



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:List[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:List[str]					=						{
    "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",
    "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",
}
SCREAMING_SNAKE_CASE_:int					=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    for attribute in key.split("""."""		):
        A			:	str										=       getattr(lowerCAmelCase__				,					lowerCAmelCase__		)

    if weight_type is not None:
        A			:	List[str]										=       getattr(lowerCAmelCase__				,					lowerCAmelCase__		).shape
    else:
        A			:	List[Any]										=       hf_pointer.shape

    assert hf_shape == value.shape, (
        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":
        A			:	List[Any]										=       value
    elif weight_type == "weight_g":
        A			:	List[str]										=       value
    elif weight_type == "weight_v":
        A			:	Tuple										=       value
    elif weight_type == "bias":
        A			:	List[Any]										=       value
    else:
        A			:	Union[str, Any]										=       value

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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	Optional[int]										=       []
    A			:	int										=       fairseq_model.state_dict()

    A			:	Tuple										=       hf_model.feature_extractor
    A			:	Dict										=       hf_model.adapter

    for name, value in fairseq_dict.items():
        A			:	List[str]										=       False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__				,					hf_model.config.feat_extract_norm == """group"""				,					)
            A			:	Optional[Any]										=       True
        elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]		):
            load_adapter(lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__		)
            A			:	Dict										=       True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                    A			:	Union[str, Any]										=       True
                    if "*" in mapped_key:
                        A			:	List[str]										=       name.split(lowerCAmelCase__		)[0].split("""."""		)[-2]
                        A			:	int										=       mapped_key.replace("""*"""				,					lowerCAmelCase__		)
                    if "weight_g" in name:
                        A			:	int										=       """weight_g"""
                    elif "weight_v" in name:
                        A			:	str										=       """weight_v"""
                    elif "bias" in name:
                        A			:	int										=       """bias"""
                    elif "weight" in name:
                        A			:	Optional[Any]										=       """weight"""
                    else:
                        A			:	Tuple										=       None
                    set_recursively(lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__		)
                continue
        if not is_used:
            unused_weights.append(lowerCAmelCase__		)

    logger.warning(f'''Unused weights: {unused_weights}'''		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	List[str]										=       full_name.split("""conv_layers."""		)[-1]
    A			:	int										=       name.split("""."""		)
    A			:	Optional[int]										=       int(items[0]		)
    A			:	int										=       int(items[1]		)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
            )
            A			:	Tuple										=       value
            logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
            )
            A			:	Union[str, Any]										=       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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                " found."
            )
            A			:	Union[str, Any]										=       value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
            )
            A			:	Union[str, Any]										=       value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
    else:
        unused_weights.append(lowerCAmelCase__		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  List[Any]:



    """simple docstring"""






    A			:	Optional[Any]										=       full_name.split("""adaptor."""		)[-1]
    A			:	List[str]										=       name.split("""."""		)

    if items[1].isdigit():
        A			:	Any										=       int(items[1]		)
    else:
        A			:	List[Any]										=       None

    if "adaptor" not in full_name:
        if "proj_ln" in full_name:
            # has to be layer norm
            if "bias" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.bias.data.shape
                ), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                A			:	Dict										=       value
                logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.'''		)
            if "weight" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.weight.data.shape
                ), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                A			:	Tuple										=       value
        else:
            # has to be projection layer
            if "bias" in name:
                assert (
                    value.shape == adapter.proj.bias.data.shape
                ), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                A			:	Tuple										=       value
                logger.info(f'''Adapter proj layer bias was initialized from {full_name}.'''		)
            if "weight" in name:
                assert (
                    value.shape == adapter.proj.weight.data.shape
                ), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                A			:	str										=       value
                logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''		)
    elif isinstance(lowerCAmelCase__				,					lowerCAmelCase__		):
        if "bias" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.bias.data.shape
            ), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
            A			:	Optional[Any]										=       value
            logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
        elif "weight" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.weight.data.shape
            ), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
            A			:	str										=       value
            logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
    else:
        unused_weights.append(lowerCAmelCase__		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A					,							A			:	Any										=       emb.weight.shape
    A			:	List[str]										=       nn.Linear(lowerCAmelCase__				,					lowerCAmelCase__				,					bias=lowerCAmelCase__		)
    A			:	Union[str, Any]										=       emb.weight.data
    return lin_layer




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					)	->  Optional[Any]:



