"""simple docstring"""





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			lowerCAmelCase_     =	'''platform'''
			import jax
			import jax.numpy as jnp
			import numpy as np

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class  _snake_case		:







				"""simple docstring"""






				a			=						PegasusConfig
				a			=						{}
				a			=						"gelu"





				def __init__(      self							:							List[str]      ,    _A							:							str      ,    _A							:							str=1_3      ,    _A							:							Dict=7      ,    _A							:							Tuple=True      ,    _A							:							Tuple=False      ,    _A							:							Any=9_9      ,    _A							:							Union[str, Any]=3_2      ,    _A							:							Dict=5      ,    _A							:							List[str]=4      ,    _A							:							Union[str, Any]=3_7      ,    _A							:							int=0.1      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Any=2_0      ,    _A							:							List[str]=2      ,    _A							:							Optional[int]=1      ,    _A							:							Optional[Any]=0      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    parent
											_SCREAMING_SNAKE_CASE		:						int			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    seq_length
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    is_training
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						Dict			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    intermediate_size

											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						int			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    eos_token_id
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    pad_token_id
											_SCREAMING_SNAKE_CASE		:						Any			  =    bos_token_id





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor([self.batch_size, self.seq_length - 1]      ,    self.vocab_size).clip(3      ,    self.vocab_size)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.expand_dims(np.array([self.eos_token_id] * self.batch_size)      ,    1)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.concatenate([input_ids, eos_tensor]      ,    axis=1)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.config_cls(
											    vocab_size=self.vocab_size      ,    d_model=self.hidden_size      ,    encoder_layers=self.num_hidden_layers      ,    decoder_layers=self.num_hidden_layers      ,    encoder_attention_heads=self.num_attention_heads      ,    decoder_attention_heads=self.num_attention_heads      ,    encoder_ffn_dim=self.intermediate_size      ,    decoder_ffn_dim=self.intermediate_size      ,    dropout=self.hidden_dropout_prob      ,    attention_dropout=self.attention_probs_dropout_prob      ,    max_position_embeddings=self.max_position_embeddings      ,    eos_token_ids=[2]      ,    bos_token_id=self.bos_token_id      ,    pad_token_id=self.pad_token_id      ,    decoder_start_token_id=self.pad_token_id      ,    **self.config_updates      ,    )
											_SCREAMING_SNAKE_CASE		:						Any			  =    prepare_pegasus_inputs_dict(_A      ,    _A      ,    _A)
											return config, inputs_dict





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							Optional[int]      ,    _A							:							int      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_0
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model_class_name(_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.encode(inputs_dict["""input_ids"""])

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    (
											    inputs_dict["""decoder_input_ids"""],
											    inputs_dict["""decoder_attention_mask"""],
											)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    model.init_cache(decoder_input_ids.shape[0]      ,    _A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,    dtype="""i4""")

											_SCREAMING_SNAKE_CASE		:						str			  =    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,    )
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model.decode(
											    decoder_input_ids[:, :-1]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=_A      ,    decoder_position_ids=_A      ,    )

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,    dtype="""i4""")
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model.decode(
											    decoder_input_ids[:, -1:]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=outputs_cache.past_key_values      ,    decoder_position_ids=_A      ,    )

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model.decode(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
											self.parent.assertTrue(diff < 1e-3      ,    msg=f"""Max diff is {diff}""")





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							Optional[Any]      ,    _A							:							List[Any]      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_0
											_SCREAMING_SNAKE_CASE		:						int			  =    model_class_name(_A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    model.encode(inputs_dict["""input_ids"""])

											_SCREAMING_SNAKE_CASE		:						Any			  =    (
											    inputs_dict["""decoder_input_ids"""],
											    inputs_dict["""decoder_attention_mask"""],
											)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
											    ]      ,    axis=-1      ,    )

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.init_cache(decoder_input_ids.shape[0]      ,    _A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,    )

											_SCREAMING_SNAKE_CASE		:						int			  =    model.decode(
											    decoder_input_ids[:, :-1]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=_A      ,    decoder_position_ids=_A      ,    )
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,    dtype="""i4""")
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model.decode(
											    decoder_input_ids[:, -1:]      ,    _A      ,    past_key_values=outputs_cache.past_key_values      ,    decoder_attention_mask=_A      ,    decoder_position_ids=_A      ,    )

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model.decode(_A      ,    _A      ,    decoder_attention_mask=_A)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
											self.parent.assertTrue(diff < 1e-3      ,    msg=f"""Max diff is {diff}""")



