




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE			=						{
    "configuration_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig", "BertOnnxConfig"],
    "tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE			=						["BertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE			=						[
							    "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BertForMaskedLM",
							    "BertForMultipleChoice",
							    "BertForNextSentencePrediction",
							    "BertForPreTraining",
							    "BertForQuestionAnswering",
							    "BertForSequenceClassification",
							    "BertForTokenClassification",
							    "BertLayer",
							    "BertLMHeadModel",
							    "BertModel",
							    "BertPreTrainedModel",
							    "load_tf_weights_in_bert",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE			=						[
							    "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFBertEmbeddings",
							    "TFBertForMaskedLM",
							    "TFBertForMultipleChoice",
							    "TFBertForNextSentencePrediction",
							    "TFBertForPreTraining",
							    "TFBertForQuestionAnswering",
							    "TFBertForSequenceClassification",
							    "TFBertForTokenClassification",
							    "TFBertLMHeadModel",
							    "TFBertMainLayer",
							    "TFBertModel",
							    "TFBertPreTrainedModel",
							]
try:
							if not is_tensorflow_text_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE			=						["TFBertTokenizer"]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE			=						[
							    "FlaxBertForCausalLM",
							    "FlaxBertForMaskedLM",
							    "FlaxBertForMultipleChoice",
							    "FlaxBertForNextSentencePrediction",
							    "FlaxBertForPreTraining",
							    "FlaxBertForQuestionAnswering",
							    "FlaxBertForSequenceClassification",
							    "FlaxBertForTokenClassification",
							    "FlaxBertModel",
							    "FlaxBertPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
							from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_fast import BertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert import (
														    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BertForMaskedLM,
														    BertForMultipleChoice,
														    BertForNextSentencePrediction,
														    BertForPreTraining,
														    BertForQuestionAnswering,
														    BertForSequenceClassification,
														    BertForTokenClassification,
														    BertLayer,
														    BertLMHeadModel,
														    BertModel,
														    BertPreTrainedModel,
														    load_tf_weights_in_bert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bert import (
														    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBertEmbeddings,
														    TFBertForMaskedLM,
														    TFBertForMultipleChoice,
														    TFBertForNextSentencePrediction,
														    TFBertForPreTraining,
														    TFBertForQuestionAnswering,
														    TFBertForSequenceClassification,
														    TFBertForTokenClassification,
														    TFBertLMHeadModel,
														    TFBertMainLayer,
														    TFBertModel,
														    TFBertPreTrainedModel,
														)

							try:
														if not is_tensorflow_text_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_tf import TFBertTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bert import (
														    FlaxBertForCausalLM,
														    FlaxBertForMaskedLM,
														    FlaxBertForMultipleChoice,
														    FlaxBertForNextSentencePrediction,
														    FlaxBertForPreTraining,
														    FlaxBertForQuestionAnswering,
														    FlaxBertForSequenceClassification,
														    FlaxBertForTokenClassification,
														    FlaxBertModel,
														    FlaxBertPreTrainedModel,
														)

else:
							import sys

							_SCREAMING_SNAKE_CASE			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		str:
			# Construct model
			if openai_config_file == "":
						A_									= OpenAIGPTConfig()
			else:
						A_									= OpenAIGPTConfig.from_json_file(SCREAMING_SNAKE_CASE_     )
			A_									= OpenAIGPTModel(SCREAMING_SNAKE_CASE_     )

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )

			# Save pytorch-model
			A_									= pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			A_									= pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'Save PyTorch model to {pytorch_weights_dump_path}'     )
			torch.save(model.state_dict()       ,SCREAMING_SNAKE_CASE_     )
			print(F'Save configuration file to {pytorch_config_dump_path}'     )
			with open(SCREAMING_SNAKE_CASE_       ,'w'       ,encoding='utf-8'     ) as f:
						f.write(config.to_json_string()     )


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--openai_checkpoint_folder_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							parser.add_argument(
							    "--openai_config_file",
							    default="",
							    type=str,
							    help=(
							        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
							        "This specifies the model architecture."
							    ),
							)
							_SCREAMING_SNAKE_CASE			=						parser.parse_args()
							convert_openai_checkpoint_to_pytorch(
							    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
							)




