






'''simple docstring'''

import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       BioGptForCausalLM,
       BioGptForSequenceClassification,
       BioGptForTokenClassification,
       BioGptModel,
       BioGptTokenizer,
   )
   from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : str	,    A__      : Dict	,    A__      : Any=13	,    A__      : Any=7	,    A__      : List[str]=True	,    A__      : Union[str, Any]=True	,    A__      : int=False	,    A__      : Optional[Any]=True	,    A__      : Optional[Any]=99	,    A__      : int=32	,    A__      : Optional[Any]=5	,    A__      : Dict=4	,    A__      : List[Any]=37	,    A__      : Any="gelu"	,    A__      : Any=0.1	,    A__      : Any=0.1	,    A__      : Optional[Any]=512	,    A__      : List[Any]=16	,    A__      : List[str]=2	,    A__      : List[str]=0.02	,    A__      : int=3	,    A__      : Dict=4	,    A__      : Dict=None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				parent
            __lowerCamelCase						:       str				   =				batch_size
            __lowerCamelCase						:       Tuple				   =				seq_length
            __lowerCamelCase						:       str				   =				is_training
            __lowerCamelCase						:       List[str]				   =				use_input_mask
            __lowerCamelCase						:       Tuple				   =				use_token_type_ids
            __lowerCamelCase						:       Optional[Any]				   =				use_labels
            __lowerCamelCase						:       List[Any]				   =				vocab_size
            __lowerCamelCase						:       str				   =				hidden_size
            __lowerCamelCase						:       List[str]				   =				num_hidden_layers
            __lowerCamelCase						:       Any				   =				num_attention_heads
            __lowerCamelCase						:       List[str]				   =				intermediate_size
            __lowerCamelCase						:       Union[str, Any]				   =				hidden_act
            __lowerCamelCase						:       Optional[int]				   =				hidden_dropout_prob
            __lowerCamelCase						:       Any				   =				attention_probs_dropout_prob
            __lowerCamelCase						:       List[Any]				   =				max_position_embeddings
            __lowerCamelCase						:       int				   =				type_vocab_size
            __lowerCamelCase						:       Union[str, Any]				   =				type_sequence_label_size
            __lowerCamelCase						:       Any				   =				initializer_range
            __lowerCamelCase						:       str				   =				num_labels
            __lowerCamelCase						:       int				   =				num_choices
            __lowerCamelCase						:       Optional[int]				   =				scope




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size					)

            __lowerCamelCase						:       List[str]				   =				None
            if self.use_input_mask:
                 __lowerCamelCase						:       int				   =				random_attention_mask([self.batch_size, self.seq_length]					)

            __lowerCamelCase						:       Optional[int]				   =				None
            if self.use_token_type_ids:
                 __lowerCamelCase						:       List[str]				   =				ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size					)

            __lowerCamelCase						:       int				   =				None
            __lowerCamelCase						:       int				   =				None
            __lowerCamelCase						:       Dict				   =				None
            if self.use_labels:
                 __lowerCamelCase						:       Tuple				   =				ids_tensor([self.batch_size]	,    self.type_sequence_label_size					)
                 __lowerCamelCase						:       Optional[int]				   =				ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels					)
                 __lowerCamelCase						:       Union[str, Any]				   =				ids_tensor([self.batch_size]	,    self.num_choices					)

            __lowerCamelCase						:       Any				   =				self.get_config()

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




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            return BioGptConfig(
                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	,    is_decoder=__a	,    initializer_range=self.initializer_range	,    )




       def 					a_					(					self      : Any	,    A__      : List[str]	,    A__      : Tuple	,    A__      : Optional[Any]	,    A__      : int	,    A__      : str	,    A__      : List[str]	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				BioGptModel(config=__a					)
            model.to(__a					)
            model.eval()
            __lowerCamelCase						:       Optional[Any]				   =				model(__a	,    attention_mask=__a					)
            __lowerCamelCase						:       Any				   =				model(__a					)
            self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)					)




       def 					a_					(					self      : List[Any]	,    A__      : List[str]	,    A__      : str	,    A__      : Union[str, Any]	,    A__      : Optional[Any]	,    A__      : Tuple	,    A__      : Tuple	,    A__      : Optional[int]	,    A__      : Dict	,    A__      : Union[str, Any]	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				BioGptForCausalLM(config=__a					)
            model.to(__a					)
            model.eval()
            __lowerCamelCase						:       List[Any]				   =				model(__a	,    attention_mask=__a	,    token_type_ids=__a	,    labels=__a					)
            self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)					)




       def 					a_					(					self      : Tuple	,    A__      : Optional[int]	,    A__      : int	,    A__      : List[Any]	,    A__      : Tuple	,    A__      : Tuple	,    *A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				BioGptModel(config=__a					)
            model.to(__a					)
            model.eval()

