List[Any]         =							use_labels
					A__  :	int         =							vocab_size
					A__  :	Any         =							hidden_size
					A__  :	List[Any]         =							num_hidden_layers
					A__  :	Dict         =							num_attention_heads
					A__  :	int         =							intermediate_size
					A__  :	Optional[int]         =							hidden_act
					A__  :	Any         =							hidden_dropout_prob
					A__  :	Any         =							attention_probs_dropout_prob
					A__  :	int         =							max_position_embeddings
					A__  :	Optional[Any]         =							type_vocab_size
					A__  :	Tuple         =							type_sequence_label_size
					A__  :	Tuple         =							initializer_range
					A__  :	Dict         =							coordinate_size
					A__  :	Optional[Any]         =							shape_size
					A__  :	int         =							num_labels
					A__  :	Dict         =							num_choices
					A__  :	Any         =							scope
					A__  :	Optional[Any]         =							range_bbox

					# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
					A__  :	Any         =							text_seq_length
					A__  :	Optional[Any]         =							(image_size // patch_size) ** 2 + 1
					A__  :	List[Any]         =							self.text_seq_length + self.image_seq_length





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							ids_tensor([self.batch_size, self.text_seq_length]				,       self.vocab_size		)

					A__  :	Union[str, Any]         =							ids_tensor([self.batch_size, self.text_seq_length, 4]				,       self.range_bbox		)
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]		):
									for j in range(bbox.shape[1]		):
													if bbox[i, j, 3] < bbox[i, j, 1]:
																	A__  :	Tuple         =							bbox[i, j, 3]
																	A__  :	Optional[int]         =							bbox[i, j, 1]
																	A__  :	Optional[Any]         =							t
													if bbox[i, j, 2] < bbox[i, j, 0]:
																	A__  :	str         =							bbox[i, j, 2]
																	A__  :	List[Any]         =							bbox[i, j, 0]
																	A__  :	List[Any]         =							t

					A__  :	Any         =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

					A__  :	Any         =							None
					if self.use_input_mask:
									A__  :	int         =							random_attention_mask([self.batch_size, self.text_seq_length]		)

					A__  :	str         =							None
					if self.use_token_type_ids:
									A__  :	Union[str, Any]         =							ids_tensor([self.batch_size, self.text_seq_length]				,       self.type_vocab_size		)

					A__  :	Dict         =							None
					A__  :	List[str]         =							None
					if self.use_labels:
									A__  :	int         =							ids_tensor([self.batch_size]				,       self.type_sequence_label_size		)
									A__  :	Optional[int]         =							ids_tensor([self.batch_size, self.text_seq_length]				,       self.num_labels		)

					A__  :	Dict         =							LayoutLMvaConfig(
					    vocab_size=self.vocab_size				,       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				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       initializer_range=self.initializer_range				,       coordinate_size=self.coordinate_size				,       shape_size=self.shape_size				,       input_size=self.image_size				,       patch_size=self.patch_size				,       )

					return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Any         =							LayoutLMvaModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()

					# text + image
					A__  :	List[str]         =							model(UpperCamelCase__				,       pixel_values=UpperCamelCase__		)
					A__  :	Optional[int]         =							model(
					    UpperCamelCase__				,       bbox=UpperCamelCase__				,       pixel_values=UpperCamelCase__				,       attention_mask=UpperCamelCase__				,       token_type_ids=UpperCamelCase__		)
					A__  :	Union[str, Any]         =							model(UpperCamelCase__				,       bbox=UpperCamelCase__				,       pixel_values=UpperCamelCase__				,       token_type_ids=UpperCamelCase__		)
					A__  :	Optional[int]         =							model(UpperCamelCase__				,       bbox=UpperCamelCase__				,       pixel_values=UpperCamelCase__		)

					self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)		)

					# text only
					A__  :	Tuple         =							model(UpperCamelCase__		)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,       (self.batch_size, self.text_seq_length, self.hidden_size)		)

