





"""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  ()   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				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=_SCREAMING_SNAKE_CASE	,  default=1	,  help='Number of TPU cores to use (1 or 8).'     )

  # positional
  parser.add_argument(
      'training_script'	,  type=_SCREAMING_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=_SCREAMING_SNAKE_CASE     )

  return parser.parse_args()
def 					__A  ()   ->List[str]:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				parse_args()

  # Import training_script as a module.
  lowerCAmelCase__     :str											=				Path(args.training_script     )
  sys.path.append(str(script_fpath.parent.resolve()     )     )
  lowerCAmelCase__     :Any											=				script_fpath.stem
  lowerCAmelCase__     :Any											=				importlib.import_module(_SCREAMING_SNAKE_CASE     )

  # Patch sys.argv
  lowerCAmelCase__     :int											=				[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()



"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

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


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						)





class  lowerCamelCase    :

			def __init__(       self					:    str     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Union[str, Any]=1_3     ,					__UpperCAmelCase					:    Union[str, Any]=7     ,					__UpperCAmelCase					:    List[str]=True     ,					__UpperCAmelCase					:    Tuple=True     ,					__UpperCAmelCase					:    Any=True     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    Union[str, Any]=9_9     ,					__UpperCAmelCase					:    str=[1, 1, 2]     ,					__UpperCAmelCase					:    str=1     ,					__UpperCAmelCase					:    Dict=3_2     ,					__UpperCAmelCase					:    Optional[int]=4     ,					__UpperCAmelCase					:    str=8     ,					__UpperCAmelCase					:    List[Any]=3_7     ,					__UpperCAmelCase					:    Union[str, Any]="gelu_new"     ,					__UpperCAmelCase					:    List[Any]=0.1     ,					__UpperCAmelCase					:    str=0.1     ,					__UpperCAmelCase					:    Any=0.0     ,					__UpperCAmelCase					:    str=5_1_2     ,					__UpperCAmelCase					:    Optional[Any]=3     ,					__UpperCAmelCase					:    List[str]=0.02     ,					__UpperCAmelCase					:    Tuple=3     ,					__UpperCAmelCase					:    str=4     ,					__UpperCAmelCase					:    Optional[Any]=None     ,					__UpperCAmelCase					:    int=False     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   seq_length
						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__										=   block_sizes
						SCREAMING_SNAKE_CASE__										=   num_decoder_layers
						SCREAMING_SNAKE_CASE__										=   d_model
						SCREAMING_SNAKE_CASE__										=   n_head
						SCREAMING_SNAKE_CASE__										=   d_head
						SCREAMING_SNAKE_CASE__										=   d_inner
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout
						SCREAMING_SNAKE_CASE__										=   attention_dropout
						SCREAMING_SNAKE_CASE__										=   activation_dropout
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   2
						SCREAMING_SNAKE_CASE__										=   num_labels
						SCREAMING_SNAKE_CASE__										=   num_choices
						SCREAMING_SNAKE_CASE__										=   scope
						SCREAMING_SNAKE_CASE__										=   initializer_std

						# Used in the tests to check the size of the first attention layer
						SCREAMING_SNAKE_CASE__										=   n_head
						# Used in the tests to check the size of the first hidden state
						SCREAMING_SNAKE_CASE__										=   self.d_model
						# Used in the tests to check the number of output hidden states/attentions
						SCREAMING_SNAKE_CASE__										=   sum(self.block_sizes							) + (0 if base else self.num_decoder_layers)
						# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
						# the last hidden state of the first block (which is the first hidden state of the decoder).
						if not base:
									SCREAMING_SNAKE_CASE__										=   self.num_hidden_layers + 2

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size							)

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

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

						SCREAMING_SNAKE_CASE__										=   None
						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.seq_length]     ,					self.num_labels							)
									SCREAMING_SNAKE_CASE__										=   ids_tensor([self.batch_size]     ,					self.num_choices							)

						SCREAMING_SNAKE_CASE__										=   FunnelConfig(
						    vocab_size=self.vocab_size     ,					block_sizes=self.block_sizes     ,					num_decoder_layers=self.num_decoder_layers     ,					d_model=self.d_model     ,					n_head=self.n_head     ,					d_head=self.d_head     ,					d_inner=self.d_inner     ,					hidden_act=self.hidden_act     ,					hidden_dropout=self.hidden_dropout     ,					attention_dropout=self.attention_dropout     ,					activation_dropout=self.activation_dropout     ,					max_position_embeddings=self.max_position_embeddings     ,					type_vocab_size=self.type_vocab_size     ,					initializer_std=self.initializer_std     ,					)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    int     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   [input_ids, input_mask]
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.d_model)							)

						SCREAMING_SNAKE_CASE__										=   False
						SCREAMING_SNAKE_CASE__										=   TFFunnelModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.d_model)							)

						SCREAMING_SNAKE_CASE__										=   False
						SCREAMING_SNAKE_CASE__										=   TFFunnelModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.d_model)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    int     ,					) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   TFFunnelBaseModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   [input_ids, input_mask]
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, 2, self.d_model)							)

						SCREAMING_SNAKE_CASE__										=   False
						SCREAMING_SNAKE_CASE__										=   TFFunnelBaseModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, 3, self.d_model)							)

						SCREAMING_SNAKE_CASE__										=   False
						SCREAMING_SNAKE_CASE__										=   TFFunnelBaseModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, 2, self.d_model)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Optional[int]     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelForPreTraining(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    List[Any]     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelForMaskedLM(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Tuple     ,					) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.num_labels
						SCREAMING_SNAKE_CASE__										=   TFFunnelForSequenceClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    int     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   self.num_choices
						SCREAMING_SNAKE_CASE__										=   TFFunnelForMultipleChoice(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1							)     ,					(1, self.num_choices, 1)							)
						SCREAMING_SNAKE_CASE__										=   tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1							)     ,					(1, self.num_choices, 1)							)
						SCREAMING_SNAKE_CASE__										=   tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1							)     ,					(1, self.num_choices, 1)							)
						SCREAMING_SNAKE_CASE__										=   {
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						    """token_type_ids""": multiple_choice_token_type_ids,
						}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    str     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.num_labels
						SCREAMING_SNAKE_CASE__										=   TFFunnelForTokenClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.num_labels)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    int     ,					) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   TFFunnelForQuestionAnswering(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__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        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		
						)										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict





@require_tf
class  lowerCamelCase    (A__      ,A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		int       		=				(
			    (
			        TFFunnelModel,
			        TFFunnelForMaskedLM,
			        TFFunnelForPreTraining,
			        TFFunnelForQuestionAnswering,
			        TFFunnelForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__ :		Tuple       		=				(
			    {
			        'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
			        'fill-mask': TFFunnelForMaskedLM,
			        'question-answering': TFFunnelForQuestionAnswering,
			        'text-classification': TFFunnelForSequenceClassification,
			        'token-classification': TFFunnelForTokenClassification,
			        'zero-shot': TFFunnelForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__ :		str       		=				False
			lowerCamelCase__ :		Any       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     str:
						self.config_tester.run_common_tests()

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase							)





@require_tf
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[Any]       		=				(
			    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
			)
			lowerCamelCase__ :		Dict       		=				False
			lowerCamelCase__ :		Tuple       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelModelTester(self     ,					base=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Union[str, Any]:
						self.config_tester.run_common_tests()

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_base_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase							)