            # create attention mask
            __lowerCamelCase						:       Dict				   =				torch.ones(input_ids.shape	,    dtype=torch.long	,    device=__a					)
            __lowerCamelCase						:       str				   =				self.seq_length // 2
            __lowerCamelCase						:       int				   =				0

            # first forward pass
            __lowerCamelCase						:       List[Any]				   =				model(__a	,    attention_mask=__a					).to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __lowerCamelCase						:       int				   =				ids_tensor((self.batch_size, 1)	,    config.vocab_size					)

            # change a random masked slice from input_ids
            __lowerCamelCase						:       Any				   =				ids_tensor((1,)	,    __a					).item() + 1
            __lowerCamelCase						:       int				   =				ids_tensor((self.batch_size, 1)	,    config.vocab_size					).squeeze(-1					)
            __lowerCamelCase						:       Tuple				   =				random_other_next_tokens

            # append to next input_ids and attn_mask
            __lowerCamelCase						:       int				   =				torch.cat([input_ids, next_tokens]	,    dim=-1					)
            __lowerCamelCase						:       int				   =				torch.cat(
                [attn_mask, torch.ones((attn_mask.shape[0], 1)	,    dtype=torch.long	,    device=__a					)]	,    dim=1	,    )

            # get two different outputs
            __lowerCamelCase						:       Union[str, Any]				   =				model(__a	,    attention_mask=__a					)["""last_hidden_state"""]
            __lowerCamelCase						:       Tuple				   =				model(__a	,    past_key_values=__a	,    attention_mask=__a					)["""last_hidden_state"""]

            # select random slice
            __lowerCamelCase						:       Dict				   =				ids_tensor((1,)	,    output_from_past.shape[-1]					).item()
            __lowerCamelCase						:       Dict				   =				output_from_no_past[:, -1, random_slice_idx].detach()
            __lowerCamelCase						:       Tuple				   =				output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(__a	,    __a	,    atol=1e-3					)					)




       def 					a_					(					self      : Union[str, Any]	,    A__      : Optional[int]	,    A__      : int	,    A__      : Tuple	,    A__      : List[str]	,    A__      : Dict	,    *A__      : int					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				BioGptModel(config=__a					).to(__a					).eval()

            __lowerCamelCase						:       Tuple				   =				torch.ones(input_ids.shape	,    dtype=torch.long	,    device=__a					)

            # first forward pass
            __lowerCamelCase						:       List[str]				   =				model(__a	,    attention_mask=__a	,    use_cache=__a					)

            __lowerCamelCase						:       Optional[Any]				   =				outputs.to_tuple()

            # create hypothetical multiple next token and extent to next_input_ids
            __lowerCamelCase						:       Dict				   =				ids_tensor((self.batch_size, 3)	,    config.vocab_size					)
            __lowerCamelCase						:       Optional[int]				   =				ids_tensor((self.batch_size, 3)	,    2					)

            # append to next input_ids and
            __lowerCamelCase						:       Tuple				   =				torch.cat([input_ids, next_tokens]	,    dim=-1					)
            __lowerCamelCase						:       Tuple				   =				torch.cat([attention_mask, next_attn_mask]	,    dim=-1					)

            __lowerCamelCase						:       int				   =				model(__a	,    attention_mask=__a					)["""last_hidden_state"""]
            __lowerCamelCase						:       Optional[Any]				   =				model(__a	,    attention_mask=__a	,    past_key_values=__a					)[
                """last_hidden_state"""
            ]

            # select random slice
            __lowerCamelCase						:       Tuple				   =				ids_tensor((1,)	,    output_from_past.shape[-1]					).item()
            __lowerCamelCase						:       List[Any]				   =				output_from_no_past[:, -3:, random_slice_idx].detach()
            __lowerCamelCase						:       List[Any]				   =				output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(__a	,    __a	,    atol=1e-3					)					)




       def 					a_					(					self      : Optional[Any]	,    A__      : Dict	,    A__      : Optional[int]	,    A__      : List[Any]	,    A__      : List[Any]	,    A__      : Dict	,    *A__      : List[str]	,    A__      : Tuple=False					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				BioGptForCausalLM(__a					)
            model.to(__a					)
            if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