					# image only
					A__  :	Tuple         =							model(pixel_values=UpperCamelCase__		)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,       (self.batch_size, self.image_seq_length, self.hidden_size)		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	List[Any]         =							self.num_labels
					A__  :	List[Any]         =							LayoutLMvaForSequenceClassification(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	Any         =							model(
					    UpperCamelCase__				,       bbox=UpperCamelCase__				,       pixel_values=UpperCamelCase__				,       attention_mask=UpperCamelCase__				,       token_type_ids=UpperCamelCase__				,       labels=UpperCamelCase__				,       )
					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Dict         =							self.num_labels
					A__  :	str         =							LayoutLMvaForTokenClassification(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	List[str]         =							model(
					    UpperCamelCase__				,       bbox=UpperCamelCase__				,       pixel_values=UpperCamelCase__				,       attention_mask=UpperCamelCase__				,       token_type_ids=UpperCamelCase__				,       labels=UpperCamelCase__				,       )
					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.text_seq_length, self.num_labels)		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Optional[Any]         =							LayoutLMvaForQuestionAnswering(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	Dict         =							model(
					    UpperCamelCase__				,       bbox=UpperCamelCase__				,       pixel_values=UpperCamelCase__				,       attention_mask=UpperCamelCase__				,       token_type_ids=UpperCamelCase__				,       start_positions=UpperCamelCase__				,       end_positions=UpperCamelCase__				,       )
					self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)		)
					self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.prepare_config_and_inputs()
					(
					    A__							
					)  :	List[Any]         =							config_and_inputs
					A__  :	List[Any]         =							{
					    '''input_ids''': input_ids,
					    '''bbox''': bbox,
					    '''pixel_values''': pixel_values,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': input_mask,
					}
					return config, inputs_dict







@require_torch
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False

	_lowerCAmelCase							       =				(
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCAmelCase							       =				(
	    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
					# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
					# the sequence dimension of the text embedding only.
					# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
					return True





	def   __snake_case				(			self		):
					A__  :	str         =							LayoutLMvaModelTester(self		)
					A__  :	Union[str, Any]         =							ConfigTester(self				,       config_class=UpperCamelCase__				,       hidden_size=37		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__  :	str         =							copy.deepcopy(UpperCamelCase__		)
					if model_class in get_values(UpperCamelCase__		):
									A__  :	Optional[Any]         =							{
									    k: v.unsqueeze(1		).expand(-1				,       self.model_tester.num_choices				,       -1		).contiguous()
									    if isinstance(UpperCamelCase__				,       torch.Tensor		) and v.ndim > 1
									    else v
									    for k, v in inputs_dict.items()
									}
					if return_labels:
									if model_class in get_values(UpperCamelCase__		):
													A__  :	List[Any]         =							torch.ones(self.model_tester.batch_size				,       dtype=torch.long				,       device=UpperCamelCase__		)
									elif model_class in get_values(UpperCamelCase__		):
													A__  :	List[Any]         =							torch.zeros(
													    self.model_tester.batch_size				,       dtype=torch.long				,       device=UpperCamelCase__		)
													A__  :	List[str]         =							torch.zeros(
													    self.model_tester.batch_size				,       dtype=torch.long				,       device=UpperCamelCase__		)
									elif model_class in [
									    *get_values(UpperCamelCase__		),
									]:
													A__  :	int         =							torch.zeros(
													    self.model_tester.batch_size				,       dtype=torch.long				,       device=UpperCamelCase__		)
									elif model_class in [
									    *get_values(UpperCamelCase__		),
									]:
													A__  :	Optional[Any]         =							torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.text_seq_length)				,       dtype=torch.long				,       device=UpperCamelCase__				,       )

					return inputs_dict





	def   __snake_case				(			self		):
					self.config_tester.run_common_tests()





	def   __snake_case				(			self		):
					A__  :	str         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									A__  :	Optional[Any]         =							type
									self.model_tester.create_and_check_model(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__		)





	@slow
	def   __snake_case				(			self		):
					for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									A__  :	Any         =							LayoutLMvaModel.from_pretrained(UpperCamelCase__		)
									self.assertIsNotNone(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	int         =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image







@require_torch
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	@cached_property
	def   __snake_case				(			self		):
					return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__		) if is_vision_available() else None





	@slow
	def   __snake_case				(			self		):
					A__  :	Tuple         =							LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''		).to(UpperCamelCase__		)

					A__  :	Any         =							self.default_image_processor
					A__  :	Dict         =							prepare_img()
					A__  :	List[str]         =							image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''		).pixel_values.to(UpperCamelCase__		)

					A__  :	Optional[int]         =							torch.tensor([[1, 2]]		)
					A__  :	Optional[Any]         =							torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

					# forward pass
					A__  :	Dict         =							model(
					    input_ids=input_ids.to(UpperCamelCase__		)				,       bbox=bbox.to(UpperCamelCase__		)				,       pixel_values=pixel_values.to(UpperCamelCase__		)				,       )

					# verify the logits
					A__  :	Union[str, Any]         =							torch.Size((1, 199, 768)		)
					self.assertEqual(outputs.last_hidden_state.shape				,       UpperCamelCase__		)

					A__  :	Tuple         =							torch.tensor(
					    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]		).to(UpperCamelCase__		)

