





from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
     TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     LayoutLMvaConfig,
     TFLayoutLMvaForQuestionAnswering,
     TFLayoutLMvaForSequenceClassification,
     TFLayoutLMvaForTokenClassification,
     TFLayoutLMvaModel,
 )

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor




class   UpperCamelCase_       :


       def __init__(							self    :int							,				__A    :int							,				__A    :Any=2							,				__A    :Union[str, Any]=3							,				__A    :List[Any]=4							,				__A    :Tuple=2							,				__A    :Union[str, Any]=7							,				__A    :List[Any]=True							,				__A    :int=True							,				__A    :Any=True							,				__A    :Dict=True							,				__A    :Optional[Any]=99							,				__A    :int=36							,				__A    :List[Any]=2							,				__A    :List[str]=4							,				__A    :int=37							,				__A    :Union[str, Any]="gelu"							,				__A    :Dict=0.1							,				__A    :int=0.1							,				__A    :Optional[int]=512							,				__A    :int=16							,				__A    :List[Any]=2							,				__A    :List[str]=0.0_2							,				__A    :Optional[int]=6							,				__A    :str=6							,				__A    :str=3							,				__A    :Any=4							,				__A    :int=None							,				__A    :Optional[int]=1000							,				)   ->   int:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      parent
         SCREAMING_SNAKE_CASE__			=      batch_size
         SCREAMING_SNAKE_CASE__			=      num_channels
         SCREAMING_SNAKE_CASE__			=      image_size
         SCREAMING_SNAKE_CASE__			=      patch_size
         SCREAMING_SNAKE_CASE__			=      is_training
         SCREAMING_SNAKE_CASE__			=      use_input_mask
         SCREAMING_SNAKE_CASE__			=      use_token_type_ids
         SCREAMING_SNAKE_CASE__			=      use_labels
         SCREAMING_SNAKE_CASE__			=      vocab_size
         SCREAMING_SNAKE_CASE__			=      hidden_size
         SCREAMING_SNAKE_CASE__			=      num_hidden_layers
         SCREAMING_SNAKE_CASE__			=      num_attention_heads
         SCREAMING_SNAKE_CASE__			=      intermediate_size
         SCREAMING_SNAKE_CASE__			=      hidden_act
         SCREAMING_SNAKE_CASE__			=      hidden_dropout_prob
         SCREAMING_SNAKE_CASE__			=      attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__			=      max_position_embeddings
         SCREAMING_SNAKE_CASE__			=      type_vocab_size
         SCREAMING_SNAKE_CASE__			=      type_sequence_label_size
         SCREAMING_SNAKE_CASE__			=      initializer_range
         SCREAMING_SNAKE_CASE__			=      coordinate_size
         SCREAMING_SNAKE_CASE__			=      shape_size
         SCREAMING_SNAKE_CASE__			=      num_labels
         SCREAMING_SNAKE_CASE__			=      num_choices
         SCREAMING_SNAKE_CASE__			=      scope
         SCREAMING_SNAKE_CASE__			=      range_bbox

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


       def      _snake_case  (							self    :int		)   ->   Union[str, Any]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      ids_tensor([self.batch_size, self.text_seq_length]							,				self.vocab_size		)

         SCREAMING_SNAKE_CASE__			=      ids_tensor([self.batch_size, self.text_seq_length, 4]							,				self.range_bbox		)
         SCREAMING_SNAKE_CASE__			=      bbox.numpy()
         # 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]:
               SCREAMING_SNAKE_CASE__			=      bbox[i, j, 3]
               SCREAMING_SNAKE_CASE__			=      bbox[i, j, 1]
               SCREAMING_SNAKE_CASE__			=      tmp_coordinate
             if bbox[i, j, 2] < bbox[i, j, 0]:
               SCREAMING_SNAKE_CASE__			=      bbox[i, j, 2]
               SCREAMING_SNAKE_CASE__			=      bbox[i, j, 0]
               SCREAMING_SNAKE_CASE__			=      tmp_coordinate
         SCREAMING_SNAKE_CASE__			=      tf.constant(__A		)

         SCREAMING_SNAKE_CASE__			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         SCREAMING_SNAKE_CASE__			=      None
         if self.use_input_mask:
           SCREAMING_SNAKE_CASE__			=      random_attention_mask([self.batch_size, self.text_seq_length]		)

         SCREAMING_SNAKE_CASE__			=      None
         if self.use_token_type_ids:
           SCREAMING_SNAKE_CASE__			=      ids_tensor([self.batch_size, self.text_seq_length]							,				self.type_vocab_size		)