            __lowerCamelCase						:       Optional[int]				   =				model(__a	,    labels=__a					)
            self.parent.assertEqual(result.loss.shape	,    ()					)
            self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)					)
            result.loss.backward()




       def 					a_					(					self      : Any	,    A__      : int	,    *A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				BioGptModel(__a					)
            __lowerCamelCase						:       Any				   =				model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers					)
            for key in model.state_dict().keys():
                 if "c_proj" in key and "weight" in key:
                      self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]					) - model_std					)	,    0.001					)
                      self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]					) - 0.0					)	,    0.01					)




       def 					a_					(					self      : Dict	,    A__      : Tuple	,    A__      : Any	,    A__      : Union[str, Any]	,    A__      : Optional[Any]	,    A__      : Optional[int]	,    *A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				self.num_labels
            __lowerCamelCase						:       str				   =				BioGptForTokenClassification(__a					)
            model.to(__a					)
            model.eval()
            __lowerCamelCase						:       List[str]				   =				model(__a	,    attention_mask=__a	,    token_type_ids=__a					)
            self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.prepare_config_and_inputs()
            (
                __lowerCamelCase			
            )						:       int				   =				config_and_inputs
            __lowerCamelCase						:       List[str]				   =				{"""input_ids""": input_ids, """attention_mask""": input_mask}
            return config, inputs_dict







@require_torch
class 	SCREAMING_SNAKE_CASE	(      __lowercase    ,   __lowercase    ,   __lowercase    ,   unittest.TestCase  ):
       snake_case__    :     List[Any]   		=			(
           (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
           if is_torch_available()
           else ()
       )
       snake_case__    :     Union[str, Any]   		=			(BioGptForCausalLM,) if is_torch_available() else ()
       snake_case__    :     Dict   		=			(
           {
               '''feature-extraction''': BioGptModel,
               '''text-classification''': BioGptForSequenceClassification,
               '''text-generation''': BioGptForCausalLM,
               '''token-classification''': BioGptForTokenClassification,
               '''zero-shot''': BioGptForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       snake_case__    :     List[Any]   		=			False




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				BioGptModelTester(self					)
            __lowerCamelCase						:       List[Any]				   =				ConfigTester(self	,    config_class=__a	,    hidden_size=37					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            self.config_tester.run_common_tests()




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__a					)




       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                 __lowerCamelCase						:       Optional[Any]				   =				type
                 self.model_tester.create_and_check_model(*__a					)




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__a					)




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*__a	,    gradient_checkpointing=__a					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__a					)




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*__a					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*__a					)




       @slow
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""					)
            model.to(__a					)
            __lowerCamelCase						:       Optional[Any]				   =				BioGptTokenizer.from_pretrained("""microsoft/biogpt"""					)

            __lowerCamelCase						:       Optional[int]				   =				"""left"""

            # Define PAD Token = EOS Token = 50256
            __lowerCamelCase						:       List[str]				   =				tokenizer.eos_token
            __lowerCamelCase						:       str				   =				model.config.eos_token_id

            # use different length sentences to test batching
            __lowerCamelCase						:       int				   =				[
                """Hello, my dog is a little""",
                """Today, I""",
            ]

            __lowerCamelCase						:       Union[str, Any]				   =				tokenizer(__a	,    return_tensors="""pt"""	,    padding=__a					)
            __lowerCamelCase						:       Any				   =				inputs["""input_ids"""].to(__a					)

            __lowerCamelCase						:       Any				   =				model.generate(
                input_ids=__a	,    attention_mask=inputs["""attention_mask"""].to(__a					)	,    )

            __lowerCamelCase						:       str				   =				tokenizer(sentences[0]	,    return_tensors="""pt"""					).input_ids.to(__a					)
            __lowerCamelCase						:       List[Any]				   =				model.generate(input_ids=__a					)

            __lowerCamelCase						:       List[Any]				   =				inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
            __lowerCamelCase						:       List[Any]				   =				tokenizer(sentences[1]	,    return_tensors="""pt"""					).input_ids.to(__a					)
            __lowerCamelCase						:       Dict				   =				model.generate(input_ids=__a	,    max_length=model.config.max_length - num_paddings					)

            __lowerCamelCase						:       List[Any]				   =				tokenizer.batch_decode(__a	,    skip_special_tokens=__a					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.decode(output_non_padded[0]	,    skip_special_tokens=__a					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.decode(output_padded[0]	,    skip_special_tokens=__a					)

