





import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[Tuple[int, ...]]:
       UpperCamelCase_          =  []
       if isinstance(UpperCamelCase_					,		UpperCamelCase_  ):
              for v in tree.values():
                     shapes.extend(_fetch_dims(UpperCamelCase_  )  )
       elif isinstance(UpperCamelCase_					,		(list, tuple)  ):
              for t in tree:
                     shapes.extend(_fetch_dims(UpperCamelCase_  )  )
       elif isinstance(UpperCamelCase_					,		torch.Tensor  ):
              shapes.append(tree.shape  )
       else:
              raise ValueError("Not supported"  )

       return shapes




@torch.jit.ignore
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple[int, ...]:
       UpperCamelCase_          =  []
       for d in reversed(UpperCamelCase_  ):
              idx.append(flat_idx % d  )
              UpperCamelCase_          =  flat_idx // d

       return tuple(reversed(UpperCamelCase_  )  )




@torch.jit.ignore
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None					,		)     ->		List[Tuple[slice, ...]]:

       # start_edges and end_edges both indicate whether, starting from any given
       # dimension, the start/end index is at the top/bottom edge of the
       # corresponding tensor, modeled as a tree
       def reduce_edge_list(UpperCamelCase_  ) -> None:
              UpperCamelCase_          =  True
              for i in range(len(UpperCamelCase_  )  ):
                     UpperCamelCase_          =  -1 * (i + 1)
                     l[reversed_idx] &= tally
                     UpperCamelCase_          =  l[reversed_idx]

       if start_edges is None:
              UpperCamelCase_          =  [s == 0 for s in start]
              reduce_edge_list(UpperCamelCase_  )
       if end_edges is None:
              UpperCamelCase_          =  [e == (d - 1) for e, d in zip(UpperCamelCase_					,		UpperCamelCase_  )]
              reduce_edge_list(UpperCamelCase_  )

       # Base cases. Either start/end are empty and we're done, or the final,
       # one-dimensional tensor can be simply sliced
       if len(UpperCamelCase_  ) == 0:
              return [()]
       elif len(UpperCamelCase_  ) == 1:
              return [(slice(start[0]					,		end[0] + 1  ),)]

       UpperCamelCase_          =  []
       UpperCamelCase_          =  []

       # Dimensions common to start and end can be selected directly
       for s, e in zip(UpperCamelCase_					,		UpperCamelCase_  ):
              if s == e:
                     path_list.append(slice(UpperCamelCase_					,		s + 1  )  )
              else:
                     break

       UpperCamelCase_          =  tuple(UpperCamelCase_  )
       UpperCamelCase_          =  len(UpperCamelCase_  )

       # start == end, and we're done
       if divergence_idx == len(UpperCamelCase_  ):
              return [path]

       def upper() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              UpperCamelCase_          =  start[divergence_idx]
              return tuple(
                  path + (slice(UpperCamelCase_					,		sdi + 1  ),) + s
                  for s in _get_minimal_slice_set(
                      start[divergence_idx + 1 :]					,		[d - 1 for d in dims[divergence_idx + 1 :]]					,		dims[divergence_idx + 1 :]					,		start_edges=start_edges[divergence_idx + 1 :]					,		end_edges=[True for _ in end_edges[divergence_idx + 1 :]]					,		)  )

       def lower() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              UpperCamelCase_          =  end[divergence_idx]
              return tuple(
                  path + (slice(UpperCamelCase_					,		edi + 1  ),) + s
                  for s in _get_minimal_slice_set(
                      [0 for _ in start[divergence_idx + 1 :]]					,		end[divergence_idx + 1 :]					,		dims[divergence_idx + 1 :]					,		start_edges=[True for _ in start_edges[divergence_idx + 1 :]]					,		end_edges=end_edges[divergence_idx + 1 :]					,		)  )

       # If both start and end are at the edges of the subtree rooted at
       # divergence_idx, we can just select the whole subtree at once
       if start_edges[divergence_idx] and end_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx]					,		end[divergence_idx] + 1  ),)  )
       # If just start is at the edge, we can grab almost all of the subtree,
       # treating only the ragged bottom edge as an edge case
       elif start_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx]					,		end[divergence_idx]  ),)  )
              slices.extend(lower()  )
       # Analogous to the previous case, but the top is ragged this time
       elif end_edges[divergence_idx]:
              slices.extend(upper()  )
              slices.append(path + (slice(start[divergence_idx] + 1					,		end[divergence_idx] + 1  ),)  )
       # If both sides of the range are ragged, we need to handle both sides
       # separately. If there's contiguous meat in between them, we can index it
       # in one big chunk
       else:
              slices.extend(upper()  )
              UpperCamelCase_          =  end[divergence_idx] - start[divergence_idx]
              if middle_ground > 1:
                     slices.append(path + (slice(start[divergence_idx] + 1					,		end[divergence_idx]  ),)  )
              slices.extend(lower()  )

