



import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


lowercase_								=     logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowercase_								=     'cuda' if torch.cuda.is_available() else 'cpu'



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=100						,  SCREAMING_SNAKE_CASE_=" "  ):
			lowercase__					  =				text.split(_lowerCamelCase  )
			return [character.join(text[i : i + n]  ).strip() for i in range(0						,  len(_lowerCamelCase  )						,  _lowerCamelCase  )]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				[], []
			for title, text in zip(documents["title"]						,  documents["text"]  ):
						if text is not None:
									for passage in split_text(_lowerCamelCase  ):
												titles.append(title if title is not None else ""  )
												texts.append(_lowerCamelCase  )
			return {"title": titles, "text": texts}



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				ctx_tokenizer(
			    documents["title"]						,  documents["text"]						,  truncation=_lowerCamelCase						,  padding="longest"						,  return_tensors="pt"  )["input_ids"]
			lowercase__					  =				ctx_encoder(input_ids.to(device=_lowerCamelCase  )						,  return_dict=_lowerCamelCase  ).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  ):
			logger.info("Step 1 - Create the dataset"  )
			######################################

			# The dataset needed for RAG must have three columns:
			# - title (string): title of the document
			# - text (string): text of a passage of the document
			# - embeddings (array of dimension d): DPR representation of the passage
			# Let's say you have documents in tab-separated csv files with columns "title" and "text"
			assert os.path.isfile(rag_example_args.csv_path  ), "Please provide a valid path to a csv file"

			# You can load a Dataset object this way
			lowercase__					  =				load_dataset(
			    "csv"						,  data_files=[rag_example_args.csv_path]						,  split="train"						,  delimiter="\t"						,  column_names=["title", "text"]  )

			# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

			# Then split the documents into passages of 100 words
			lowercase__					  =				dataset.map(_lowerCamelCase						,  batched=_lowerCamelCase						,  num_proc=processing_args.num_proc  )

			# And compute the embeddings
			lowercase__					  =				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  ).to(device=_lowerCamelCase  )
			lowercase__					  =				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  )
			lowercase__					  =				Features(
			    {"text": Value("string"  ), "title": Value("string"  ), "embeddings": Sequence(Value("float32"  )  )}  )  # optional, save as float32 instead of float64 to save space
			lowercase__					  =				dataset.map(
			    partial(_lowerCamelCase						,  ctx_encoder=_lowerCamelCase						,  ctx_tokenizer=_lowerCamelCase  )						,  batched=_lowerCamelCase						,  batch_size=processing_args.batch_size						,  features=_lowerCamelCase						,  )

			# And finally save your dataset
			lowercase__					  =				os.path.join(rag_example_args.output_dir						,  "my_knowledge_dataset"  )
			dataset.save_to_disk(_lowerCamelCase  )
			# from datasets import load_from_disk
			# dataset = load_from_disk(passages_path)  # to reload the dataset

			######################################
			logger.info("Step 2 - Index the dataset"  )
			######################################

			# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
			lowercase__					  =				faiss.IndexHNSWFlat(index_hnsw_args.d						,  index_hnsw_args.m						,  faiss.METRIC_INNER_PRODUCT  )
			dataset.add_faiss_index("embeddings"						,  custom_index=_lowerCamelCase  )

