
import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    data_utils.TransfoXLTokenizer
SCREAMING_SNAKE_CASE__   :			str												=    data_utils.TransfoXLCorpus
SCREAMING_SNAKE_CASE__   :			List[Any]												=    data_utils
SCREAMING_SNAKE_CASE__   :			Any												=    data_utils




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Optional[int]							)			->   Dict:
				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(__lowerCAmelCase		,  '''rb'''							) as fp:
												__lowerCamelCase    					=	pickle.load(__lowerCAmelCase		,  encoding='''latin1'''							)
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								__lowerCamelCase    					=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
								print(f'''Save vocabulary to {pytorch_vocab_dump_path}'''							)
								__lowerCamelCase    					=	corpus.vocab.__dict__
								torch.save(__lowerCAmelCase		,  __lowerCAmelCase							)

								__lowerCamelCase    					=	corpus.__dict__
								corpus_dict_no_vocab.pop('''vocab'''		,  __lowerCAmelCase							)
								__lowerCamelCase    					=	pytorch_dump_folder_path + '''/''' + CORPUS_NAME
								print(f'''Save dataset to {pytorch_dataset_dump_path}'''							)
								torch.save(__lowerCAmelCase		,  __lowerCAmelCase							)

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								__lowerCamelCase    					=	os.path.abspath(__lowerCAmelCase							)
								__lowerCamelCase    					=	os.path.abspath(__lowerCAmelCase							)

								print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''							)
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												__lowerCamelCase    					=	TransfoXLConfig()
								else:
												__lowerCamelCase    					=	TransfoXLConfig.from_json_file(__lowerCAmelCase							)
								print(f'''Building PyTorch model from configuration: {config}'''							)
								__lowerCamelCase    					=	TransfoXLLMHeadModel(__lowerCAmelCase							)

								__lowerCamelCase    					=	load_tf_weights_in_transfo_xl(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)
								# Save pytorch-model
								__lowerCamelCase    					=	os.path.join(__lowerCAmelCase		,  __lowerCAmelCase							)
								__lowerCamelCase    					=	os.path.join(__lowerCAmelCase		,  __lowerCAmelCase							)
								print(f'''Save PyTorch model to {os.path.abspath(__lowerCAmelCase							)}'''							)
								torch.save(model.state_dict()		,  __lowerCAmelCase							)
								print(f'''Save configuration file to {os.path.abspath(__lowerCAmelCase							)}'''							)
								with open(__lowerCAmelCase		,  '''w'''		,  encoding='''utf-8'''							) as f:
												f.write(config.to_json_string()							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			Any												=    argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the folder to store the PyTorch model or dataset/vocab.",
					)
					parser.add_argument(
					    "--tf_checkpoint_path",
					    default="",
					    type=str,
					    help="An optional path to a TensorFlow checkpoint path to be converted.",
					)
					parser.add_argument(
					    "--transfo_xl_config_file",
					    default="",
					    type=str,
					    help=(
					        "An optional config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--transfo_xl_dataset_file",
					    default="",
					    type=str,
					    help="An optional dataset file to be converted in a vocabulary.",
					)
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__   :			List[str]												=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			str												=    ["BlipImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BlipModel",
					    "BlipPreTrainedModel",
					    "BlipForConditionalGeneration",
					    "BlipForQuestionAnswering",
					    "BlipVisionModel",
					    "BlipTextModel",
					    "BlipForImageTextRetrieval",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    [
					    "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFBlipModel",
					    "TFBlipPreTrainedModel",
					    "TFBlipForConditionalGeneration",
					    "TFBlipForQuestionAnswering",
					    "TFBlipVisionModel",
					    "TFBlipTextModel",
					    "TFBlipForImageTextRetrieval",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Tuple												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