       return slices




@torch.jit.ignore
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		torch.Tensor:
       UpperCamelCase_          =  t.shape[:no_batch_dims]
       UpperCamelCase_          =  list(_flat_idx_to_idx(UpperCamelCase_					,		UpperCamelCase_  )  )
       # _get_minimal_slice_set is inclusive
       UpperCamelCase_          =  list(_flat_idx_to_idx(flat_end - 1					,		UpperCamelCase_  )  )

       # Get an ordered list of slices to perform
       UpperCamelCase_          =  _get_minimal_slice_set(
           UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		)

       UpperCamelCase_          =  [t[s] for s in slices]

       return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]  ) for s in sliced_tensors]  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = False					,		UpperCamelCase_ = None					,		UpperCamelCase_ = False					,		)     ->		Any:
       if not (len(UpperCamelCase_  ) > 0):
              raise ValueError("Must provide at least one input"  )

       UpperCamelCase_          =  [shape[:no_batch_dims] for shape in _fetch_dims(UpperCamelCase_  )]
       UpperCamelCase_          =  tuple([max(UpperCamelCase_  ) for s in zip(*UpperCamelCase_  )]  )

       def _prep_inputs(UpperCamelCase_  ) -> torch.Tensor:
              if not low_mem:
                     if not sum(t.shape[:no_batch_dims]  ) == no_batch_dims:
                            UpperCamelCase_          =  t.expand(orig_batch_dims + t.shape[no_batch_dims:]  )
                     UpperCamelCase_          =  t.reshape(-1					,		*t.shape[no_batch_dims:]  )
              else:
                     UpperCamelCase_          =  t.expand(orig_batch_dims + t.shape[no_batch_dims:]  )
              return t

       UpperCamelCase_          =  tensor_tree_map(_prep_inputs					,		UpperCamelCase_  )
       UpperCamelCase_          =  None
       if _out is not None:
              UpperCamelCase_          =  tensor_tree_map(lambda UpperCamelCase_  : t.view([-1] + list(t.shape[no_batch_dims:]  )  )					,		_out  )

       UpperCamelCase_          =  1
       for d in orig_batch_dims:
              flat_batch_dim *= d

       UpperCamelCase_          =  flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

       def _select_chunk(UpperCamelCase_  ) -> torch.Tensor:
              return t[i : i + chunk_size] if t.shape[0] != 1 else t

       UpperCamelCase_          =  0
       UpperCamelCase_          =  prepped_outputs
       for _ in range(UpperCamelCase_  ):
              # Chunk the input
              if not low_mem:
                     UpperCamelCase_          =  _select_chunk
              else:
                     UpperCamelCase_          =  partial(
                         _chunk_slice					,		flat_start=UpperCamelCase_					,		flat_end=min(UpperCamelCase_					,		i + chunk_size  )					,		no_batch_dims=len(UpperCamelCase_  )					,		)

              UpperCamelCase_          =  tensor_tree_map(UpperCamelCase_					,		UpperCamelCase_  )

              # Run the layer on the chunk
              UpperCamelCase_          =  layer(**UpperCamelCase_  )

              # Allocate space for the output
              if out is None:
                     UpperCamelCase_          =  tensor_tree_map(lambda UpperCamelCase_  : t.new_zeros((flat_batch_dim,) + t.shape[1:]  )					,		UpperCamelCase_  )

              # Put the chunk in its pre-allocated space
              if isinstance(UpperCamelCase_					,		UpperCamelCase_  ):

                     def assign(UpperCamelCase_					,		UpperCamelCase_  ) -> None:
                            for k, v in da.items():
                                   if isinstance(UpperCamelCase_					,		UpperCamelCase_  ):
                                          assign(UpperCamelCase_					,		da[k]  )
                                   else:
                                          if _add_into_out:
                                                 v[i : i + chunk_size] += da[k]
                                          else:
                                                 UpperCamelCase_          =  da[k]

                     assign(UpperCamelCase_					,		UpperCamelCase_  )
              elif isinstance(UpperCamelCase_					,		UpperCamelCase_  ):
                     for xa, xa in zip(UpperCamelCase_					,		UpperCamelCase_  ):
                            if _add_into_out:
                                   xa[i : i + chunk_size] += xa
                            else:
                                   UpperCamelCase_          =  xa
              elif isinstance(UpperCamelCase_					,		torch.Tensor  ):
                     if _add_into_out:
                            out[i : i + chunk_size] += output_chunk
                     else:
                            UpperCamelCase_          =  output_chunk
              else:
                     raise ValueError("Not supported"  )