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

import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						int=False       ) ->       Tuple:

				"""simple docstring"""







				try:
								A__  :	Dict         =							os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								A__  :	Tuple         =							default
				else:
								# KEY is set, convert it to True or False.
								try:
												A__  :	Union[str, Any]         =							strtobool(__UpperCamelCase       )
								except ValueError:
												# More values are supported, but let's keep the message simple.
												raise ValueError(F"If set, {key} must be yes or no."       )
				return _value


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parse_flag_from_env('RUN_SLOW', default=False)

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

				"""simple docstring"""







				return unittest.skip('''Test was skipped'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       Union[str, Any]:

				"""simple docstring"""







				return unittest.skipUnless(_run_slow_tests							,						'''test is slow'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str       ) ->       int:

				"""simple docstring"""







				return unittest.skipUnless(not torch.cuda.is_available()							,						'''test requires only a CPU'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ) ->       Tuple:

				"""simple docstring"""







				return unittest.skipUnless(torch.cuda.is_available()							,						'''test requires a GPU'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict       ) ->       List[str]:

				"""simple docstring"""







				return unittest.skipUnless(is_xpu_available()							,						'''test requires a XPU'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict       ) ->       Any:

				"""simple docstring"""







				return unittest.skipUnless(is_mps_available()							,						'''test requires a `mps` backend support in `torch`'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Optional[Any]:

				"""simple docstring"""







				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()							,						'''test requires the Hugging Face suite'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       Tuple:

				"""simple docstring"""







				return unittest.skipUnless(is_bnb_available()							,						'''test requires the bitsandbytes library'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       List[Any]:

				"""simple docstring"""







				return unittest.skipUnless(is_tpu_available()							,						'''test requires TPU'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Tuple:

				"""simple docstring"""







				return unittest.skipUnless(torch.cuda.device_count() == 1							,						'''test requires a GPU'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Dict:

				"""simple docstring"""







				return unittest.skipUnless(torch.xpu.device_count() == 1							,						'''test requires a XPU'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any       ) ->       str:

				"""simple docstring"""







				return unittest.skipUnless(torch.cuda.device_count() > 1							,						'''test requires multiple GPUs'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Any:

				"""simple docstring"""







				return unittest.skipUnless(torch.xpu.device_count() > 1							,						'''test requires multiple XPUs'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       int:

				"""simple docstring"""







				return unittest.skipUnless(is_safetensors_available()							,						'''test requires safetensors'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Optional[Any]:

				"""simple docstring"""







				return unittest.skipUnless(is_deepspeed_available()							,						'''test requires DeepSpeed'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any       ) ->       List[Any]:

				"""simple docstring"""







				return unittest.skipUnless(is_torch_version('''>='''							,						'''1.12.0'''       )							,						'''test requires torch version >= 1.12.0'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]=None							,						__UpperCamelCase :						List[Any]=None       ) ->       Optional[Any]:

				"""simple docstring"""







				if test_case is None:
								return partial(__UpperCamelCase							,						version=__UpperCamelCase       )
				return unittest.skipUnless(is_torch_version('''>='''							,						__UpperCamelCase       )							,						F"test requires torch version >= {version}"       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ) ->       Optional[int]:

				"""simple docstring"""







				return unittest.skipUnless(is_tensorboard_available()							,						'''test requires Tensorboard'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any       ) ->       Tuple:

				"""simple docstring"""







				return unittest.skipUnless(is_wandb_available()							,						'''test requires wandb'''       )(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       Any:

				"""simple docstring"""







				return unittest.skipUnless(is_comet_ml_available()							,						'''test requires comet_ml'''       )(__UpperCamelCase       )


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]       ) ->       Optional[Any]:

				"""simple docstring"""







				return unittest.skipUnless(
				    _atleast_one_tracker_available							,						'''test requires at least one tracker to be available and for `comet_ml` to not be installed'''							,						)(__UpperCamelCase       )







class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				True





	@classmethod
	def   __snake_case				(			cls		):
					A__  :	Tuple         =							tempfile.mkdtemp()





	@classmethod
	def   __snake_case				(			cls		):
					if os.path.exists(cls.tmpdir		):
									shutil.rmtree(cls.tmpdir		)





	def   __snake_case				(			self		):
					if self.clear_on_setup:
									for path in Path(self.tmpdir		).glob('''**/*'''		):
													if path.is_file():
																	path.unlink()
													elif path.is_dir():
																	shutil.rmtree(UpperCamelCase__		)







class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					super().tearDown()
					# Reset the state of the AcceleratorState singleton.
					AcceleratorState._reset_state()
					PartialState._reset_state()







class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	Tuple         =							mocks if isinstance(UpperCamelCase__				,       (tuple, list)		) else [mocks]
					for m in self.mocks:
									m.start()
									self.addCleanup(m.stop		)