         SCREAMING_SNAKE_CASE__			=      None
         SCREAMING_SNAKE_CASE__			=      None
         if self.use_labels:
           SCREAMING_SNAKE_CASE__			=      ids_tensor([self.batch_size]							,				self.type_sequence_label_size		)
           SCREAMING_SNAKE_CASE__			=      ids_tensor([self.batch_size, self.text_seq_length]							,				self.num_labels		)

         SCREAMING_SNAKE_CASE__			=      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    :Any							,				__A    :Union[str, Any]							,				__A    :Optional[int]							,				__A    :Any							,				__A    :Optional[int]							,				__A    :Any							,				__A    :Union[str, Any]		)   ->   str:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      TFLayoutLMvaModel(config=__A		)

         # text + image
         SCREAMING_SNAKE_CASE__			=      model(__A							,				pixel_values=__A							,				training=__A		)
         SCREAMING_SNAKE_CASE__			=      model(
             __A							,				bbox=__A							,				pixel_values=__A							,				attention_mask=__A							,				token_type_ids=__A							,				training=__A							,				)
         SCREAMING_SNAKE_CASE__			=      model(__A							,				bbox=__A							,				pixel_values=__A							,				training=__A		)

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

         # text only
         SCREAMING_SNAKE_CASE__			=      model(__A							,				training=__A		)
         self.parent.assertEqual(
             result.last_hidden_state.shape							,				(self.batch_size, self.text_seq_length, self.hidden_size)		)

         # image only
         SCREAMING_SNAKE_CASE__			=      model({"""pixel_values""": pixel_values}							,				training=__A		)
         self.parent.assertEqual(
             result.last_hidden_state.shape							,				(self.batch_size, self.image_seq_length, self.hidden_size)		)


       def      _snake_case  (							self    :Optional[int]							,				__A    :str							,				__A    :Optional[int]							,				__A    :int							,				__A    :Union[str, Any]							,				__A    :str							,				__A    :Dict							,				__A    :Tuple		)   ->   Optional[int]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.num_labels
         SCREAMING_SNAKE_CASE__			=      TFLayoutLMvaForSequenceClassification(config=__A		)
         SCREAMING_SNAKE_CASE__			=      model(
             __A							,				bbox=__A							,				pixel_values=__A							,				attention_mask=__A							,				token_type_ids=__A							,				labels=__A							,				training=__A							,				)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)		)


       def      _snake_case  (							self    :List[Any]							,				__A    :Tuple							,				__A    :List[Any]							,				__A    :List[str]							,				__A    :Union[str, Any]							,				__A    :Any							,				__A    :Union[str, Any]							,				__A    :str		)   ->   Any:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.num_labels
         SCREAMING_SNAKE_CASE__			=      TFLayoutLMvaForTokenClassification(config=__A		)
         SCREAMING_SNAKE_CASE__			=      model(
             __A							,				bbox=__A							,				pixel_values=__A							,				attention_mask=__A							,				token_type_ids=__A							,				labels=__A							,				training=__A							,				)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.text_seq_length, self.num_labels)		)


       def      _snake_case  (							self    :Any							,				__A    :Tuple							,				__A    :Any							,				__A    :int							,				__A    :Any							,				__A    :List[Any]							,				__A    :Dict							,				__A    :Optional[Any]		)   ->   Tuple:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      2
         SCREAMING_SNAKE_CASE__			=      TFLayoutLMvaForQuestionAnswering(config=__A		)
         SCREAMING_SNAKE_CASE__			=      model(
             __A							,				bbox=__A							,				pixel_values=__A							,				attention_mask=__A							,				token_type_ids=__A							,				start_positions=__A							,				end_positions=__A							,				training=__A							,				)
         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    :str		)   ->   List[Any]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.prepare_config_and_inputs()
         (SCREAMING_SNAKE_CASE__)			=      config_and_inputs
         SCREAMING_SNAKE_CASE__			=      {
             """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_tf
class   UpperCamelCase_       (     UpperCAmelCase__				,  UpperCAmelCase__				,  unittest.TestCase    ):
       lowerCamelCase_           =					(
           (
               TFLayoutLMvaModel,
               TFLayoutLMvaForQuestionAnswering,
               TFLayoutLMvaForSequenceClassification,
               TFLayoutLMvaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       lowerCamelCase_           =					(
           {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
           if is_tf_available()
           else {}
       )

       lowerCamelCase_           =					False
       lowerCamelCase_           =					False
       lowerCamelCase_           =					False


       def      _snake_case  (							self    :List[Any]							,				__A    :str							,				__A    :Union[str, Any]							,				__A    :Union[str, Any]							,				__A    :Union[str, Any]							,				__A    :Any		)   ->   List[Any]:



