
'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__lowerCAmelCase					=			{
    '''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''',
}
__lowerCAmelCase					=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							for attribute in key.split(""".""" ):
														_snake_case  =    getattr(__snake_case				,   __snake_case )

							if weight_type is not None:
														_snake_case  =    getattr(__snake_case				,   __snake_case ).shape
							else:
														_snake_case  =    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":
														_snake_case  =    value
							elif weight_type == "weight_g":
														_snake_case  =    value
							elif weight_type == "weight_v":
														_snake_case  =    value
							elif weight_type == "bias":
														_snake_case  =    value
							else:
														_snake_case  =    value

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






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							_snake_case  =    []
							_snake_case  =    fairseq_model.state_dict()

							_snake_case  =    hf_model.feature_extractor

							# if encoder has different dim to decoder -> use proj_weight
							_snake_case  =    None

							for name, value in fairseq_dict.items():
														_snake_case  =    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __snake_case				,   __snake_case				,   __snake_case				,   __snake_case				,   hf_model.config.feat_extract_norm == """group"""				,   )
																					_snake_case  =    True
														elif name.split(""".""" )[0] == "proj":
																					_snake_case  =    fairseq_model.proj
																					_snake_case  =    True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
																																			_snake_case  =    True
																																			if "*" in mapped_key:
																																										_snake_case  =    name.split(__snake_case )[0].split(""".""" )[-2]
																																										_snake_case  =    mapped_key.replace("""*"""				,   __snake_case )
																																			if "weight_g" in name:
																																										_snake_case  =    """weight_g"""
																																			elif "weight_v" in name:
																																										_snake_case  =    """weight_v"""
																																			elif "bias" in name:
																																										_snake_case  =    """bias"""
																																			elif "weight" in name:
																																										_snake_case  =    """weight"""
																																			else:
																																										_snake_case  =    None
																																			set_recursively(__snake_case				,   __snake_case				,   __snake_case				,   __snake_case				,   __snake_case )
																												continue
														if not is_used:
																					unused_weights.append(__snake_case )

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

							return proj_weight






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							_snake_case  =    full_name.split("""conv_layers.""" )[-1]
							_snake_case  =    name.split(""".""" )
							_snake_case  =    int(items[0] )
							_snake_case  =    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."""
																					)
																					_snake_case  =    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."""
																					)
																					_snake_case  =    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."
																					)
																					_snake_case  =    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."""
																					)
																					_snake_case  =    value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
							else:
														unused_weights.append(__snake_case )






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_snake_case,				_snake_case  =    emb.weight.shape
							_snake_case  =    nn.Linear(__snake_case				,   __snake_case				,   bias=__snake_case )
							_snake_case  =    emb.weight.data
							return lin_layer






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							with open(__snake_case				,   """r"""				,   encoding="""utf-8""" ) as f:
														_snake_case  =    f.readlines()
														_snake_case  =    [line.split(""" """ )[0] for line in lines]

							_snake_case  =    len(__snake_case )

							_snake_case  =    {
							    """<s>""": 0,
							    """<pad>""": 1,
							    """</s>""": 2,
							    """<unk>""": 3,
							}

							vocab_dict.update(dict(zip(__snake_case				,   range(4				,   num_words + 4 ) ) ) )
							return vocab_dict






@torch.no_grad()
def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   ):
							_snake_case  =    WavaVecaConfig.from_pretrained(__snake_case )
							_snake_case  =    SpeechaTextaConfig.from_pretrained(
							    __snake_case				,   vocab_size=__snake_case				,   decoder_layers=__snake_case				,   do_stable_layer_norm=__snake_case )

							_snake_case  =    WavaVecaFeatureExtractor(
							    feature_size=1				,   sampling_rate=1_6000				,   padding_value=0				,   do_normalize=__snake_case				,   return_attention_mask=__snake_case				,   )

							_snake_case,				_snake_case,				_snake_case  =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]				,   arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
							_snake_case  =    model[0].eval()

							# set weights for wav2vec2 encoder
							_snake_case  =    WavaVecaModel(__snake_case )
							_snake_case  =    recursively_load_weights_wavaveca(model.encoder				,   __snake_case )