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str       ) ->       Any:

				"""simple docstring"""







				A__  :	int         =							AcceleratorState()
				A__  :	Any         =							tensor[None].clone().to(state.device       )
				A__  :	Optional[int]         =							gather(__UpperCamelCase       ).cpu()
				A__  :	Any         =							tensor[0].cpu()
				for i in range(tensors.shape[0]       ):
								if not torch.equal(tensors[i]							,						__UpperCamelCase       ):
												return False
				return True







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	List[Any]         =							returncode
					A__  :	Union[str, Any]         =							stdout
					A__  :	Dict         =							stderr




async def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						Optional[Any]       ) ->       Any:

				"""simple docstring"""







				while True:
								A__  :	Tuple         =							await stream.readline()
								if line:
												callback(__UpperCamelCase       )
								else:
												break

async def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Optional[Any]=None							,						__UpperCamelCase :						List[Any]=None							,						__UpperCamelCase :						Tuple=None							,						__UpperCamelCase :						Tuple=False							,						__UpperCamelCase :						List[Any]=False       ) ->       _RunOutput:

				"""simple docstring"""







				if echo:
								print('''\nRunning: '''							,						''' '''.join(__UpperCamelCase       )       )

				A__  :	int         =							await asyncio.create_subprocess_exec(
				    cmd[0]							,						*cmd[1:]							,						stdin=__UpperCamelCase							,						stdout=asyncio.subprocess.PIPE							,						stderr=asyncio.subprocess.PIPE							,						env=__UpperCamelCase							,						)

				# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
				# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
				#
				# If it starts hanging, will need to switch to the following code. The problem is that no data
				# will be seen until it's done and if it hangs for example there will be no debug info.
				# out, err = await p.communicate()
				# return _RunOutput(p.returncode, out, err)

				A__  :	List[Any]         =							[]
				A__  :	str         =							[]

				def tee(__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Union[str, Any]							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						List[Any]=""       ):
								A__  :	Optional[Any]         =							line.decode('''utf-8'''       ).rstrip()
								sink.append(__UpperCamelCase       )
								if not quiet:
												print(__UpperCamelCase							,						__UpperCamelCase							,						file=__UpperCamelCase       )

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        asyncio.create_task(_read_stream(p.stdout							,						lambda __UpperCamelCase       : tee(__UpperCamelCase							,						__UpperCamelCase							,						sys.stdout							,						label='''stdout:'''       )       )       ),
				        asyncio.create_task(_read_stream(p.stderr							,						lambda __UpperCamelCase       : tee(__UpperCamelCase							,						__UpperCamelCase							,						sys.stderr							,						label='''stderr:'''       )       )       ),
				    ]							,						timeout=__UpperCamelCase							,						)
				return _RunOutput(await p.wait()							,						__UpperCamelCase							,						__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						Any=None							,						__UpperCamelCase :						List[Any]=None							,						__UpperCamelCase :						List[str]=1_80							,						__UpperCamelCase :						List[str]=False							,						__UpperCamelCase :						Dict=True       ) ->       _RunOutput:

				"""simple docstring"""







				A__  :	Dict         =							asyncio.get_event_loop()
				A__  :	Optional[Any]         =							loop.run_until_complete(
				    _stream_subprocess(__UpperCamelCase							,						env=__UpperCamelCase							,						stdin=__UpperCamelCase							,						timeout=__UpperCamelCase							,						quiet=__UpperCamelCase							,						echo=__UpperCamelCase       )       )

				A__  :	Union[str, Any]         =							''' '''.join(__UpperCamelCase       )
				if result.returncode > 0:
								A__  :	Optional[Any]         =							'''\n'''.join(result.stderr       )
								raise RuntimeError(
								    F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
								    F"The combined stderr from workers follows:\n{stderr}"       )

				return result







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	pass




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						List[Any]=False       ) ->       Dict:

				"""simple docstring"""







				try:
								A__  :	List[Any]         =							subprocess.check_output(__UpperCamelCase							,						stderr=subprocess.STDOUT       )
								if return_stdout:
												if hasattr(__UpperCamelCase							,						'''decode'''       ):
																A__  :	Any         =							output.decode('''utf-8'''       )
												return output
				except subprocess.CalledProcessError as e:
								raise SubprocessCallException(
								    F"Command `{' '.join(__UpperCamelCase       )}` failed with the following error:\n\n{e.output.decode()}"       ) from e