
import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import YolosForObjectDetection, YolosModel
					from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=13				,       UpperCamelCase__=[30, 30]				,       UpperCamelCase__=2				,       UpperCamelCase__=3				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=32				,       UpperCamelCase__=5				,       UpperCamelCase__=4				,       UpperCamelCase__=37				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=10				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=3				,       UpperCamelCase__=None				,       UpperCamelCase__=8				,       UpperCamelCase__=10				,       ):
					A__  :	Optional[int]         =							parent
					A__  :	List[Any]         =							batch_size
					A__  :	Dict         =							image_size
					A__  :	Any         =							patch_size
					A__  :	Dict         =							num_channels
					A__  :	List[Any]         =							is_training
					A__  :	int         =							use_labels
					A__  :	Any         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Optional[int]         =							num_attention_heads
					A__  :	Optional[Any]         =							intermediate_size
					A__  :	str         =							hidden_act
					A__  :	str         =							hidden_dropout_prob
					A__  :	Optional[int]         =							attention_probs_dropout_prob
					A__  :	Optional[int]         =							type_sequence_label_size
					A__  :	Any         =							initializer_range
					A__  :	Optional[int]         =							num_labels
					A__  :	Union[str, Any]         =							scope
					A__  :	Union[str, Any]         =							n_targets
					A__  :	Dict         =							num_detection_tokens
					# we set the expected sequence length (which is used in several tests)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
					A__  :	int         =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
					A__  :	List[str]         =							num_patches + 1 + self.num_detection_tokens





	def   __snake_case				(			self		):
					A__  :	Dict         =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

					A__  :	int         =							None
					if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									A__  :	Tuple         =							[]
									for i in range(self.batch_size		):
													A__  :	List[Any]         =							{}
													A__  :	Tuple         =							torch.randint(
													    high=self.num_labels				,       size=(self.n_targets,)				,       device=UpperCamelCase__		)
													A__  :	Any         =							torch.rand(self.n_targets				,       4				,       device=UpperCamelCase__		)
													labels.append(UpperCamelCase__		)

					A__  :	Union[str, Any]         =							self.get_config()

					return config, pixel_values, labels





	def   __snake_case				(			self		):
					return YolosConfig(
					    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				,       num_detection_tokens=self.num_detection_tokens				,       num_labels=self.num_labels				,       )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							YolosModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	Optional[Any]         =							model(UpperCamelCase__		)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,       (self.batch_size, self.expected_seq_len, self.hidden_size)		)





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

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__		)
					A__  :	Optional[int]         =							model(UpperCamelCase__		)

					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__				,       labels=UpperCamelCase__		)

					self.parent.assertEqual(result.loss.shape				,       ()		)
					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.prepare_config_and_inputs()
					A__       ,							A__       ,							A__  :	Optional[Any]         =							config_and_inputs
					A__  :	Optional[int]         =							{'''pixel_values''': pixel_values}
					return config, inputs_dict







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







	'''simple docstring'''
	_lowerCAmelCase							       =				(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	_lowerCAmelCase							       =				(
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__  :	Optional[int]         =							super()._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__				,       return_labels=UpperCamelCase__		)

					if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
													A__  :	str         =							[]
													for i in range(self.model_tester.batch_size		):
																	A__  :	int         =							{}
																	A__  :	Dict         =							torch.ones(
																	    size=(self.model_tester.n_targets,)				,       device=UpperCamelCase__				,       dtype=torch.long		)
																	A__  :	Dict         =							torch.ones(
																	    self.model_tester.n_targets				,       4				,       device=UpperCamelCase__				,       dtype=torch.float		)
																	labels.append(UpperCamelCase__		)
													A__  :	Dict         =							labels

					return inputs_dict





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							YolosModelTester(self		)
					A__  :	List[str]         =							ConfigTester(self				,       config_class=UpperCamelCase__				,       has_text_modality=UpperCamelCase__				,       hidden_size=37		)





