




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)

a_   		=     {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
a_   		=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						{}
    with open(__A					,     '''r'''      ) as file:
        for line_number, line in enumerate(__A      ):
            __UpperCamelCase  		=						line.strip()
            if line:
                __UpperCamelCase  		=						line.split()
                __UpperCamelCase  		=						line_number
                __UpperCamelCase  		=						words[0]
                __UpperCamelCase  		=						value
    return result




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    for attribute in key.split('''.'''      ):
        __UpperCamelCase  		=						getattr(__A					,     __A      )

    __UpperCamelCase  		=						None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(__A      ):
            __UpperCamelCase  		=						PARAM_MAPPING[full_name.split('''.'''      )[-1]]
            __UpperCamelCase  		=						'''param'''

    if weight_type is not None and weight_type != "param":
        __UpperCamelCase  		=						getattr(__A					,     __A      ).shape
    elif weight_type is not None and weight_type == "param":
        __UpperCamelCase  		=						hf_pointer
        for attribute in hf_param_name.split('''.'''      ):
            __UpperCamelCase  		=						getattr(__A					,     __A      )
        __UpperCamelCase  		=						shape_pointer.shape

        # let's reduce dimension
        __UpperCamelCase  		=						value[0]
    else:
        __UpperCamelCase  		=						hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
            F" {value.shape} for {full_name}"      )

    if weight_type == "weight":
        __UpperCamelCase  		=						value
    elif weight_type == "weight_g":
        __UpperCamelCase  		=						value
    elif weight_type == "weight_v":
        __UpperCamelCase  		=						value
    elif weight_type == "bias":
        __UpperCamelCase  		=						value
    elif weight_type == "param":
        for attribute in hf_param_name.split('''.'''      ):
            __UpperCamelCase  		=						getattr(__A					,     __A      )
        __UpperCamelCase  		=						value
    else:
        __UpperCamelCase  		=						value

    logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(__A      ):
            __UpperCamelCase  		=						PARAM_MAPPING[full_name.split('''.'''      )[-1]]
            __UpperCamelCase  		=						'''param'''

    if weight_type is not None and weight_type != "param":
        __UpperCamelCase  		=						'''.'''.join([key, weight_type]      )
    elif weight_type is not None and weight_type == "param":
        __UpperCamelCase  		=						'''.'''.join([key, hf_param_name]      )
    else:
        __UpperCamelCase  		=						key

    __UpperCamelCase  		=						value if '''lm_head''' in full_key else value[0]


a_   		=     {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None					,     lowercase_=None      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						False
    for key, mapped_key in MAPPING.items():
        __UpperCamelCase  		=						'''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
            __UpperCamelCase  		=						True
            if "*" in mapped_key:
                __UpperCamelCase  		=						name.split(__A      )[0].split('''.'''      )[-2]
                __UpperCamelCase  		=						mapped_key.replace('''*'''					,     __A      )
            if "weight_g" in name:
                __UpperCamelCase  		=						'''weight_g'''
            elif "weight_v" in name:
                __UpperCamelCase  		=						'''weight_v'''
            elif "bias" in name:
                __UpperCamelCase  		=						'''bias'''
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                __UpperCamelCase  		=						'''weight'''
            else:
                __UpperCamelCase  		=						None
            if hf_dict is not None:
                rename_dict(__A					,     __A					,     __A					,     __A					,     __A      )
            else:
                set_recursively(__A					,     __A					,     __A					,     __A					,     __A      )
            return is_used
    return is_used




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						fairseq_model.state_dict()

    __UpperCamelCase  		=						hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase  		=						False
        if "conv_layers" in name:
            load_conv_layer(
                __A					,     __A					,     __A					,     __A					,     hf_model.config.feat_extract_norm == '''group'''					,     )
            __UpperCamelCase  		=						True
        else:
            __UpperCamelCase  		=						load_wavaveca_layer(__A					,     __A					,     __A      )
        if not is_used:
            unused_weights.append(__A      )

    logger.warning(F"Unused weights: {unused_weights}"      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						full_name.split('''conv_layers.'''      )[-1]
    __UpperCamelCase  		=						name.split('''.'''      )
    __UpperCamelCase  		=						int(items[0]      )
    __UpperCamelCase  		=						int(items[1]      )

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."      )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."      )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."      )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."      )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(__A      )




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_=True					,     lowercase_=False      )       ->	Union[str, Any]:

    """simple docstring"""


    if config_path is not None:
        __UpperCamelCase  		=						WavaVecaConfig.from_pretrained(__A      )
    else:
        __UpperCamelCase  		=						WavaVecaConfig()

    if is_seq_class:
        __UpperCamelCase  		=						read_txt_into_dict(__A      )
        __UpperCamelCase  		=						idalabel
        __UpperCamelCase  		=						WavaVecaForSequenceClassification(__A      )
        __UpperCamelCase  		=						WavaVecaFeatureExtractor(
            feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=__A					,     return_attention_mask=__A					,     )
        feature_extractor.save_pretrained(__A      )

    elif is_finetuned:
        if dict_path:
            __UpperCamelCase  		=						Dictionary.load(__A      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase  		=						target_dict.pad_index
            __UpperCamelCase  		=						target_dict.bos_index
            __UpperCamelCase  		=						target_dict.eos_index
            __UpperCamelCase  		=						len(target_dict.symbols      )
            __UpperCamelCase  		=						os.path.join(__A					,     '''vocab.json'''      )
            if not os.path.isdir(__A      ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__A      )      )
                return
            os.makedirs(__A					,     exist_ok=__A      )
            __UpperCamelCase  		=						target_dict.indices