         """simple docstring"""



         return True


       def      _snake_case  (							self    :Tuple							,				__A    :int							,				__A    :Union[str, Any]							,				__A    :Optional[int]=False		)   ->   dict:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      copy.deepcopy(__A		)

         if model_class in get_values(__A		):
           SCREAMING_SNAKE_CASE__			=      {
               k: tf.tile(tf.expand_dims(__A							,				1		)							,				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
               if isinstance(__A							,				tf.Tensor		) and v.ndim > 0
               else v
               for k, v in inputs_dict.items()
           }

         if return_labels:
           if model_class in get_values(__A		):
             SCREAMING_SNAKE_CASE__			=      tf.ones(self.model_tester.batch_size							,				dtype=tf.intaa		)
           elif model_class in get_values(__A		):
             SCREAMING_SNAKE_CASE__			=      tf.zeros(self.model_tester.batch_size							,				dtype=tf.intaa		)
             SCREAMING_SNAKE_CASE__			=      tf.zeros(self.model_tester.batch_size							,				dtype=tf.intaa		)
           elif model_class in get_values(__A		):
             SCREAMING_SNAKE_CASE__			=      tf.zeros(self.model_tester.batch_size							,				dtype=tf.intaa		)
           elif model_class in get_values(__A		):
             SCREAMING_SNAKE_CASE__			=      tf.zeros(
                 (self.model_tester.batch_size, self.model_tester.text_seq_length)							,				dtype=tf.intaa		)

         return inputs_dict


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



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      TFLayoutLMvaModelTester(self		)
         SCREAMING_SNAKE_CASE__			=      ConfigTester(self							,				config_class=__A							,				hidden_size=37		)


       def      _snake_case  (							self    :Dict		)   ->   str:



         """simple docstring"""



         self.config_tester.run_common_tests()


       def      _snake_case  (							self    :Optional[Any]		)   ->   Union[str, Any]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE__			=      model_class(__A		)
           if getattr(__A							,				"""hf_compute_loss"""							,				__A		):
             # The number of elements in the loss should be the same as the number of elements in the label
             SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(inputs_dict.copy()							,				__A							,				return_labels=__A		)
             SCREAMING_SNAKE_CASE__			=      prepared_for_class[
                 sorted(prepared_for_class.keys() - inputs_dict.keys()							,				reverse=__A		)[0]
             ]
             SCREAMING_SNAKE_CASE__			=      added_label.shape.as_list()[:1]

             # Test that model correctly compute the loss with kwargs
             SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(inputs_dict.copy()							,				__A							,				return_labels=__A		)
             SCREAMING_SNAKE_CASE__			=      prepared_for_class.pop("""input_ids"""		)

             SCREAMING_SNAKE_CASE__			=      model(__A							,				**__A		)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

             # Test that model correctly compute the loss when we mask some positions
             SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(inputs_dict.copy()							,				__A							,				return_labels=__A		)
             SCREAMING_SNAKE_CASE__			=      prepared_for_class.pop("""input_ids"""		)
             if "labels" in prepared_for_class:
               SCREAMING_SNAKE_CASE__			=      prepared_for_class["""labels"""].numpy()
               if len(labels.shape		) > 1 and labels.shape[1] != 1:
                 SCREAMING_SNAKE_CASE__			=      -100
                 SCREAMING_SNAKE_CASE__			=      tf.convert_to_tensor(__A		)
                 SCREAMING_SNAKE_CASE__			=      model(__A							,				**__A		)[0]
                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
                 self.assertTrue(not np.any(np.isnan(loss.numpy()		)		)		)

                 # Test that model correctly compute the loss with a dict
             SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(inputs_dict.copy()							,				__A							,				return_labels=__A		)
             SCREAMING_SNAKE_CASE__			=      model(__A		)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

             # Test that model correctly compute the loss with a tuple
             SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(inputs_dict.copy()							,				__A							,				return_labels=__A		)

             # Get keys that were added with the _prepare_for_class function
             SCREAMING_SNAKE_CASE__			=      prepared_for_class.keys() - inputs_dict.keys()
             SCREAMING_SNAKE_CASE__			=      inspect.signature(model.call		).parameters
             SCREAMING_SNAKE_CASE__			=      list(signature.keys()		)

             # Create a dictionary holding the location of the tensors in the tuple
             SCREAMING_SNAKE_CASE__			=      {0: """input_ids"""}
             for label_key in label_keys:
               SCREAMING_SNAKE_CASE__			=      signature_names.index(__A		)
               SCREAMING_SNAKE_CASE__			=      label_key
             SCREAMING_SNAKE_CASE__			=      sorted(tuple_index_mapping.items()		)
             # Initialize a list with their default values, update the values and convert to a tuple
             SCREAMING_SNAKE_CASE__			=      []

             for name in signature_names:
               if name != "kwargs":
                 list_input.append(signature[name].default		)

             for index, value in sorted_tuple_index_mapping:
               SCREAMING_SNAKE_CASE__			=      prepared_for_class[value]