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	)->    Tuple:
							if attention_mask is None:
														_SCREAMING_SNAKE_CASE		:						int			  =    np.not_equal(__SCREAMING_SNAKE_CASE     ,	config.pad_token_id  ).astype(np.inta  )
							if decoder_attention_mask is None:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape     ,	dtype=np.inta  ),
														        np.not_equal(decoder_input_ids[:, 1:]     ,	config.pad_token_id  ).astype(np.inta  ),
														    ]     ,	axis=-1     ,	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}




@require_flax
class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						(
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				a			=						(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				a			=						True
				a			=						False
				a			=						False
				a			=						False





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    FlaxPegasusModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Any			  =    ConfigTester(self      ,    config_class=_A)





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											self.config_tester.run_common_tests()





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(_A      ,    _A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(_A      ,    _A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self._prepare_for_class(_A      ,    _A)
																									_SCREAMING_SNAKE_CASE		:						str			  =    model_class(_A)

																									@jax.jit
																									def encode_jitted(_A							:							str      ,    _A							:							Tuple=None      ,    **_A							:							Any):
																																return model.encode(input_ids=_A      ,    attention_mask=_A)

																									with self.subTest("""JIT Enabled"""):
																																_SCREAMING_SNAKE_CASE		:						Any			  =    encode_jitted(**_A).to_tuple()

																									with self.subTest("""JIT Disabled"""):
																																with jax.disable_jit():
																																							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    encode_jitted(**_A).to_tuple()

																									self.assertEqual(len(_A)      ,    len(_A))
																									for jitted_output, output in zip(_A      ,    _A):
																																self.assertEqual(jitted_output.shape      ,    output.shape)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    model_class(_A)
																									_SCREAMING_SNAKE_CASE		:						str			  =    model.encode(inputs_dict["""input_ids"""]      ,    inputs_dict["""attention_mask"""])

																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    {
																									    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																									    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																									    """encoder_outputs""": encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(_A							:							Optional[Any]      ,    _A							:							Optional[int]      ,    _A							:							int):
																																return model.decode(
																																    decoder_input_ids=_A      ,    decoder_attention_mask=_A      ,    encoder_outputs=_A      ,    )

																									with self.subTest("""JIT Enabled"""):
																																_SCREAMING_SNAKE_CASE		:						Dict			  =    decode_jitted(**_A).to_tuple()

																									with self.subTest("""JIT Disabled"""):
																																with jax.disable_jit():
																																							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    decode_jitted(**_A).to_tuple()

																									self.assertEqual(len(_A)      ,    len(_A))
																									for jitted_output, output in zip(_A      ,    _A):
																																self.assertEqual(jitted_output.shape      ,    output.shape)





				@slow
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model_class_name.from_pretrained("""google/pegasus-large"""      ,    from_pt=_A)
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.ones((1, 1))
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(_A)
																		self.assertIsNotNone(_A)





				@slow
				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [
											    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
											    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
											]

											_SCREAMING_SNAKE_CASE		:						Any			  =    [
											    """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
											    """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
											]

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer(_A      ,    return_tensors="""np"""      ,    truncation=_A      ,    max_length=5_1_2      ,    padding=_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    model.generate(**_A      ,    num_beams=2).sequences
											_SCREAMING_SNAKE_CASE		:						int			  =    tokenizer.batch_decode(_A      ,    skip_special_tokens=_A)
											assert tgt_text == decoded



"""simple docstring"""





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														_SCREAMING_SNAKE_CASE		:						str			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasModel(config=__SCREAMING_SNAKE_CASE  )
							else:
														raise ValueError(F"""Task {task} not supported."""  )

							print(F"""Building PyTorch model from configuration: {config}"""  )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
							model.save_pretrained(__SCREAMING_SNAKE_CASE  )

							# Save tokenizer files
							print(F"""Save tokenizer files to {pytorch_dump_path}"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""     ,	model_max_length=512  )
							tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE  )

							print("""Used relative position embeddings:"""     ,	model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
			)
			parser.add_argument(
			    '''--reset_position_index_per_cell''',
			    default=False,
			    action='''store_true''',
			    help='''Whether to use relative position embeddings or not. Defaults to True.''',
			)
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--tapas_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			lowerCAmelCase_     =	parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.task,
			    args.reset_position_index_per_cell,
			    args.tf_checkpoint_path,
			    args.tapas_config_file,
			    args.pytorch_dump_path,
			)


