
"""simple docstring"""

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 __lowerCAmelCase	:





     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=2			,  __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			,  ):

      '''simple docstring'''




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

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

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

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

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

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

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

      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMvaModel(config=lowercase_   )

      # text + image
      __UpperCamelCase       						= model(lowercase_			,  pixel_values=lowercase_			,  training=lowercase_   )
      __UpperCamelCase       						= model(
          lowercase_			,  bbox=lowercase_			,  pixel_values=lowercase_			,  attention_mask=lowercase_			,  token_type_ids=lowercase_			,  training=lowercase_			,  )
      __UpperCamelCase       						= model(lowercase_			,  bbox=lowercase_			,  pixel_values=lowercase_			,  training=lowercase_   )

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

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

      # image only
      __UpperCamelCase       						= model({'pixel_values': pixel_values}			,  training=lowercase_   )
      self.parent.assertEqual(
          result.last_hidden_state.shape			,  (self.batch_size, self.image_seq_length, self.hidden_size)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= 2
      __UpperCamelCase       						= TFLayoutLMvaForQuestionAnswering(config=lowercase_   )
      __UpperCamelCase       						= model(
          lowercase_			,  bbox=lowercase_			,  pixel_values=lowercase_			,  attention_mask=lowercase_			,  token_type_ids=lowercase_			,  start_positions=lowercase_			,  end_positions=lowercase_			,  training=lowercase_			,  )
      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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.prepare_config_and_inputs()
      ((__UpperCamelCase)						,			(__UpperCamelCase)						,			(__UpperCamelCase)						,			(__UpperCamelCase)						,			(__UpperCamelCase)						,			(__UpperCamelCase)						,			(__UpperCamelCase)						,			(__UpperCamelCase))       						= config_and_inputs
      __UpperCamelCase       						= {
          '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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(
         (
             TFLayoutLMvaModel,
             TFLayoutLMvaForQuestionAnswering,
             TFLayoutLMvaForSequenceClassification,
             TFLayoutLMvaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     lowercase		     =		(
         {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
         if is_tf_available()
         else {}
     )

     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return True





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase=False   ):

      '''simple docstring'''




      __UpperCamelCase       						= copy.deepcopy(lowercase_   )

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

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

      return inputs_dict





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMvaModelTester(self   )
      __UpperCamelCase       						= ConfigTester(self			,  config_class=lowercase_			,  hidden_size=37   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

        # Test that model correctly compute the loss with kwargs
        __UpperCamelCase       						= self._prepare_for_class(inputs_dict.copy()			,  lowercase_			,  return_labels=lowercase_   )
        __UpperCamelCase       						= prepared_for_class.pop('input_ids'   )

        __UpperCamelCase       						= model(lowercase_			,  **lowercase_   )[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
        __UpperCamelCase       						= self._prepare_for_class(inputs_dict.copy()			,  lowercase_			,  return_labels=lowercase_   )
        __UpperCamelCase       						= prepared_for_class.pop('input_ids'   )
        if "labels" in prepared_for_class:
         __UpperCamelCase       						= prepared_for_class['labels'].numpy()
         if len(labels.shape   ) > 1 and labels.shape[1] != 1:
          __UpperCamelCase       						= -100
          __UpperCamelCase       						= tf.convert_to_tensor(lowercase_   )
          __UpperCamelCase       						= model(lowercase_			,  **lowercase_   )[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
        __UpperCamelCase       						= self._prepare_for_class(inputs_dict.copy()			,  lowercase_			,  return_labels=lowercase_   )
        __UpperCamelCase       						= model(lowercase_   )[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
        __UpperCamelCase       						= self._prepare_for_class(inputs_dict.copy()			,  lowercase_			,  return_labels=lowercase_   )

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

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

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

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

        __UpperCamelCase       						= tuple(lowercase_   )

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

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      (
          (
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,				
      )       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      (
          (
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,				
      )       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(
          lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      (
          (
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,				
      )       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(
          lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      (
          (
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,			(
          __UpperCamelCase	
      )						,				
      )       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(
          lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_			,  lowercase_   )







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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






def 							A		(       )   ->			Any:
 __UpperCamelCase       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image





@require_tf
class __lowerCAmelCase	(   unittest.TestCase    ):





     @cached_property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'   )

      __UpperCamelCase       						= self.default_image_processor
      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=lowercase_			,  return_tensors='tf'   ).pixel_values

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

      # forward pass
      __UpperCamelCase       						= model(input_ids=lowercase_			,  bbox=lowercase_			,  pixel_values=lowercase_			,  training=lowercase_   )

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

      __UpperCamelCase       						= 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]			,  lowercase_			,  atol=1E-4   )   )





"""simple docstring"""

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							A		(       )   ->			List[Any]:
 __UpperCamelCase       						= ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )	)

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores'       ,      type=snake_case       ,      default=1       ,      help='Number of TPU cores to use (1 or 8).'	)

 # positional
 parser.add_argument(
     'training_script'       ,      type=snake_case       ,      help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     )       ,      )

 # rest from the training program
 parser.add_argument('training_script_args'       ,      nargs=snake_case	)

 return parser.parse_args()






def 							A		(       )   ->			Optional[int]:
 __UpperCamelCase       						= parse_args()

 # Import training_script as a module.
 __UpperCamelCase       						= Path(args.training_script	)
 sys.path.append(str(script_fpath.parent.resolve()	)	)
 __UpperCamelCase       						= script_fpath.stem
 __UpperCamelCase       						= importlib.import_module(snake_case	)

 # Patch sys.argv
 __UpperCamelCase       						= [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores	)]

 xmp.spawn(mod._mp_fn       ,      args=()       ,      nprocs=args.num_cores	)


if __name__ == "__main__":
      main()



