



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	Any:
    __UpperCAmelCase			 =384
    __UpperCAmelCase			 =7
    if "tiny" in model_name:
        __UpperCAmelCase			 =96
        __UpperCAmelCase			 =(2, 2, 6, 2)
        __UpperCAmelCase			 =(3, 6, 12, 24)
    elif "small" in model_name:
        __UpperCAmelCase			 =96
        __UpperCAmelCase			 =(2, 2, 18, 2)
        __UpperCAmelCase			 =(3, 6, 12, 24)
    elif "base" in model_name:
        __UpperCAmelCase			 =128
        __UpperCAmelCase			 =(2, 2, 18, 2)
        __UpperCAmelCase			 =(4, 8, 16, 32)
        __UpperCAmelCase			 =12
        __UpperCAmelCase			 =512
    elif "large" in model_name:
        __UpperCAmelCase			 =192
        __UpperCAmelCase			 =(2, 2, 18, 2)
        __UpperCAmelCase			 =(6, 12, 24, 48)
        __UpperCAmelCase			 =12
        __UpperCAmelCase			 =768

    # set label information
    __UpperCAmelCase			 =150
    __UpperCAmelCase			 ='''huggingface/label-files'''
    __UpperCAmelCase			 ='''ade20k-id2label.json'''
    __UpperCAmelCase			 =json.load(open(hf_hub_download(_lowercase				,   _lowercase				,   repo_type='''dataset'''      )				,   '''r'''      )      )
    __UpperCAmelCase			 ={int(_lowercase      ): v for k, v in idalabel.items()}
    __UpperCAmelCase			 ={v: k for k, v in idalabel.items()}

    __UpperCAmelCase			 =SwinConfig(
        embed_dim=_lowercase				,   depths=_lowercase				,   num_heads=_lowercase				,   window_size=_lowercase				,   out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,   )
    __UpperCAmelCase			 =UperNetConfig(
        backbone_config=_lowercase				,   auxiliary_in_channels=_lowercase				,   num_labels=_lowercase				,   idalabel=_lowercase				,   labelaid=_lowercase				,   )

    return config




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	List[str]:
    __UpperCAmelCase			 =[]

    # fmt: off
    # stem
    rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')      )
    rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')      )
    rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')      )
    rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')      )
    # stages
    for i in range(len(config.backbone_config.depths      )      ):
        for j in range(config.backbone_config.depths[i]      ):
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")      )
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")      )

        if i < 3:
            rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")      )
            rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")      )
            rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")      )
        rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")      )
        rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")      )

    # decode head
    rename_keys.extend(
        [
            ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
            ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
            ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
            ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
        ]      )
    # fmt: on

    return rename_keys




def 		SCREAMING_SNAKE_CASE     (     snake_case__				,   snake_case__				,   snake_case__      )   ->	Tuple:
    __UpperCAmelCase			 =dct.pop(_lowercase      )
    __UpperCAmelCase			 =val




def 		SCREAMING_SNAKE_CASE     (     snake_case__				,   snake_case__      )   ->	Dict:
    __UpperCAmelCase			 =[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
    for i in range(len(backbone_config.depths      )      ):
        __UpperCAmelCase			 =num_features[i]
        for j in range(backbone_config.depths[i]      ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            __UpperCAmelCase			 =state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""      )
            __UpperCAmelCase			 =state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""      )
            # next, add query, keys and values (in that order) to the state dict
            __UpperCAmelCase			 =in_proj_weight[:dim, :]
            __UpperCAmelCase			 =in_proj_bias[: dim]
            __UpperCAmelCase			 =in_proj_weight[
                dim : dim * 2, :
            ]
            __UpperCAmelCase			 =in_proj_bias[
                dim : dim * 2
            ]
            __UpperCAmelCase			 =in_proj_weight[
                -dim :, :
            ]
            __UpperCAmelCase			 =in_proj_bias[-dim :]
            # fmt: on




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	Optional[int]:
    __UpperCAmelCase , __UpperCAmelCase			 =x.shape
    __UpperCAmelCase			 =x.reshape(_lowercase				,   4				,   in_channel // 4      )
    __UpperCAmelCase			 =x[:, [0, 2, 1, 3], :].transpose(1				,   2      ).reshape(_lowercase				,   _lowercase      )
    return x




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	Dict:
    __UpperCAmelCase , __UpperCAmelCase			 =x.shape
    __UpperCAmelCase			 =x.reshape(_lowercase				,   in_channel // 4				,   4      )
    __UpperCAmelCase			 =x[:, :, [0, 2, 1, 3]].transpose(1				,   2      ).reshape(_lowercase				,   _lowercase      )

    return x




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	List[Any]:
    __UpperCAmelCase			 =x.shape[0]
    __UpperCAmelCase			 =x.reshape(4				,   in_channel // 4      )
    __UpperCAmelCase			 =x[[0, 2, 1, 3], :].transpose(0				,   1      ).reshape(_lowercase      )
    return x




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	int:
    __UpperCAmelCase			 =x.shape[0]
    __UpperCAmelCase			 =x.reshape(in_channel // 4				,   4      )
    __UpperCAmelCase			 =x[:, [0, 2, 1, 3]].transpose(0				,   1      ).reshape(_lowercase      )
    return x




def 		SCREAMING_SNAKE_CASE     (     snake_case__				,   snake_case__				,   snake_case__      )   ->	int:
    __UpperCAmelCase			 ={
        '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
        '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
        '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
        '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
    }
    __UpperCAmelCase			 =model_name_to_url[model_name]
    __UpperCAmelCase			 =torch.hub.load_state_dict_from_url(_lowercase				,   map_location='''cpu'''				,   file_name=_lowercase      )[
        '''state_dict'''
    ]

    for name, param in state_dict.items():
        print(_lowercase				,   param.shape      )