             SCREAMING_SNAKE_CASE__			=      tuple(__A		)

             # Send to model
             SCREAMING_SNAKE_CASE__			=      model(tuple_input[:-1]		)[0]

             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)


       def      _snake_case  (							self    :Union[str, Any]		)   ->   Union[str, Any]:



         """simple docstring"""



         (
             SCREAMING_SNAKE_CASE__		
         )			=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(__A							,				__A							,				__A							,				__A							,				__A							,				__A		)


       def      _snake_case  (							self    :Optional[int]		)   ->   Union[str, Any]:



         """simple docstring"""



         (
             SCREAMING_SNAKE_CASE__		
         )			=      self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
           SCREAMING_SNAKE_CASE__			=      type
           self.model_tester.create_and_check_model(__A							,				__A							,				__A							,				__A							,				__A							,				__A		)


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



         """simple docstring"""



         (
             SCREAMING_SNAKE_CASE__		
         )			=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(
             __A							,				__A							,				__A							,				__A							,				__A							,				__A							,				__A		)


       def      _snake_case  (							self    :Any		)   ->   List[Any]:



         """simple docstring"""



         (
             SCREAMING_SNAKE_CASE__		
         )			=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(
             __A							,				__A							,				__A							,				__A							,				__A							,				__A							,				__A		)


       def      _snake_case  (							self    :List[Any]		)   ->   Any:



         """simple docstring"""



         (
             SCREAMING_SNAKE_CASE__		
         )			=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(
             __A							,				__A							,				__A							,				__A							,				__A							,				__A							,				__A		)


       @slow
       def      _snake_case  (							self    :str		)   ->   Optional[int]:



         """simple docstring"""



         for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE__			=      TFLayoutLMvaModel.from_pretrained(__A		)
           self.assertIsNotNone(__A		)






def 				SCREAMING_SNAKE_CASE__							(       ):
  SCREAMING_SNAKE_CASE__			=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
  return image




@require_tf
class   UpperCamelCase_       (     unittest.TestCase    ):


       @cached_property
       def      _snake_case  (							self    :str		)   ->   Optional[int]:



         """simple docstring"""



         return LayoutLMvaImageProcessor(apply_ocr=__A		) if is_vision_available() else None


       @slow
       def      _snake_case  (							self    :Dict		)   ->   str:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""		)

         SCREAMING_SNAKE_CASE__			=      self.default_image_processor
         SCREAMING_SNAKE_CASE__			=      prepare_img()
         SCREAMING_SNAKE_CASE__			=      image_processor(images=__A							,				return_tensors="""tf"""		).pixel_values

         SCREAMING_SNAKE_CASE__			=      tf.constant([[1, 2]]		)
         SCREAMING_SNAKE_CASE__			=      tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]		)							,				axis=0		)

         # forward pass
         SCREAMING_SNAKE_CASE__			=      model(input_ids=__A							,				bbox=__A							,				pixel_values=__A							,				training=__A		)

         # verify the logits
         SCREAMING_SNAKE_CASE__			=      (1, 199, 768)
         self.assertEqual(outputs.last_hidden_state.shape							,				__A		)

         SCREAMING_SNAKE_CASE__			=      tf.constant(
             [[-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]]		)

         self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]							,				__A							,				atol=1E-4		)		)






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class   UpperCamelCase_       (     unittest.TestCase    ):


							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      inspect.getfile(accelerate.test_utils		)
									SCREAMING_SNAKE_CASE__			=      os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_script.py"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.sep.join(
									    mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_ops.py"""]		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
									print(f'''Command: {cmd}'''		)
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[int]		)   ->   str:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''		)
									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
									with patch_environment(omp_num_threads=1							,				cuda_visible_devices="""0,1"""		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


if __name__ == "__main__":
	_lowerCamelCase   				=			Accelerator()
	_lowerCamelCase   				=			(accelerator.state.process_index + 2, 10)
	_lowerCamelCase   				=			torch.randint(0, 10, shape).to(accelerator.device)

	_lowerCamelCase   				=			''

	_lowerCamelCase   				=			accelerator.pad_across_processes(tensor)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
		error_msg += "Padding was not done with the right value (0)."

	_lowerCamelCase   				=			accelerator.pad_across_processes(tensor, pad_first=True)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	_lowerCamelCase   				=			accelerator.state.num_processes - accelerator.state.process_index - 1
	if not torch.equal(tensora[index:], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[:index] == 0):
		error_msg += "Padding was not done with the right value (0)."

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)