    """simple docstring"""






    A			:	int										=       WavaVecaConfig.from_pretrained(
        lowerCAmelCase__				,					add_adapter=lowerCAmelCase__				,					adapter_stride=lowerCAmelCase__				,					adapter_kernel_size=lowerCAmelCase__				,					use_auth_token=lowerCAmelCase__				,					output_hidden_size=lowerCAmelCase__				,					)
    A			:	int										=       MBartConfig.from_pretrained(lowerCAmelCase__		)

    # load model
    A					,							A					,							A			:	Optional[Any]										=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]				,					arg_overrides={
            """config_yaml""": config_yaml_path,
            """data""": """/""".join(dict_path.split("""/"""		)[:-1]		),
            """w2v_path""": checkpoint_path,
            """load_pretrained_decoder_from""": None,
        }				,					)
    A			:	int										=       model[0].eval()

    # load feature extractor
    A			:	Dict										=       WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__				,					use_auth_token=lowerCAmelCase__		)

    # set weights for wav2vec2 encoder
    A			:	Optional[Any]										=       WavaVecaModel(lowerCAmelCase__		)

    recursively_load_weights_wavaveca(model.encoder				,					lowerCAmelCase__		)

    # load decoder weights
    A			:	int										=       MBartForCausalLM(lowerCAmelCase__		)
    A					,							A			:	Tuple										=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,					strict=lowerCAmelCase__		)
    logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''		)
    logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''		)

    A			:	str										=       SpeechEncoderDecoderModel(encoder=lowerCAmelCase__				,					decoder=lowerCAmelCase__		)
    A			:	Any										=       False

    A			:	List[str]										=       MBartaaTokenizer(lowerCAmelCase__		)
    tokenizer.save_pretrained(lowerCAmelCase__		)

    A			:	int										=       hf_wavavec.config.to_dict()
    A			:	List[Any]										=       tokenizer.pad_token_id
    A			:	Optional[Any]										=       tokenizer.bos_token_id
    A			:	Optional[Any]										=       tokenizer.eos_token_id
    A			:	Union[str, Any]										=       """mbart50"""
    A			:	str										=       """wav2vec2"""

    A			:	List[str]										=       tokenizer.eos_token_id
    A			:	int										=       25_0004
    A			:	List[Any]										=       tokenizer.eos_token_id

    A			:	Any										=       SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase__		)

    hf_wavavec.save_pretrained(lowerCAmelCase__		)
    feature_extractor.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE_:Union[str, Any]					=						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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
       parser.add_argument(
           """--encoder_config_path""",
           default="""facebook/wav2vec2-xls-r-1b""",
           type=str,
           help="""Path to hf encoder wav2vec2 checkpoint config""",
       )
       parser.add_argument(
           """--decoder_config_path""",
           default="""facebook/mbart-large-50-one-to-many-mmt""",
           type=str,
           help="""Path to hf decoder checkpoint config""",
       )
       parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
       parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
       parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
       parser.add_argument("""--encoder_output_dim""", default=1_024, type=int, help="""encoder output dim""")
       parser.add_argument("""--start_token_id""", default=250_004, type=int, help="""`decoder_start_token_id` of model config""")

       SCREAMING_SNAKE_CASE_:Dict					=						parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           args.config_yaml_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           add_adapter=args.add_adapter,
           adapter_kernel_size=args.adapter_kernel_size,
           adapter_stride=args.adapter_stride,
           decoder_start_token_id=args.start_token_id,
           encoder_output_dim=args.encoder_output_dim,
       )







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:Dict					=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:int					=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE_:Tuple					=						{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""facebook/blenderbot_small-90M""": 512}




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Optional[int]										=       set()
    A			:	Any										=       word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A			:	List[Any]										=       char

    A			:	Optional[int]										=       set(_lowerCAmelCase		)
    return pairs






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Union[str, Any] =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			Any =				PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Optional[Any] =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			int =				["input_ids", "attention_mask"]






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__="__start__",		lowerCamelCase__="__end__",		lowerCamelCase__="__unk__",		lowerCamelCase__="__null__",		**lowerCamelCase__,		):
        super().__init__(unk_token=lowerCamelCase__,		bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		**lowerCamelCase__		)

        with open(lowerCamelCase__,		encoding="""utf-8"""		) as vocab_handle:
            A			:	Tuple										=       json.load(lowerCamelCase__		)
        A			:	Optional[Any]										=       {v: k for k, v in self.encoder.items()}
        with open(lowerCamelCase__,		encoding="""utf-8"""		) as merges_handle:
            A			:	str										=       merges_handle.read().split("""\n"""		)[1:-1]
        A			:	List[str]										=       [tuple(merge.split()		) for merge in merges]
        A			:	int										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[int]										=       {}