    __UpperCAmelCase			 =get_upernet_config(_lowercase      )
    __UpperCAmelCase			 =UperNetForSemanticSegmentation(_lowercase      )
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        __UpperCAmelCase			 =state_dict.pop(_lowercase      )
        if "bn" in key:
            __UpperCAmelCase			 =key.replace('''bn'''				,   '''batch_norm'''      )
        __UpperCAmelCase			 =val

    # rename keys
    __UpperCAmelCase			 =create_rename_keys(_lowercase      )
    for src, dest in rename_keys:
        rename_key(_lowercase				,   _lowercase				,   _lowercase      )
    read_in_q_k_v(_lowercase				,   config.backbone_config      )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                __UpperCAmelCase			 =reverse_correct_unfold_reduction_order(_lowercase      )
            if "norm" in key:
                __UpperCAmelCase			 =reverse_correct_unfold_norm_order(_lowercase      )

    model.load_state_dict(_lowercase      )

    # verify on image
    __UpperCAmelCase			 ='''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    __UpperCAmelCase			 =Image.open(requests.get(_lowercase				,   stream=_lowercase      ).raw      ).convert('''RGB'''      )

    __UpperCAmelCase			 =SegformerImageProcessor()
    __UpperCAmelCase			 =processor(_lowercase				,   return_tensors='''pt'''      ).pixel_values

    with torch.no_grad():
        __UpperCAmelCase			 =model(_lowercase      )
        __UpperCAmelCase			 =outputs.logits

    print(logits.shape      )
    print('''First values of logits:'''				,   logits[0, 0, :3, :3]      )
    # assert values
    if model_name == "upernet-swin-tiny":
        __UpperCAmelCase			 =torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]      )
    elif model_name == "upernet-swin-small":
        __UpperCAmelCase			 =torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]      )
    elif model_name == "upernet-swin-base":
        __UpperCAmelCase			 =torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]      )
    elif model_name == "upernet-swin-large":
        __UpperCAmelCase			 =torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]      )
    print('''Logits:'''				,   outputs.logits[0, 0, :3, :3]      )
    assert torch.allclose(outputs.logits[0, 0, :3, :3]				,   _lowercase				,   atol=1e-4      )
    print('''Looks ok!'''      )

    if pytorch_dump_folder_path is not None:
        print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""      )
        model.save_pretrained(_lowercase      )
        print(f"""Saving processor to {pytorch_dump_folder_path}"""      )
        processor.save_pretrained(_lowercase      )

    if push_to_hub:
        print(f"""Pushing model and processor for {model_name} to hub"""      )
        model.push_to_hub(f"""openmmlab/{model_name}"""      )
        processor.push_to_hub(f"""openmmlab/{model_name}"""      )


if __name__ == "__main__":
       UpperCamelCase_	   =			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='upernet-swin-tiny',
           type=str,
           choices=[f'upernet-swin-{size}' for size in ['tiny', 'small', 'base', 'large']],
           help='Name of the Swin + UperNet model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       UpperCamelCase_	   =			parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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()

_snake_case      = {
    '''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					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=True			)       ->      List[Any]:
     if model_type not in MODEL_CLASSES:
          raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}.'			)

     UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					MODEL_CLASSES[model_type]

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

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

     if compare_with_pt_model:
          UpperCamelCase						    =					tf_model(tf_model.dummy_inputs						,     training=_lowercase			)  # build the network

          UpperCamelCase						    =					torch.load(_lowercase						,     map_location='cpu'			)
          UpperCamelCase						    =					pt_model_class.from_pretrained(
              pretrained_model_name_or_path=_lowercase						,     config=_lowercase						,     state_dict=_lowercase			)

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

          UpperCamelCase						    =					pto[0].numpy()
          UpperCamelCase						    =					tfo[0].numpy()
          UpperCamelCase						    =					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(_lowercase						,     save_format='h5'			)

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=False						,     _lowercase=False						,     _lowercase=False						,     _lowercase=False						,     )       ->      int:
     if args_model_type is None:
          UpperCamelCase						    =					list(MODEL_CLASSES.keys()			)
     else:
          UpperCamelCase						    =					[args_model_type]

     for j, model_type in enumerate(_lowercase						,     start=1			):
          print('=' * 100			)
          print(F' Converting model type {j}/{len(_lowercase			)}: {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()			)}.'			)

          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					MODEL_CLASSES[model_type]

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

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(_lowercase						,     _lowercase			)						,     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
                    UpperCamelCase						    =					model_shortcut_name
               elif only_convert_finetuned_models:
                    print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'			)
                    continue
               print(
                   F'    Converting checkpoint {i}/{len(_lowercase			)}: {model_shortcut_name} - model_type {model_type}'			)
               print('-' * 100			)

               if config_shortcut_name in aws_config_map:
                    UpperCamelCase						    =					cached_file(_lowercase						,     _lowercase						,     force_download=not use_cached_models			)
               else:
                    UpperCamelCase						    =					config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    UpperCamelCase						    =					cached_file(_lowercase						,     _lowercase						,     force_download=not use_cached_models			)
               else:
                    UpperCamelCase						    =					model_shortcut_name

               if os.path.isfile(_lowercase			):
                    UpperCamelCase						    =					'converted_model'

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


if __name__ == "__main__":
 _snake_case      = 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.''')
 _snake_case      = 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,
 )