              i += chunk_size

       UpperCamelCase_          =  tensor_tree_map(lambda UpperCamelCase_  : t.view(orig_batch_dims + t.shape[1:]  )					,		UpperCamelCase_  )

       return out







class 							_UpperCamelCase					:

       def __init__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: int = 512					,   )       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  max_chunk_size
              UpperCamelCase_          =  None
              UpperCamelCase_          =  None

       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: Callable					,   _SCREAMING_SNAKE_CASE: tuple					,   _SCREAMING_SNAKE_CASE: int		)       -> int:




              """simple docstring"""


              logging.info("Tuning chunk size..."		)

              if min_chunk_size >= self.max_chunk_size:
                     return min_chunk_size

              UpperCamelCase_          =  [2**l for l in range(int(math.log(self.max_chunk_size					,   2		)		) + 1		)]
              UpperCamelCase_          =  [c for c in candidates if c > min_chunk_size]
              UpperCamelCase_          =  [min_chunk_size] + candidates
              candidates[-1] += 4

              def test_chunk_size(_SCREAMING_SNAKE_CASE: int		) -> bool:
                     try:
                            with torch.no_grad():
                                   fn(*_SCREAMING_SNAKE_CASE					,   chunk_size=_SCREAMING_SNAKE_CASE		)
                            return True
                     except RuntimeError:
                            return False

              UpperCamelCase_          =  0
              UpperCamelCase_          =  len(_SCREAMING_SNAKE_CASE		) - 1
              while i > min_viable_chunk_size_index:
                     UpperCamelCase_          =  test_chunk_size(candidates[i]		)
                     if not viable:
                            UpperCamelCase_          =  (min_viable_chunk_size_index + i) // 2
                     else:
                            UpperCamelCase_          =  i
                            UpperCamelCase_          =  (i + len(_SCREAMING_SNAKE_CASE		) - 1) // 2

              return candidates[min_viable_chunk_size_index]

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Iterable					,   _SCREAMING_SNAKE_CASE: Iterable		)       -> bool:




              """simple docstring"""


              UpperCamelCase_          =  True
              for aa, aa in zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     assert type(_SCREAMING_SNAKE_CASE		) == type(_SCREAMING_SNAKE_CASE		)
                     if isinstance(_SCREAMING_SNAKE_CASE					,   (list, tuple)		):
                            consistent &= self._compare_arg_caches(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                     elif isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                            UpperCamelCase_          =  [v for _, v in sorted(aa.items()					,   key=lambda _SCREAMING_SNAKE_CASE		: x[0]		)]
                            UpperCamelCase_          =  [v for _, v in sorted(aa.items()					,   key=lambda _SCREAMING_SNAKE_CASE		: x[0]		)]
                            consistent &= self._compare_arg_caches(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                     else:
                            consistent &= aa == aa

              return consistent



       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Callable					,   _SCREAMING_SNAKE_CASE: tuple					,   _SCREAMING_SNAKE_CASE: int					,   )       -> int:




              """simple docstring"""


              UpperCamelCase_          =  True
              UpperCamelCase_          =  tree_map(lambda _SCREAMING_SNAKE_CASE		: a.shape if isinstance(_SCREAMING_SNAKE_CASE					,   torch.Tensor		) else a					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              if self.cached_arg_data is not None:
                     # If args have changed shape/value, we need to re-tune
                     assert len(self.cached_arg_data		) == len(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  self._compare_arg_caches(self.cached_arg_data					,   _SCREAMING_SNAKE_CASE		)
              else:
                     # Otherwise, we can reuse the precomputed value
                     UpperCamelCase_          =  False

              if not consistent:
                     UpperCamelCase_          =  self._determine_favorable_chunk_size(
                         _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   )
                     UpperCamelCase_          =  arg_data

              assert self.cached_chunk_size is not None

              return self.cached_chunk_size








import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_UpperCAmelCase        =				logging.get_logger('transformers.models.speecht5')