			# And save the index
			lowercase__					  =				os.path.join(rag_example_args.output_dir						,  "my_knowledge_dataset_hnsw_index.faiss"  )
			dataset.get_index("embeddings"  ).save(_lowerCamelCase  )
			# dataset.load_faiss_index("embeddings", index_path)  # to reload the index
@dataclass
class 				_snake_case			:
				UpperCamelCase__				:					Tuple     		=field(
				    default=str(Path(__lowercase).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv""")    ,	metadata={"""help""": """Path to a tab-separated csv file with columns \'title\' and \'text\'"""}    ,	)
				UpperCamelCase__				:					int     		=field(
				    default=__lowercase    ,	metadata={"""help""": """Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'."""}    ,	)
				UpperCamelCase__				:					Tuple     		=field(
				    default="""facebook/rag-sequence-nq"""    ,	metadata={"""help""": """The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\'"""}    ,	)
				UpperCamelCase__				:					Tuple     		=field(
				    default="""facebook/dpr-ctx_encoder-multiset-base"""    ,	metadata={
				        """help""": (
				            """The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or"""
				            """ \'facebook/dpr-ctx_encoder-multiset-base\'"""
				        )
				    }    ,	)
				UpperCamelCase__				:					Any     		=field(
				    default=str(Path(__lowercase).parent / """test_run""" / """dummy-kb""")    ,	metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}    ,	)
@dataclass
class 				_snake_case			:
				UpperCamelCase__				:					str     		=field(
				    default=__lowercase    ,	metadata={
				        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
				    }    ,	)
				UpperCamelCase__				:					Optional[Any]     		=field(
				    default=1_6    ,	metadata={
				        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
				    }    ,	)
@dataclass
class 				_snake_case			:
				UpperCamelCase__				:					Tuple     		=field(
				    default=7_6_8    ,	metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}    ,	)
				UpperCamelCase__				:					str     		=field(
				    default=1_2_8    ,	metadata={
				        """help""": (
				            """The number of bi-directional links created for every new element during the HNSW index construction."""
				        )
				    }    ,	)


if __name__ == "__main__":
						logging.basicConfig(level=logging.WARNING)
						logger.setLevel(logging.INFO)

						lowercase_								=     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						lowercase_								=     parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												lowercase_								=     rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)









import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
							import numpy as np
							import torch

							from . import (
							    AlbertForPreTraining,
							    BartForConditionalGeneration,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    CamembertForMaskedLM,
							    CTRLLMHeadModel,
							    DistilBertForMaskedLM,
							    DistilBertForQuestionAnswering,
							    DPRContextEncoder,
							    DPRQuestionEncoder,
							    DPRReader,
							    ElectraForPreTraining,
							    FlaubertWithLMHeadModel,
							    GPTaLMHeadModel,
							    LayoutLMForMaskedLM,
							    LxmertForPreTraining,
							    LxmertVisualFeatureEncoder,
							    OpenAIGPTLMHeadModel,
							    RobertaForMaskedLM,
							    RobertaForSequenceClassification,
							    TaForConditionalGeneration,
							    TransfoXLLMHeadModel,
							    XLMRobertaForMaskedLM,
							    XLMWithLMHeadModel,
							    XLNetLMHeadModel,
							)


logging.set_verbosity_info()

lowercase_								=     {
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=True  ):
			if model_type not in MODEL_CLASSES:
						raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()  )}.'''  )

			lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				MODEL_CLASSES[model_type]

			# Initialise TF model
			if config_file in aws_config_map:
						lowercase__					  =				cached_file(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  force_download=not use_cached_models  )
			lowercase__					  =				config_class.from_json_file(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				True
			lowercase__					  =				True
			print(f'''Building TensorFlow model from configuration: {config}'''  )
			lowercase__					  =				model_class(SCREAMING_SNAKE_CASE_  )

			# Load weights from tf checkpoint
			if pytorch_checkpoint_path in aws_config_map.keys():
						lowercase__					  =				cached_file(
						    SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  force_download=not use_cached_models  )
			# Load PyTorch checkpoint in tf2 model:
			lowercase__					  =				load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

			if compare_with_pt_model:
						lowercase__					  =				tf_model(tf_model.dummy_inputs						,  training=SCREAMING_SNAKE_CASE_  )  # build the network

						lowercase__					  =				torch.load(SCREAMING_SNAKE_CASE_						,  map_location="cpu"  )
						lowercase__					  =				pt_model_class.from_pretrained(
						    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_						,  config=SCREAMING_SNAKE_CASE_						,  state_dict=SCREAMING_SNAKE_CASE_  )

						with torch.no_grad():
									lowercase__					  =				pt_model(**pt_model.dummy_inputs  )

						lowercase__					  =				pto[0].numpy()
						lowercase__					  =				tfo[0].numpy()
						lowercase__					  =				np.amax(np.abs(np_pt - np_tf  )  )
						print(f'''Max absolute difference between models outputs {diff}'''  )
						assert diff <= 2e-2, f'''Error, model absolute difference is >2e-2: {diff}'''