            __lowerCamelCase						:       Optional[Any]				   =				[
                """Hello, my dog is a little bit bigger than a little bit.""",
                """Today, I have a good idea of how to use the information""",
            ]
            self.assertListEqual(__a	,    __a					)
            self.assertListEqual(__a	,    [non_padded_sentence, padded_sentence]					)




       @slow
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowerCamelCase						:       Optional[Any]				   =				BioGptModel.from_pretrained(__a					)
                 self.assertIsNotNone(__a					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.model_tester.prepare_config_and_inputs_for_common()
            __lowerCamelCase						:       List[str]				   =				3
            __lowerCamelCase						:       Dict				   =				input_dict["""input_ids"""]
            __lowerCamelCase						:       str				   =				input_ids.ne(1					).to(__a					)
            __lowerCamelCase						:       List[Any]				   =				ids_tensor([self.model_tester.batch_size]	,    self.model_tester.type_sequence_label_size					)
            __lowerCamelCase						:       Tuple				   =				BioGptForSequenceClassification(__a					)
            model.to(__a					)
            model.eval()
            __lowerCamelCase						:       List[str]				   =				model(__a	,    attention_mask=__a	,    labels=__a					)
            self.assertEqual(result.logits.shape	,    (self.model_tester.batch_size, self.model_tester.num_labels)					)




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.model_tester.prepare_config_and_inputs_for_common()
            __lowerCamelCase						:       Dict				   =				3
            __lowerCamelCase						:       Optional[Any]				   =				"""multi_label_classification"""
            __lowerCamelCase						:       Optional[Any]				   =				input_dict["""input_ids"""]
            __lowerCamelCase						:       Any				   =				input_ids.ne(1					).to(__a					)
            __lowerCamelCase						:       Optional[Any]				   =				ids_tensor(
                [self.model_tester.batch_size, config.num_labels]	,    self.model_tester.type_sequence_label_size					).to(torch.float					)
            __lowerCamelCase						:       List[Any]				   =				BioGptForSequenceClassification(__a					)
            model.to(__a					)
            model.eval()
            __lowerCamelCase						:       Optional[int]				   =				model(__a	,    attention_mask=__a	,    labels=__a					)
            self.assertEqual(result.logits.shape	,    (self.model_tester.batch_size, self.model_tester.num_labels)					)







@require_torch
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       @slow
       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""					)
            __lowerCamelCase						:       List[str]				   =				torch.tensor([[2, 4805, 9, 656, 21]]					)
            __lowerCamelCase						:       Optional[int]				   =				model(__a					)[0]

            __lowerCamelCase						:       int				   =				42384

            __lowerCamelCase						:       Dict				   =				torch.Size((1, 5, vocab_size)					)
            self.assertEqual(output.shape	,    __a					)

            __lowerCamelCase						:       int				   =				torch.tensor(
                [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]					)

            self.assertTrue(torch.allclose(output[:, :3, :3]	,    __a	,    atol=1e-4					)					)




       @slow
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				BioGptTokenizer.from_pretrained("""microsoft/biogpt"""					)
            __lowerCamelCase						:       int				   =				BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""					)
            model.to(__a					)

            torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				tokenizer("""COVID-19 is"""	,    return_tensors="""pt"""					).to(__a					)
            __lowerCamelCase						:       str				   =				model.generate(
                **__a	,    min_length=100	,    max_length=1024	,    num_beams=5	,    early_stopping=__a	,    )
            __lowerCamelCase						:       int				   =				tokenizer.decode(output_ids[0]	,    skip_special_tokens=__a					)

            __lowerCamelCase						:       Dict				   =				(
                """COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
                """ causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
                """ territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
                """ and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
                """ more than 800,000 deaths."""
            )
            self.assertEqual(__a	,    __a					)










'''simple docstring'''

import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase						:       str				   =				RemBertConfig.from_json_file(_lowercase     )
     print("""Building PyTorch model from configuration: {}""".format(str(_lowercase     )     )     )
     __lowerCamelCase						:       List[Any]				   =				RemBertModel(_lowercase     )

     # Load weights from tf checkpoint
     load_tf_weights_in_rembert(_lowercase,						_lowercase,						_lowercase     )

     # Save pytorch-model
     print("""Save PyTorch model to {}""".format(_lowercase     )     )
     torch.save(model.state_dict(),						_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