            # fairseq has the <pad> and <s> switched
            __UpperCamelCase  		=						0
            __UpperCamelCase  		=						1
            with open(__A					,     '''w'''					,     encoding='''utf-8'''      ) as vocab_handle:
                json.dump(__A					,     __A      )
            __UpperCamelCase  		=						WavaVecaCTCTokenizer(
                __A					,     unk_token=target_dict.unk_word					,     pad_token=target_dict.pad_word					,     bos_token=target_dict.bos_word					,     eos_token=target_dict.eos_word					,     word_delimiter_token='''|'''					,     do_lower_case=__A					,     )
            __UpperCamelCase  		=						True if config.feat_extract_norm == '''layer''' else False
            __UpperCamelCase  		=						WavaVecaFeatureExtractor(
                feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=__A					,     return_attention_mask=__A					,     )
            __UpperCamelCase  		=						WavaVecaProcessor(feature_extractor=__A					,     tokenizer=__A      )
            processor.save_pretrained(__A      )

        __UpperCamelCase  		=						WavaVecaForCTC(__A      )
    else:
        __UpperCamelCase  		=						WavaVecaForPreTraining(__A      )

    if is_finetuned or is_seq_class:
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]					,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
    else:
        __UpperCamelCase  		=						argparse.Namespace(task='''audio_pretraining'''      )
        __UpperCamelCase  		=						fairseq.tasks.setup_task(__A      )

        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,     task=__A      )

    __UpperCamelCase  		=						model[0].eval()

    recursively_load_weights(__A					,     __A					,     not is_finetuned      )

    hf_wavavec.save_pretrained(__A      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 parser.add_argument(
     "--is_seq_class",
     action="store_true",
     help="Whether the model to convert is a fine-tuned sequence classification model or not",
 )
 a_   		=     parser.parse_args()

 a_   		=     not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )







import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						model.config

    __UpperCamelCase  		=						DonutSwinConfig(
        image_size=original_config.input_size					,     patch_size=4					,     depths=original_config.encoder_layer					,     num_heads=[4, 8, 16, 32]					,     window_size=original_config.window_size					,     embed_dim=1_28					,     )
    __UpperCamelCase  		=						MBartConfig(
        is_decoder=lowercase_					,     is_encoder_decoder=lowercase_					,     add_cross_attention=lowercase_					,     decoder_layers=original_config.decoder_layer					,     max_position_embeddings=original_config.max_position_embeddings					,     vocab_size=len(
            model.decoder.tokenizer      )					,     scale_embedding=lowercase_					,     add_final_layer_norm=lowercase_					,     )

    return encoder_config, decoder_config




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


    if "encoder.model" in name:
        __UpperCamelCase  		=						name.replace('''encoder.model'''					,     '''encoder'''      )
    if "decoder.model" in name:
        __UpperCamelCase  		=						name.replace('''decoder.model'''					,     '''decoder'''      )
    if "patch_embed.proj" in name:
        __UpperCamelCase  		=						name.replace('''patch_embed.proj'''					,     '''embeddings.patch_embeddings.projection'''      )
    if "patch_embed.norm" in name:
        __UpperCamelCase  		=						name.replace('''patch_embed.norm'''					,     '''embeddings.norm'''      )
    if name.startswith('''encoder'''      ):
        if "layers" in name:
            __UpperCamelCase  		=						'''encoder.''' + name
        if "attn.proj" in name:
            __UpperCamelCase  		=						name.replace('''attn.proj'''					,     '''attention.output.dense'''      )
        if "attn" in name and "mask" not in name:
            __UpperCamelCase  		=						name.replace('''attn'''					,     '''attention.self'''      )
        if "norm1" in name:
            __UpperCamelCase  		=						name.replace('''norm1'''					,     '''layernorm_before'''      )
        if "norm2" in name:
            __UpperCamelCase  		=						name.replace('''norm2'''					,     '''layernorm_after'''      )
        if "mlp.fc1" in name:
            __UpperCamelCase  		=						name.replace('''mlp.fc1'''					,     '''intermediate.dense'''      )
        if "mlp.fc2" in name:
            __UpperCamelCase  		=						name.replace('''mlp.fc2'''					,     '''output.dense'''      )

        if name == "encoder.norm.weight":
            __UpperCamelCase  		=						'''encoder.layernorm.weight'''
        if name == "encoder.norm.bias":
            __UpperCamelCase  		=						'''encoder.layernorm.bias'''

    return name




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase  		=						orig_state_dict.pop(lowercase_      )

        if "qkv" in key:
            __UpperCamelCase  		=						key.split('''.'''      )
            __UpperCamelCase  		=						int(key_split[3]      )
            __UpperCamelCase  		=						int(key_split[5]      )
            __UpperCamelCase  		=						model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                __UpperCamelCase  		=						val[:dim, :]
                __UpperCamelCase  		=						val[dim : dim * 2, :]
                __UpperCamelCase  		=						val[-dim:, :]
            else:
                __UpperCamelCase  		=						val[:dim]
                __UpperCamelCase  		=						val[dim : dim * 2]
                __UpperCamelCase  		=						val[-dim:]
        elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
            # HuggingFace implementation doesn't use attn_mask buffer
            # and model doesn't use final LayerNorms for the encoder
            pass
        else:
            __UpperCamelCase  		=						val

    return orig_state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=None					,     lowercase_=False      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						DonutModel.from_pretrained(lowercase_      ).eval()

    # load HuggingFace model
    __UpperCamelCase		,      __UpperCamelCase  		=						get_configs(lowercase_      )
    __UpperCamelCase  		=						DonutSwinModel(lowercase_      )
    __UpperCamelCase  		=						MBartForCausalLM(lowercase_      )
    __UpperCamelCase  		=						VisionEncoderDecoderModel(encoder=lowercase_					,     decoder=lowercase_      )
    model.eval()

    __UpperCamelCase  		=						original_model.state_dict()
    __UpperCamelCase  		=						convert_state_dict(lowercase_					,     lowercase_      )
    model.load_state_dict(lowercase_      )

    # verify results on scanned document
    __UpperCamelCase  		=						load_dataset('''hf-internal-testing/example-documents'''      )
    __UpperCamelCase  		=						dataset['''test'''][0]['''image'''].convert('''RGB'''      )

    __UpperCamelCase  		=						XLMRobertaTokenizerFast.from_pretrained(lowercase_					,     from_slow=lowercase_      )
    __UpperCamelCase  		=						DonutImageProcessor(
        do_align_long_axis=original_model.config.align_long_axis					,     size=original_model.config.input_size[::-1]      )
    __UpperCamelCase  		=						DonutProcessor(lowercase_					,     lowercase_      )
    __UpperCamelCase  		=						processor(lowercase_					,     return_tensors='''pt'''      ).pixel_values

    if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
        __UpperCamelCase  		=						'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
        __UpperCamelCase  		=						'''When is the coffee break?'''
        __UpperCamelCase  		=						task_prompt.replace('''{user_input}'''					,     lowercase_      )
    elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
        __UpperCamelCase  		=						'''<s_rvlcdip>'''
    elif model_name in [
        "naver-clova-ix/donut-base-finetuned-cord-v1",
        "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
    ]:
        __UpperCamelCase  		=						'''<s_cord>'''
    elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
        __UpperCamelCase  		=						'''s_cord-v2>'''
    elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
        __UpperCamelCase  		=						'''<s_zhtrainticket>'''
    elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
        # use a random prompt
        __UpperCamelCase  		=						'''hello world'''
    else:
        raise ValueError('''Model name not supported'''      )
    __UpperCamelCase  		=						original_model.decoder.tokenizer(lowercase_					,     add_special_tokens=lowercase_					,     return_tensors='''pt'''      )[
        '''input_ids'''
    ]

    __UpperCamelCase  		=						original_model.encoder.model.patch_embed(lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						model.encoder.embeddings(lowercase_      )
    assert torch.allclose(lowercase_					,     lowercase_					,     atol=1E-3      )

    # verify encoder hidden states
    __UpperCamelCase  		=						original_model.encoder(lowercase_      )
    __UpperCamelCase  		=						model.encoder(lowercase_      ).last_hidden_state
    assert torch.allclose(lowercase_					,     lowercase_					,     atol=1E-2      )

    # verify decoder hidden states
    __UpperCamelCase  		=						original_model(lowercase_					,     lowercase_					,     lowercase_      ).logits
    __UpperCamelCase  		=						model(lowercase_					,     decoder_input_ids=lowercase_      ).logits
    assert torch.allclose(lowercase_					,     lowercase_					,     atol=1E-3      )
    print('''Looks ok!'''      )

    if pytorch_dump_folder_path is not None:
        print(F"Saving model and processor to {pytorch_dump_folder_path}"      )
        model.save_pretrained(lowercase_      )
        processor.save_pretrained(lowercase_      )

    if push_to_hub:
        model.push_to_hub('''nielsr/''' + model_name.split('''/'''      )[-1]					,     commit_message='''Update model'''      )
        processor.push_to_hub('''nielsr/''' + model_name.split('''/'''      )[-1]					,     commit_message='''Update model'''      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="naver-clova-ix/donut-base-finetuned-docvqa",
     required=False,
     type=str,
     help="Name of the original model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     required=False,
     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 and processor to the 🤗 hub.",
 )

 a_   		=     parser.parse_args()
 convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