_UpperCAmelCase        =				{
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
_UpperCAmelCase        =				{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_UpperCAmelCase        =				{
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
_UpperCAmelCase        =				{
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
_UpperCAmelCase        =				{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_UpperCAmelCase        =				{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_UpperCAmelCase        =				{
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
_UpperCAmelCase        =				{
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				[]
_UpperCAmelCase        =				[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Any:
       for attribute in key.split("."  ):
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  )

       if weight_type is not None:
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  ).shape
       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 == "running_mean":
              UpperCamelCase_          =  value
       elif weight_type == "running_var":
              UpperCamelCase_          =  value
       elif weight_type == "num_batches_tracked":
              UpperCamelCase_          =  value
       else:
              UpperCamelCase_          =  value

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       for key in ignore_keys:
              if key.endswith(".*"  ):
                     if name.startswith(key[:-1]  ):
                            return True
              elif ".*." in key:
                     UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[Any]:
       UpperCamelCase_          =  []

       if task == "s2t":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2T
              UpperCamelCase_          =  IGNORE_KEYS_S2T
       elif task == "t2s":
              UpperCamelCase_          =  None
              UpperCamelCase_          =  MAPPING_T2S
              UpperCamelCase_          =  IGNORE_KEYS_T2S
       elif task == "s2s":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2S
              UpperCamelCase_          =  IGNORE_KEYS_S2S
       else:
              raise ValueError(F'''Unsupported task: {task}'''  )

       for name, value in fairseq_dict.items():
              if should_ignore(UpperCamelCase_					,		UpperCamelCase_  ):
                     logger.info(F'''{name} was ignored'''  )
                     continue

              UpperCamelCase_          =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		hf_model.config.feat_extract_norm == "group"					,		)
                     UpperCamelCase_          =  True
              else:
                     for key, mapped_key in MAPPING.items():
                            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                            if "*" in key:
                                   UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                                   if prefix in name and suffix in name:
                                          UpperCamelCase_          =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   UpperCamelCase_          =  True
                                   if "*" in mapped_key:
                                          UpperCamelCase_          =  name.split(UpperCamelCase_  )[0].split("."  )[-2]
                                          UpperCamelCase_          =  mapped_key.replace("*"					,		UpperCamelCase_  )
                                   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:
                                          UpperCamelCase_          =  "weight"
                                   elif "running_mean" in name:
                                          UpperCamelCase_          =  "running_mean"
                                   elif "running_var" in name:
                                          UpperCamelCase_          =  "running_var"
                                   elif "num_batches_tracked" in name:
                                          UpperCamelCase_          =  "num_batches_tracked"
                                   else:
                                          UpperCamelCase_          =  None
                                   set_recursively(UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )
                            continue
              if not is_used:
                     unused_weights.append(UpperCamelCase_  )

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       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(UpperCamelCase_  )




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		)     ->		str:
       if config_path is not None:
              UpperCamelCase_          =  SpeechTaConfig.from_pretrained(UpperCamelCase_  )
       else:
              UpperCamelCase_          =  SpeechTaConfig()

       if task == "s2t":
              UpperCamelCase_          =  config.max_text_positions
              UpperCamelCase_          =  SpeechTaForSpeechToText(UpperCamelCase_  )
       elif task == "t2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  600
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForTextToSpeech(UpperCamelCase_  )
       elif task == "s2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForSpeechToSpeech(UpperCamelCase_  )
       else:
              raise ValueError(F'''Unknown task name: {task}'''  )

       if vocab_path:
              UpperCamelCase_          =  SpeechTaTokenizer(UpperCamelCase_					,		model_max_length=config.max_text_positions  )

              # Mask token behaves like a normal word, i.e. include the space before it
              UpperCamelCase_          =  AddedToken("<mask>"					,		lstrip=UpperCamelCase_					,		rstrip=UpperCamelCase_  )
              UpperCamelCase_          =  mask_token
              tokenizer.add_special_tokens({"mask_token": mask_token}  )
              tokenizer.add_tokens(["<ctc_blank>"]  )

       UpperCamelCase_          =  SpeechTaFeatureExtractor()
       UpperCamelCase_          =  SpeechTaProcessor(tokenizer=UpperCamelCase_					,		feature_extractor=UpperCamelCase_  )
       processor.save_pretrained(UpperCamelCase_  )

       UpperCamelCase_          =  torch.load(UpperCamelCase_  )
       recursively_load_weights(fairseq_checkpoint["model"]					,		UpperCamelCase_					,		UpperCamelCase_  )

       model.save_pretrained(UpperCamelCase_  )

       if repo_id:
              print("Pushing to the hub..."  )
              processor.push_to_hub(UpperCamelCase_  )
              model.push_to_hub(UpperCamelCase_  )


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     parser.add_argument(
         '--task',
         default='s2t',
         type=str,
         help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
     )
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     _UpperCAmelCase        =				parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )

