
"""simple docstring"""




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
   raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
   raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
_A     :			Optional[int]  						=			logging.get_logger(__name__)

_A     :			str  						=			"""Hello, World!"""
_A     :			Optional[int]  						=			"""en_XX"""


def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			bool    )					->							int:
       lowercase       :							str									=				Path("data_bin"    )
       lowercase       :							Union[str, Any]									=				FairseqXmodModel.from_pretrained(
           model_name_or_path=str(Path(__snake_case    ).parent    )	,					checkpoint_file=Path(__snake_case    ).name	,					_name="xmod_base"	,					arch="xmod_base"	,					task="multilingual_masked_lm"	,					data_name_or_path=str(__snake_case    )	,					bpe="sentencepiece"	,					sentencepiece_model=str(Path(__snake_case    ).parent / "sentencepiece.bpe.model"    )	,					src_dict=str(data_dir / "dict.txt"    )	,					)
       xmod.eval()  # disable dropout
       print(__snake_case    )

       lowercase       :							Union[str, Any]									=				xmod.model.encoder.sentence_encoder
       lowercase       :							Optional[int]									=				XmodConfig(
           vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings	,					hidden_size=xmod.cfg.model.encoder_embed_dim	,					num_hidden_layers=xmod.cfg.model.encoder_layers	,					num_attention_heads=xmod.cfg.model.encoder_attention_heads	,					intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim	,					max_position_embeddings=514	,					type_vocab_size=1	,					layer_norm_eps=1E-5	,					pre_norm=xmod.cfg.model.encoder_normalize_before	,					adapter_reduction_factor=getattr(xmod.cfg.model	,					"bottleneck"	,					2    )	,					adapter_layer_norm=xmod.cfg.model.adapter_layer_norm	,					adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm	,					ln_before_adapter=xmod.cfg.model.ln_before_adapter	,					languages=xmod.cfg.model.languages	,					)
       if classification_head:
              lowercase       :							Optional[Any]									=				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

       print("Our X-MOD config:"	,					__snake_case    )

       lowercase       :							Optional[int]									=				XmodForSequenceClassification(__snake_case    ) if classification_head else XmodForMaskedLM(__snake_case    )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       lowercase       :							Any									=				xmod_sent_encoder.embed_tokens.weight
       lowercase       :							Optional[int]									=				xmod_sent_encoder.embed_positions.weight
       lowercase       :							Dict									=				torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c xmod doesn't use them.

       lowercase       :							Optional[Any]									=				xmod_sent_encoder.layernorm_embedding.weight
       lowercase       :							Optional[Any]									=				xmod_sent_encoder.layernorm_embedding.bias

       for i in range(config.num_hidden_layers    ):
              # Encoder: start of layer
              lowercase       :							List[Any]									=				model.roberta.encoder.layer[i]
              lowercase       :							Dict									=				xmod_sent_encoder.layers[i]

              # self attention
              lowercase       :							int									=				layer.attention.self
              if not (
                  xmod_layer.self_attn.k_proj.weight.data.shape
                  == xmod_layer.self_attn.q_proj.weight.data.shape
                  == xmod_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)    )
              ):
                     raise AssertionError("Dimensions of self-attention weights do not match."    )

              lowercase       :							List[str]									=				xmod_layer.self_attn.q_proj.weight
              lowercase       :							List[str]									=				xmod_layer.self_attn.q_proj.bias
              lowercase       :							str									=				xmod_layer.self_attn.k_proj.weight
              lowercase       :							List[str]									=				xmod_layer.self_attn.k_proj.bias
              lowercase       :							Dict									=				xmod_layer.self_attn.v_proj.weight
              lowercase       :							str									=				xmod_layer.self_attn.v_proj.bias

              # self-attention output
              lowercase       :							List[Any]									=				layer.attention.output
              if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                     raise AssertionError("Dimensions of self-attention output weights do not match."    )
              lowercase       :							List[Any]									=				xmod_layer.self_attn.out_proj.weight
              lowercase       :							Any									=				xmod_layer.self_attn.out_proj.bias
              lowercase       :							int									=				xmod_layer.self_attn_layer_norm.weight
              lowercase       :							Any									=				xmod_layer.self_attn_layer_norm.bias

              # intermediate
              lowercase       :							Union[str, Any]									=				layer.intermediate
              if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError("Dimensions of intermediate weights do not match."    )
              lowercase       :							int									=				xmod_layer.fca.weight
              lowercase       :							List[str]									=				xmod_layer.fca.bias

              # output
              lowercase       :							str									=				layer.output
              if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError("Dimensions of feed-forward weights do not match."    )
              lowercase       :							int									=				xmod_layer.fca.weight
              lowercase       :							Union[str, Any]									=				xmod_layer.fca.bias
              lowercase       :							List[str]									=				xmod_layer.final_layer_norm.weight
              lowercase       :							Union[str, Any]									=				xmod_layer.final_layer_norm.bias
              if bert_output.adapter_layer_norm is not None:
                     lowercase       :							List[str]									=				xmod_layer.adapter_layer_norm.weight
                     lowercase       :							List[str]									=				xmod_layer.adapter_layer_norm.bias

              if sorted(bert_output.adapter_modules.keys()    ) != sorted(xmod_layer.adapter_modules.keys()    ):
                     raise AssertionError("Lists of language adapters do not match."    )
              for lang_code, adapter in xmod_layer.adapter_modules.items():
                     lowercase       :							Any									=				bert_output.adapter_modules[lang_code]
                     lowercase       :							Tuple									=				xmod_layer.adapter_modules[lang_code]
                     lowercase       :							Union[str, Any]									=				from_adapter.fca.weight
                     lowercase       :							List[str]									=				from_adapter.fca.bias
                     lowercase       :							Dict									=				from_adapter.fca.weight
                     lowercase       :							int									=				from_adapter.fca.bias

              # end of layer

       if xmod_sent_encoder.layer_norm is not None:
              lowercase       :							str									=				xmod_sent_encoder.layer_norm.weight
              lowercase       :							Optional[int]									=				xmod_sent_encoder.layer_norm.bias

       if classification_head:
              lowercase       :							int									=				xmod.model.classification_heads["mnli"].dense.weight
              lowercase       :							str									=				xmod.model.classification_heads["mnli"].dense.bias
              lowercase       :							Any									=				xmod.model.classification_heads["mnli"].out_proj.weight
              lowercase       :							List[Any]									=				xmod.model.classification_heads["mnli"].out_proj.bias
       else:
              # LM Head
              lowercase       :							Union[str, Any]									=				xmod.model.encoder.lm_head.dense.weight
              lowercase       :							Optional[int]									=				xmod.model.encoder.lm_head.dense.bias
              lowercase       :							Any									=				xmod.model.encoder.lm_head.layer_norm.weight
              lowercase       :							int									=				xmod.model.encoder.lm_head.layer_norm.bias
              lowercase       :							Union[str, Any]									=				xmod.model.encoder.lm_head.weight
              lowercase       :							Any									=				xmod.model.encoder.lm_head.bias

       # Let's check that we get the same results.
       lowercase       :							Optional[Any]									=				xmod.encode(__snake_case    ).unsqueeze(0    )  # batch of size 1
       model.roberta.set_default_language(__snake_case    )

       lowercase       :							str									=				model(__snake_case    )[0]
       if classification_head:
              lowercase       :							Optional[Any]									=				xmod.model.classification_heads["mnli"](xmod.extract_features(__snake_case    )    )
       else:
              lowercase       :							List[str]									=				xmod.model(__snake_case	,					lang_id=[SAMPLE_LANGUAGE]    )[0]
       print(our_output.shape	,					their_output.shape    )
       lowercase       :							Optional[int]									=				torch.max(torch.abs(our_output - their_output    )    ).item()
       print(f"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
       lowercase       :							List[str]									=				torch.allclose(__snake_case	,					__snake_case	,					atol=1E-3    )
       print("Do both models output the same tensors?"	,					"🔥" if success else "💩"    )
       if not success:
              raise Exception("Something went wRoNg"    )