							_snake_case  =    SpeechaTextaForCausalLM(__snake_case )
							_snake_case,				_snake_case  =    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,   strict=__snake_case )

							# set output linear layer
							unexpected_keys.remove("""embed_out""" )
							_snake_case  =    nn.Parameter(model.decoder.embed_out.detach() )

							# layer norm is init to identity matrix so leaving it is fine
							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}""" )

							_snake_case  =    SpeechEncoderDecoderModel(encoder=__snake_case				,   decoder=__snake_case )
							_snake_case  =    False

							# add projection layer
							_snake_case  =    nn.Parameter(projection_layer.weight )
							_snake_case  =    nn.Parameter(projection_layer.bias )

							_snake_case  =    create_vocab_dict(__snake_case )

							with open(os.path.join(__snake_case				,   """vocab.json""" )				,   """w""" ) as fp:
														json.dump(__snake_case				,   __snake_case )

							_snake_case  =    SpeechaTextaTokenizer(os.path.join(__snake_case				,   """vocab.json""" ) )
							tokenizer.save_pretrained(__snake_case )

							_snake_case  =    hf_wavavec.config.to_dict()
							_snake_case  =    tokenizer.pad_token_id
							_snake_case  =    tokenizer.bos_token_id
							_snake_case  =    tokenizer.eos_token_id
							_snake_case  =    """speech_to_text_2"""
							_snake_case  =    """wav2vec2"""

							_snake_case  =    SpeechEncoderDecoderConfig.from_dict(__snake_case )

							hf_wavavec.save_pretrained(__snake_case )
							feature_extractor.save_pretrained(__snake_case )


if __name__ == "__main__":
				__lowerCAmelCase					=			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(
				    '--encoder_config_path',
				    default='facebook/wav2vec2-large-lv60',
				    type=str,
				    help='Path to hf encoder wav2vec2 checkpoint config',
				)
				parser.add_argument(
				    '--decoder_config_path',
				    default='facebook/s2t-small-mustc-en-fr-st',
				    type=str,
				    help='Path to hf decoder s2t checkpoint config',
				)
				parser.add_argument('--vocab_size', default=10_224, type=int, help='Vocab size of decoder')
				parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

				__lowerCAmelCase					=			parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    vocab_size=args.vocab_size,
				    num_decoder_layers=args.num_decoder_layers,
				)







import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				["""image_processor""", """tokenizer"""]
      snake_case_	=				"""FlavaImageProcessor"""
      snake_case_	=				("""BertTokenizer""", """BertTokenizerFast""")


      def __init__(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       None
           if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."       ,    __UpperCamelCase       ,    )
                __lowerCAmelCase             =       kwargs.pop("feature_extractor"						)

           __lowerCAmelCase             =       image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                raise ValueError("You need to specify an `image_processor`."						)
           if tokenizer is None:
                raise ValueError("You need to specify a `tokenizer`."						)

           super().__init__(__UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.image_processor


      def __call__(						self       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = True       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = 0       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = True       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						int:

           if text is None and images is None:
                raise ValueError("You have to specify either text or images. Both cannot be none."						)

           if text is not None:
                __lowerCAmelCase             =       self.tokenizer(
                    text=__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    stride=__UpperCamelCase       ,    pad_to_multiple_of=__UpperCamelCase       ,    return_token_type_ids=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    return_overflowing_tokens=__UpperCamelCase       ,    return_special_tokens_mask=__UpperCamelCase       ,    return_offsets_mapping=__UpperCamelCase       ,    return_length=__UpperCamelCase       ,    verbose=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           if images is not None:
                __lowerCAmelCase             =       self.image_processor(
                    __UpperCamelCase       ,    return_image_mask=__UpperCamelCase       ,    return_codebook_pixels=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )

           if text is not None and images is not None:
                encoding.update(__UpperCamelCase						)
                return encoding
           elif text is not None:
                return encoding
           else:
                return BatchEncoding(data=dict(**__UpperCamelCase						)       ,    tensor_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						int:
           return self.tokenizer.batch_decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           return self.tokenizer.decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.tokenizer.model_input_names
           __lowerCAmelCase             =       self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Dict:
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor_class


      @property
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor






