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()
SCREAMING_SNAKE_CASE			:						Tuple				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """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 	__A		(      _A				,					_A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 for attribute in key.split("."       ):
  __a							=	getattr(_UpperCAmelCase				,					_UpperCAmelCase       )

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

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



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	[]
 __a							=	fairseq_model.state_dict()

 __a							=	hf_model.feature_extractor

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

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

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

 return proj_weight



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


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



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	emb.weight.shape
 __a							=	nn.Linear(_UpperCAmelCase				,					_UpperCAmelCase				,					bias=_UpperCAmelCase       )
 __a							=	emb.weight.data
 return lin_layer



def 	__A		(      _A       ):



 """simple docstring"""


 with open(_UpperCAmelCase				,					"r"				,					encoding="utf-8"       ) as f:
  __a							=	f.readlines()
  __a							=	[line.split(" "       )[0] for line in lines]

 __a							=	len(_UpperCAmelCase       )

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

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



@torch.no_grad()
def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A				,					_A				,					_A				,					):



 """simple docstring"""


 __a							=	WavaVecaConfig.from_pretrained(_UpperCAmelCase       )
 __a							=	SpeechaTextaConfig.from_pretrained(
     _UpperCAmelCase				,					vocab_size=_UpperCAmelCase				,					decoder_layers=_UpperCAmelCase				,					do_stable_layer_norm=_UpperCAmelCase       )

 __a							=	WavaVecaFeatureExtractor(
     feature_size=1				,					sampling_rate=1_6000				,					padding_value=0				,					do_normalize=_UpperCAmelCase				,					return_attention_mask=_UpperCAmelCase				,					)

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

 # set weights for wav2vec2 encoder
 __a							=	WavaVecaModel(_UpperCAmelCase       )
 __a							=	recursively_load_weights_wavaveca(model.encoder				,					_UpperCAmelCase       )

 __a							=	SpeechaTextaForCausalLM(_UpperCAmelCase       )
 __a							=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,					strict=_UpperCAmelCase       )

 # set output linear layer
 unexpected_keys.remove("embed_out"       )
 __a							=	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}"""       )

 __a							=	SpeechEncoderDecoderModel(encoder=_UpperCAmelCase				,					decoder=_UpperCAmelCase       )
 __a							=	False

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

 __a							=	create_vocab_dict(_UpperCAmelCase       )

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

 __a							=	SpeechaTextaTokenizer(os.path.join(_UpperCAmelCase				,					"vocab.json"       )       )
 tokenizer.save_pretrained(_UpperCAmelCase       )

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

 __a							=	SpeechEncoderDecoderConfig.from_dict(_UpperCAmelCase       )

 hf_wavavec.save_pretrained(_UpperCAmelCase       )
 feature_extractor.save_pretrained(_UpperCAmelCase       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						List[str]				  =		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=1_0224, type=int, help="""Vocab size of decoder""")
  parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

  SCREAMING_SNAKE_CASE			:						Dict				  =		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 numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class   __lowercase						(						__snake_case       ):






	def __init__(self				: List[Any]      ,	snake_case				: Optional[int]      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	Tuple           =   dataset
							_lowercase       :	List[str]           =   process
							_lowercase       :	Any           =   params






	def __len__(self				: Optional[Any]    ) ->			Any:
							return len(self.dataset    )






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			int:
							_lowercase       :	Optional[Any]           =   self.dataset[i]
							_lowercase       :	Union[str, Any]           =   self.process(snake_case      ,	**self.params    )
							return processed




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: List[Any]      ,	snake_case				: Tuple      ,	snake_case				: Tuple      ,	snake_case				: List[str]=None    ) ->			Optional[int]:
							_lowercase       :	List[str]           =   loader
							_lowercase       :	Optional[Any]           =   infer
							_lowercase       :	List[Any]           =   params
							if loader_batch_size == 1:
													# Let's spare some time by deactivating altogether
													_lowercase       :	str           =   None
							_lowercase       :	Optional[int]           =   loader_batch_size

							# Internal bookkeeping
							_lowercase       :	Dict           =   None
							_lowercase       :	Union[str, Any]           =   None






	def __len__(self				: Any    ) ->			Any:
							return len(self.loader    )






	def __iter__(self				: int    ) ->			Optional[Any]:
							_lowercase       :	List[str]           =   iter(self.loader    )
							return self






	def       _a(self				: int    ) ->			List[str]:
							if isinstance(self._loader_batch_data      ,	torch.Tensor    ):
													# Batch data is simple tensor, just fetch the slice
													_lowercase       :	Any           =   self._loader_batch_data[self._loader_batch_index]
							else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													_lowercase       :	List[str]           =   {}
													for k, element in self._loader_batch_data.items():
																			if isinstance(snake_case      ,	snake_case    ):
																									# Convert ModelOutput to tuple first
																									_lowercase       :	Dict           =   element.to_tuple()
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	Optional[Any]           =   tuple(np.expand_dims(el[self._loader_batch_index]      ,	0    ) for el in element    )
																									continue
																			if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case      ,	snake_case    ):
																									# Those are stored as lists of tensors so need specific unbatching.
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	List[str]           =   tuple(np.expand_dims(el[self._loader_batch_index]      ,	0    ) for el in element    )
																									continue
																			if element is None:
																									# This can happen for optional data that get passed around
																									_lowercase       :	Optional[Any]           =   None
																			elif isinstance(element[self._loader_batch_index]      ,	torch.Tensor    ):
																									# Take correct batch data, but make it looked like batch_size=1
																									# For compatibility with other methods within transformers