			# Save pytorch-model
			print(f'''Save TensorFlow model to {tf_dump_path}'''  )
			tf_model.save_weights(SCREAMING_SNAKE_CASE_						,  save_format="h5"  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False						,  ):
			if args_model_type is None:
						lowercase__					  =				list(MODEL_CLASSES.keys()  )
			else:
						lowercase__					  =				[args_model_type]

			for j, model_type in enumerate(SCREAMING_SNAKE_CASE_						,  start=1  ):
						print("=" * 100  )
						print(f''' Converting model type {j}/{len(SCREAMING_SNAKE_CASE_  )}: {model_type}'''  )
						print("=" * 100  )
						if model_type not in MODEL_CLASSES:
									raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()  )}.'''  )

						lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				MODEL_CLASSES[model_type]

						if model_shortcut_names_or_path is None:
									lowercase__					  =				list(aws_model_maps.keys()  )
						if config_shortcut_names_or_path is None:
									lowercase__					  =				model_shortcut_names_or_path

						for i, (model_shortcut_name, config_shortcut_name) in enumerate(
						    zip(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )						,  start=1  ):
									print("-" * 100  )
									if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
												if not only_convert_finetuned_models:
															print(f'''    Skipping finetuned checkpoint {model_shortcut_name}'''  )
															continue
												lowercase__					  =				model_shortcut_name
									elif only_convert_finetuned_models:
												print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''  )
												continue
									print(
									    f'''    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE_  )}: {model_shortcut_name} - model_type {model_type}'''  )
									print("-" * 100  )

									if config_shortcut_name in aws_config_map:
												lowercase__					  =				cached_file(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  force_download=not use_cached_models  )
									else:
												lowercase__					  =				config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												lowercase__					  =				cached_file(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  force_download=not use_cached_models  )
									else:
												lowercase__					  =				model_shortcut_name

									if os.path.isfile(SCREAMING_SNAKE_CASE_  ):
												lowercase__					  =				"converted_model"

									convert_pt_checkpoint_to_tf(
									    model_type=SCREAMING_SNAKE_CASE_						,  pytorch_checkpoint_path=SCREAMING_SNAKE_CASE_						,  config_file=SCREAMING_SNAKE_CASE_						,  tf_dump_path=os.path.join(SCREAMING_SNAKE_CASE_						,  model_shortcut_name + "-tf_model.h5"  )						,  compare_with_pt_model=SCREAMING_SNAKE_CASE_						,  )
									if remove_cached_files:
												os.remove(SCREAMING_SNAKE_CASE_  )
												os.remove(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							lowercase_								=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
							)
							parser.add_argument(
							    """--model_type""",
							    default=None,
							    type=str,
							    help=(
							        F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
							        """convert all the models from AWS."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_checkpoint_path""",
							    default=None,
							    type=str,
							    help=(
							        """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
							        """If not given, will download and convert all the checkpoints from AWS."""
							    ),
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    help=(
							        """The config json file corresponding to the pre-trained model. \n"""
							        """This specifies the model architecture. If not given and """
							        """--pytorch_checkpoint_path is not given or is a shortcut name """
							        """use the configuration associated to the shortcut name on the AWS"""
							    ),
							)
							parser.add_argument(
							    """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
							)
							parser.add_argument(
							    """--use_cached_models""",
							    action="""store_true""",
							    help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
							)
							parser.add_argument(
							    """--remove_cached_files""",
							    action="""store_true""",
							    help="""Remove pytorch models after conversion (save memory when converting in batches).""",
							)
							parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
							lowercase_								=     parser.parse_args()

							# if args.pytorch_checkpoint_path is not None:
							#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
							#                                 args.pytorch_checkpoint_path,
							#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
							#                                 args.tf_dump_path,
							#                                 compare_with_pt_model=args.compare_with_pt_model,
							#                                 use_cached_models=args.use_cached_models)
							# else:
							convert_all_pt_checkpoints_to_tf(
							    args.model_type.lower() if args.model_type is not None else None,
							    args.tf_dump_path,
							    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
							    if args.pytorch_checkpoint_path is not None
							    else None,
							    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
							    compare_with_pt_model=args.compare_with_pt_model,
							    use_cached_models=args.use_cached_models,
							    remove_cached_files=args.remove_cached_files,
							    only_convert_finetuned_models=args.only_convert_finetuned_models,
							)




