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()
__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''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''',
}
__lowercase											=      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	{}
    with open(A__						,    '''r'''			) as file:
        for line_number, line in enumerate(A__			):
            __UpperCamelCase  :List[Any]    						=	line.strip()
            if line:
                __UpperCamelCase  :Union[str, Any]    						=	line.split()
                __UpperCamelCase  :Dict    						=	line_number
                __UpperCamelCase  :Any    						=	words[0]
                __UpperCamelCase  :Optional[int]    						=	value
    return result



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    for attribute in key.split('''.'''			):
        __UpperCamelCase  :List[str]    						=	getattr(A__						,    A__			)

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

    if weight_type is not None and weight_type != "param":
        __UpperCamelCase  :Optional[Any]    						=	getattr(A__						,    A__			).shape
    elif weight_type is not None and weight_type == "param":
        __UpperCamelCase  :Optional[int]    						=	hf_pointer
        for attribute in hf_param_name.split('''.'''			):
            __UpperCamelCase  :Any    						=	getattr(A__						,    A__			)
        __UpperCamelCase  :List[Any]    						=	shape_pointer.shape

        # let's reduce dimension
        __UpperCamelCase  :List[Any]    						=	value[0]
    else:
        __UpperCamelCase  :Tuple    						=	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  :str    						=	value
    elif weight_type == "weight_g":
        __UpperCamelCase  :int    						=	value
    elif weight_type == "weight_v":
        __UpperCamelCase  :Optional[Any]    						=	value
    elif weight_type == "bias":
        __UpperCamelCase  :Optional[Any]    						=	value
    elif weight_type == "param":
        for attribute in hf_param_name.split('''.'''			):
            __UpperCamelCase  :List[str]    						=	getattr(A__						,    A__			)
        __UpperCamelCase  :List[str]    						=	value
    else:
        __UpperCamelCase  :Any    						=	value

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(A__			):
            __UpperCamelCase  :Optional[Any]    						=	PARAM_MAPPING[full_name.split('''.'''			)[-1]]
            __UpperCamelCase  :List[str]    						=	'''param'''

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

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


__lowercase											=      {
    '''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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	False
    for key, mapped_key in MAPPING.items():
        __UpperCamelCase  :str    						=	'''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  :Union[str, Any]    						=	True
            if "*" in mapped_key:
                __UpperCamelCase  :int    						=	name.split(A__			)[0].split('''.'''			)[-2]
                __UpperCamelCase  :List[Any]    						=	mapped_key.replace('''*'''						,    A__			)
            if "weight_g" in name:
                __UpperCamelCase  :Optional[Any]    						=	'''weight_g'''
            elif "weight_v" in name:
                __UpperCamelCase  :List[Any]    						=	'''weight_v'''
            elif "bias" in name:
                __UpperCamelCase  :List[str]    						=	'''bias'''
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                __UpperCamelCase  :Optional[int]    						=	'''weight'''
            else:
                __UpperCamelCase  :Union[str, Any]    						=	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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	[]
    __UpperCamelCase  :str    						=	fairseq_model.state_dict()

    __UpperCamelCase  :Optional[Any]    						=	hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase  :List[str]    						=	False
        if "conv_layers" in name:
            load_conv_layer(
                A__						,    A__						,    A__						,    A__						,    hf_model.config.feat_extract_norm == '''group'''						,    )
            __UpperCamelCase  :Optional[Any]    						=	True
        else:
            __UpperCamelCase  :List[Any]    						=	load_wavaveca_layer(A__						,    A__						,    A__			)
        if not is_used:
            unused_weights.append(A__			)

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	full_name.split('''conv_layers.'''			)[-1]
    __UpperCamelCase  :Union[str, Any]    						=	name.split('''.'''			)
    __UpperCamelCase  :int    						=	int(items[0]			)
    __UpperCamelCase  :Any    						=	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  :Optional[Any]    						=	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  :Optional[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:
            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  :int    						=	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  :int    						=	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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=True						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase  :str    						=	WavaVecaConfig.from_pretrained(A__			)
    else:
        __UpperCamelCase  :Any    						=	WavaVecaConfig()

    if is_seq_class:
        __UpperCamelCase  :Tuple    						=	read_txt_into_dict(A__			)
        __UpperCamelCase  :List[Any]    						=	idalabel
        __UpperCamelCase  :List[str]    						=	WavaVecaForSequenceClassification(A__			)
        __UpperCamelCase  :Tuple    						=	WavaVecaFeatureExtractor(
            feature_size=1						,    sampling_rate=16_000						,    padding_value=0						,    do_normalize=A__						,    return_attention_mask=A__						,    )
        feature_extractor.save_pretrained(A__			)

    elif is_finetuned:
        if dict_path:
            __UpperCamelCase  :Optional[int]    						=	Dictionary.load(A__			)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase  :Union[str, Any]    						=	target_dict.pad_index
            __UpperCamelCase  :int    						=	target_dict.bos_index
            __UpperCamelCase  :Optional[int]    						=	target_dict.eos_index
            __UpperCamelCase  :Tuple    						=	len(target_dict.symbols			)
            __UpperCamelCase  :List[str]    						=	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  :Tuple    						=	target_dict.indices

            # fairseq has the <pad> and <s> switched
            __UpperCamelCase  :Any    						=	0
            __UpperCamelCase  :Any    						=	1
            with open(A__						,    '''w'''						,    encoding='''utf-8'''			) as vocab_handle:
                json.dump(A__						,    A__			)
            __UpperCamelCase  :int    						=	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  :Any    						=	True if config.feat_extract_norm == '''layer''' else False
            __UpperCamelCase  :str    						=	WavaVecaFeatureExtractor(
                feature_size=1						,    sampling_rate=16_000						,    padding_value=0						,    do_normalize=A__						,    return_attention_mask=A__						,    )
            __UpperCamelCase  :Optional[int]    						=	WavaVecaProcessor(feature_extractor=A__						,    tokenizer=A__			)
            processor.save_pretrained(A__			)

        __UpperCamelCase  :str    						=	WavaVecaForCTC(A__			)
    else:
        __UpperCamelCase  :Optional[int]    						=	WavaVecaForPreTraining(A__			)

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

        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Tuple    						=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,    task=A__			)

    __UpperCamelCase  :List[Any]    						=	model[0].eval()

    recursively_load_weights(A__						,    A__						,    not is_finetuned			)

    hf_wavavec.save_pretrained(A__			)


if __name__ == "__main__":
    __lowercase											=      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''',
    )
    __lowercase											=      parser.parse_args()

    __lowercase											=      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 unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