																									_lowercase       :	int           =   element[self._loader_batch_index].unsqueeze(0    )
																			elif isinstance(element[self._loader_batch_index]      ,	np.ndarray    ):
																									# Take correct batch data, but make it looked like batch_size=1
																									# For compatibility with other methods within transformers
																									_lowercase       :	Tuple           =   np.expand_dims(element[self._loader_batch_index]      ,	0    )
																			else:
																									# This is typically a list, so no need to `unsqueeze`.
																									_lowercase       :	Union[str, Any]           =   element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
													_lowercase       :	int           =   self._loader_batch_data.__class__(snake_case    )
							self._loader_batch_index += 1
							return result






	def       _a(self				: Any    ) ->			str:
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													# We are currently unrolling a batch so we just need to return
													# the current item within a batch
													return self.loader_batch_item()

							# We're out of items within a batch
							_lowercase       :	str           =   next(self.iterator    )
							_lowercase       :	Any           =   self.infer(snake_case      ,	**self.params    )
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
													# Try to infer the size of the batch
													if isinstance(snake_case      ,	torch.Tensor    ):
																			_lowercase       :	int           =   processed
													else:
																			_lowercase       :	List[str]           =   list(processed.keys()    )[0]
																			_lowercase       :	List[str]           =   processed[key]
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	int           =   len(snake_case    )
													else:
																			_lowercase       :	Optional[int]           =   first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																			# could be last batch so we can't unroll as many
																			# elements.
																			_lowercase       :	Union[str, Any]           =   observed_batch_size
													# Setting internal index to unwrap the batch
													_lowercase       :	Any           =   processed
													_lowercase       :	Dict           =   0
													return self.loader_batch_item()
							else:
													# We're not unrolling batches
													return processed




class   __lowercase						(						__snake_case       ):






	def __init__(self				: Union[str, Any]      ,	snake_case				: str      ,	snake_case				: Dict      ,	snake_case				: Optional[Any]      ,	snake_case				: List[str]=None    ) ->			List[str]:
							super().__init__(snake_case      ,	snake_case      ,	snake_case    )






	def __iter__(self				: List[Any]    ) ->			str:
							_lowercase       :	int           =   iter(self.loader    )
							_lowercase       :	Optional[int]           =   None
							return self






	def       _a(self				: Optional[Any]    ) ->			Optional[int]:
							if self.subiterator is None:
													_lowercase       :	List[str]           =   self.infer(next(self.iterator    )      ,	**self.params    )
							try:
													# Try to return next item
													_lowercase       :	str           =   next(self.subiterator    )
							except StopIteration:
													# When a preprocess iterator ends, we can start lookig at the next item
													# ChunkIterator will keep feeding until ALL elements of iterator
													# all have created their subiterator and have been iterating against.
													#
													# Another way to look at it, is we're basically flattening lists of lists
													# into a single list, but with generators
													_lowercase       :	Any           =   self.infer(next(self.iterator    )      ,	**self.params    )
													_lowercase       :	Optional[int]           =   next(self.subiterator    )
							return processed




class   __lowercase						(						__snake_case       ):






	def __iter__(self				: List[str]    ) ->			List[str]:
							_lowercase       :	Any           =   iter(self.loader    )
							return self






	def       _a(self				: List[Any]    ) ->			Union[str, Any]:
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							_lowercase       :	Dict           =   False
							_lowercase       :	Optional[int]           =   []
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													while self._loader_batch_index < self.loader_batch_size:
																			_lowercase       :	Tuple           =   self.loader_batch_item()
																			_lowercase       :	List[Any]           =   item.pop("is_last"    )
																			accumulator.append(snake_case    )
																			if is_last:
																									return accumulator

							while not is_last:
													_lowercase       :	List[Any]           =   self.infer(next(self.iterator    )      ,	**self.params    )
													if self.loader_batch_size is not None:
																			if isinstance(snake_case      ,	torch.Tensor    ):
																									_lowercase       :	Optional[Any]           =   processed
																			else:
																									_lowercase       :	Tuple           =   list(processed.keys()    )[0]
																									_lowercase       :	int           =   processed[key]
																			if isinstance(snake_case      ,	snake_case    ):
																									_lowercase       :	str           =   len(snake_case    )
																			else:
																									_lowercase       :	List[str]           =   first_tensor.shape[0]
																			if 0 < observed_batch_size < self.loader_batch_size:
																									# could be last batch so we can't unroll as many
																									# elements.
																									_lowercase       :	Tuple           =   observed_batch_size
																			_lowercase       :	Any           =   processed
																			_lowercase       :	Dict           =   0
																			while self._loader_batch_index < self.loader_batch_size:
																									_lowercase       :	str           =   self.loader_batch_item()
																									_lowercase       :	int           =   item.pop("is_last"    )
																									accumulator.append(snake_case    )
																									if is_last:
																															return accumulator
													else:
																			_lowercase       :	str           =   processed
																			_lowercase       :	int           =   item.pop("is_last"    )
																			accumulator.append(snake_case    )
							return accumulator




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str    ) ->			List[Any]:
							_lowercase       :	Optional[Any]           =   dataset
							_lowercase       :	Any           =   key






	def __len__(self				: Any    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			Any:
							return self.dataset[i][self.key]




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	int           =   dataset
							_lowercase       :	Optional[Any]           =   keya
							_lowercase       :	Tuple           =   keya






	def __len__(self				: List[str]    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: Optional[Any]      ,	snake_case				: Dict    ) ->			int:
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