    @property
    def       _lowerCAmelCase (	self		):
        return len(self.encoder		)






    def       _lowerCAmelCase (	self		):
        return dict(self.encoder,		**self.added_tokens_encoder		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if token in self.cache:
            return self.cache[token]
        A			:	Optional[int]										=       re.sub("""([.,!?()])""",		R""" \1""",		lowerCamelCase__		)
        A			:	List[Any]										=       re.sub("""(')""",		R""" \1 """,		lowerCamelCase__		)
        A			:	int										=       re.sub(R"""\s{2,}""",		""" """,		lowerCamelCase__		)
        if "\n" in token:
            A			:	Dict										=       token.replace("""\n""",		""" __newln__"""		)

        A			:	Tuple										=       token.split(""" """		)
        A			:	Union[str, Any]										=       []
        for token in tokens:
            if not len(lowerCamelCase__		):
                continue

            A			:	Optional[int]										=       token.lower()
            A			:	Optional[Any]										=       tuple(lowerCamelCase__		)
            A			:	int										=       tuple(list(word[:-1]		) + [word[-1] + """</w>"""]		)
            A			:	Tuple										=       get_pairs(lowerCamelCase__		)

            if not pairs:
                words.append(lowerCamelCase__		)
                continue

            while True:
                A			:	Any										=       min(lowerCamelCase__,		key=lambda lowerCamelCase__		: self.bpe_ranks.get(lowerCamelCase__,		float("""inf"""		)		)		)
                if bigram not in self.bpe_ranks:
                    break
                A					,							A			:	Any										=       bigram
                A			:	Optional[Any]										=       []
                A			:	Any										=       0

                while i < len(lowerCamelCase__		):
                    try:
                        A			:	List[str]										=       word.index(lowerCamelCase__,		lowerCamelCase__		)
                        new_word.extend(word[i:j]		)
                        A			:	Dict										=       j
                    except ValueError:
                        new_word.extend(word[i:]		)
                        break

                    if word[i] == first and i < len(lowerCamelCase__		) - 1 and word[i + 1] == second:
                        new_word.append(first + second		)
                        i += 2
                    else:
                        new_word.append(word[i]		)
                        i += 1
                A			:	Tuple										=       tuple(lowerCamelCase__		)
                A			:	int										=       new_word
                if len(lowerCamelCase__		) == 1:
                    break
                else:
                    A			:	Dict										=       get_pairs(lowerCamelCase__		)
            A			:	Any										=       """@@ """.join(lowerCamelCase__		)
            A			:	Dict										=       word[:-4]

            A			:	Union[str, Any]										=       word
            words.append(lowerCamelCase__		)
        return " ".join(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	int										=       []

        A			:	Optional[Any]										=       re.findall(R"""\S+\n?""",		lowerCamelCase__		)

        for token in words:
            split_tokens.extend(list(self.bpe(lowerCamelCase__		).split(""" """		)		)		)
        return split_tokens






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	List[Any]										=       token.lower()
        return self.encoder.get(lowerCamelCase__,		self.encoder.get(self.unk_token		)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.decoder.get(lowerCamelCase__,		self.unk_token		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Dict										=       """ """.join(lowerCamelCase__		).replace("""@@ """,		""""""		).strip()
        return out_string






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A			:	str										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
        A			:	int										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]		)

        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as f:
            f.write(json.dumps(self.encoder,		indent=2,		sort_keys=lowerCamelCase__,		ensure_ascii=lowerCamelCase__		) + """\n"""		)

        A			:	str										=       0
        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as writer:
            writer.write("""#version: 0.2\n"""		)
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),		key=lambda lowerCamelCase__		: kv[1]		):
                if index != token_index:
                    logger.warning(
                        f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                        """ Please check that the tokenizer is not corrupted!"""		)
                    A			:	List[Any]										=       token_index
                writer.write(""" """.join(lowerCamelCase__		) + """\n"""		)
                index += 1

        return vocab_file, merge_file