       Path(__snake_case    ).mkdir(parents=__snake_case	,					exist_ok=__snake_case    )
       print(f"""Saving model to {pytorch_dump_folder_path}"""    )
       model.save_pretrained(__snake_case    )


if __name__ == "__main__":
   _A     :			Dict  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
   )
   _A     :			Optional[Any]  						=			parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )




"""simple docstring"""




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()

_A     :			Optional[int]  						=			{
    """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     __magic_name__      ( __snake_case							:			Optional[int]	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Dict	,					__snake_case							:			str	,					__snake_case							:			Dict=False	,					__snake_case							:			Optional[Any]=True    )					->							Dict:
       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       :							Tuple									=				MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              lowercase       :							Optional[Any]									=				cached_file(__snake_case	,					__snake_case	,					force_download=not use_cached_models    )
       lowercase       :							Optional[int]									=				config_class.from_json_file(__snake_case    )
       lowercase       :							str									=				True
       lowercase       :							Optional[int]									=				True
       print(f"""Building TensorFlow model from configuration: {config}"""    )
       lowercase       :							Optional[int]									=				model_class(__snake_case    )

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              lowercase       :							List[Any]									=				cached_file(
                  __snake_case	,					__snake_case	,					force_download=not use_cached_models    )
       # Load PyTorch checkpoint in tf2 model:
       lowercase       :							List[str]									=				load_pytorch_checkpoint_in_tfa_model(__snake_case	,					__snake_case    )

       if compare_with_pt_model:
              lowercase       :							Optional[int]									=				tf_model(tf_model.dummy_inputs	,					training=__snake_case    )  # build the network

              lowercase       :							Optional[Any]									=				torch.load(__snake_case	,					map_location="cpu"    )
              lowercase       :							Tuple									=				pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=__snake_case	,					config=__snake_case	,					state_dict=__snake_case    )

              with torch.no_grad():
                     lowercase       :							Dict									=				pt_model(**pt_model.dummy_inputs    )

              lowercase       :							List[str]									=				pto[0].numpy()
              lowercase       :							List[Any]									=				tfo[0].numpy()
              lowercase       :							List[str]									=				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(__snake_case	,					save_format="h5"    )


def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			Optional[int]	,					__snake_case							:			Dict=None	,					__snake_case							:			List[str]=None	,					__snake_case							:			Optional[int]=False	,					__snake_case							:			Optional[int]=False	,					__snake_case							:			List[Any]=False	,					__snake_case							:			Tuple=False	,					)					->							int:
       if args_model_type is None:
              lowercase       :							Any									=				list(MODEL_CLASSES.keys()    )
       else:
              lowercase       :							int									=				[args_model_type]

       for j, model_type in enumerate(__snake_case	,					start=1    ):
              print("=" * 100    )
              print(f""" Converting model type {j}/{len(__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       :							int									=				MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     lowercase       :							Optional[int]									=				list(aws_model_maps.keys()    )
              if config_shortcut_names_or_path is None:
                     lowercase       :							Union[str, Any]									=				model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(__snake_case	,					__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       :							Optional[Any]									=				model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""    )
                            continue
                     print(
                         f"""    Converting checkpoint {i}/{len(__snake_case    )}: {model_shortcut_name} - model_type {model_type}"""    )
                     print("-" * 100    )

                     if config_shortcut_name in aws_config_map:
                            lowercase       :							List[Any]									=				cached_file(__snake_case	,					__snake_case	,					force_download=not use_cached_models    )
                     else:
                            lowercase       :							Optional[int]									=				config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            lowercase       :							int									=				cached_file(__snake_case	,					__snake_case	,					force_download=not use_cached_models    )
                     else:
                            lowercase       :							Union[str, Any]									=				model_shortcut_name

                     if os.path.isfile(__snake_case    ):
                            lowercase       :							Union[str, Any]									=				"converted_model"

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


if __name__ == "__main__":
   _A     :			Tuple  						=			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.""")
   _A     :			str  						=			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,
   )