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





	def   __snake_case				(			self		):
					# YOLOS does not use inputs_embeds
					pass





	def   __snake_case				(			self		):
					A__       ,							A__  :	Dict         =							self.model_tester.prepare_config_and_inputs_for_common()

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





	def   __snake_case				(			self		):
					A__       ,							A__  :	int         =							self.model_tester.prepare_config_and_inputs_for_common()

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

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





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





	def   __snake_case				(			self		):
					A__       ,							A__  :	List[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()
					A__  :	Tuple         =							True

					# in YOLOS, the seq_len is different
					A__  :	List[Any]         =							self.model_tester.expected_seq_len
					for model_class in self.all_model_classes:
									A__  :	Any         =							True
									A__  :	Optional[int]         =							False
									A__  :	Optional[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	List[str]         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Optional[int]         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									A__  :	Tuple         =							True
									A__  :	Optional[Any]         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Tuple         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )
									A__  :	List[Any]         =							len(UpperCamelCase__		)

									# Check attention is always last and order is fine
									A__  :	List[str]         =							True
									A__  :	List[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Tuple         =							1
									self.assertEqual(out_len + added_hidden_states				,       len(UpperCamelCase__		)		)

									A__  :	List[str]         =							outputs.attentions

									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )





	def   __snake_case				(			self		):
					def check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	str         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()

									with torch.no_grad():
													A__  :	int         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Optional[Any]         =							outputs.hidden_states

									A__  :	int         =							getattr(
									    self.model_tester				,       '''expected_num_hidden_layers'''				,       self.model_tester.num_hidden_layers + 1		)
									self.assertEqual(len(UpperCamelCase__		)				,       UpperCamelCase__		)

									# YOLOS has a different seq_length
									A__  :	Union[str, Any]         =							self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]		)				,       [seq_length, self.model_tester.hidden_size]				,       )

					A__       ,							A__  :	Optional[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	int         =							True
									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									A__  :	Optional[int]         =							True

									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)





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





	@slow
	def   __snake_case				(			self		):
					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									A__  :	Union[str, Any]         =							YolosModel.from_pretrained(UpperCamelCase__		)
									self.assertIsNotNone(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       List[str]:

				"""simple docstring"""







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







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







	'''simple docstring'''





	@cached_property
	def   __snake_case				(			self		):
					return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None





	@slow
	def   __snake_case				(			self		):
					A__  :	Tuple         =							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(UpperCamelCase__		)

					A__  :	str         =							self.default_image_processor
					A__  :	Tuple         =							prepare_img()
					A__  :	Tuple         =							image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''		).to(UpperCamelCase__		)

					# forward pass
					with torch.no_grad():
									A__  :	Any         =							model(inputs.pixel_values		)

					# verify outputs
					A__  :	List[Any]         =							torch.Size((1, 100, 92)		)
					self.assertEqual(outputs.logits.shape				,       UpperCamelCase__		)

					A__  :	Optional[int]         =							torch.tensor(
					    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]				,       device=UpperCamelCase__				,       )
					A__  :	Optional[int]         =							torch.tensor(
					    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]				,       device=UpperCamelCase__		)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)

					# verify postprocessing
					A__  :	Dict         =							image_processor.post_process_object_detection(
					    UpperCamelCase__				,       threshold=0.3				,       target_sizes=[image.size[::-1]]		)[0]
					A__  :	int         =							torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]		).to(UpperCamelCase__		)
					A__  :	str         =							[75, 75, 17, 63, 17]
					A__  :	Tuple         =							torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]		).to(UpperCamelCase__		)

					self.assertEqual(len(results['''scores''']		)				,       5		)
					self.assertTrue(torch.allclose(results['''scores''']				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertSequenceEqual(results['''labels'''].tolist()				,       UpperCamelCase__		)
					self.assertTrue(torch.allclose(results['''boxes'''][0, :]				,       UpperCamelCase__		)		)

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