



import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					# Construct configuration depending on size
					UpperCamelCase_			= {"small": (5_12, 8, 24), "medium": (10_24, 8, 36), "large": (15_36, 16, 48)}
					UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= MODELS[model_size]  # set model hyperparameters
					UpperCamelCase_			= ImageGPTConfig(n_embd=_lowerCAmelCase ,						n_layer=_lowerCAmelCase ,						n_head=_lowerCAmelCase)
					UpperCamelCase_			= ImageGPTForCausalLM(_lowerCAmelCase)

					# Load weights from numpy
					load_tf_weights_in_imagegpt(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

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


if __name__ == "__main__":
	UpperCAmelCase :				Optional[int]							=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--imagegpt_checkpoint_path""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Path to the TensorFlow checkpoint path.""",
	)
	parser.add_argument(
	    """--model_size""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	UpperCAmelCase :				List[Any]							=parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      [
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Dict							 =      [
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)