
"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase__          = random.Random()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       int=1.0 ,					__lowerCamelCase   :       Optional[int]=None ,					__lowerCamelCase   :       Dict=None		)     ->					str:
  if rng is None:
    _snake_case					     =       global_rng

  _snake_case					     =       []
  for batch_idx in range(shape[0]		):
    values.append([]		)
    for _ in range(shape[1]		):
      values[-1].append(rng.random() * scale		)

  return values

class       lowerCAmelCase__				(						unittest.TestCase	):







     def __init__(							self			:			Optional[int]    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Optional[Any]=7    ,      _lowerCamelCase			:			int=400    ,      _lowerCamelCase			:			int=2000    ,      _lowerCamelCase			:			Dict=1    ,      _lowerCamelCase			:			int=0.0    ,      _lowerCamelCase			:			Optional[Any]=16000    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			Tuple=True    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       min_seq_length
       _snake_case					     =       max_seq_length
       _snake_case					     =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       _snake_case					     =       feature_size
       _snake_case					     =       padding_value
       _snake_case					     =       sampling_rate
       _snake_case					     =       return_attention_mask
       _snake_case					     =       do_normalize







     def      lowercase	(							self			:			int			):
       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			str=False			):
       def _flatten(_lowerCamelCase			:			int			):
         return list(itertools.chain(*_lowerCamelCase			)			)

       if equal_length:
         _snake_case					     =       floats_list((self.batch_size, self.max_seq_length)			)
       else:
         # make sure that inputs increase in size
         _snake_case					     =       [
             _flatten(floats_list((x, self.feature_size)			)			)
             for x in range(self.min_seq_length    ,      self.max_seq_length    ,      self.seq_length_diff			)
         ]

       if numpify:
         _snake_case					     =       [np.asarray(_lowerCamelCase			) for x in speech_inputs]

       return speech_inputs






class       lowerCAmelCase__				(						A_ ,	unittest.TestCase	):
     __a						=					WavaVecaFeatureExtractor







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       WavaVecaFeatureExtractionTester(self			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			int			):
       self.assertTrue(np.all(np.mean(_lowerCamelCase    ,      axis=0			) < 1e-3			)			)
       self.assertTrue(np.all(np.abs(np.var(_lowerCamelCase    ,      axis=0			) - 1			) < 1e-3			)			)







     def      lowercase	(							self			:			Union[str, Any]			):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       # create three inputs of length 800, 1000, and 1200
       _snake_case					     =       [floats_list((1, x)			)[0] for x in range(800    ,      1400    ,      200			)]
       _snake_case					     =       [np.asarray(_lowerCamelCase			) for speech_input in speech_inputs]

       # Test not batched input
       _snake_case					     =       feat_extract(speech_inputs[0]    ,      return_tensors='''np'''			).input_values
       _snake_case					     =       feat_extract(np_speech_inputs[0]    ,      return_tensors='''np'''			).input_values
       self.assertTrue(np.allclose(_lowerCamelCase    ,      _lowerCamelCase    ,      atol=1e-3			)			)

       # Test batched
       _snake_case					     =       feat_extract(_lowerCamelCase    ,      return_tensors='''np'''			).input_values
       _snake_case					     =       feat_extract(_lowerCamelCase    ,      return_tensors='''np'''			).input_values
       for enc_seq_a, enc_seq_a in zip(_lowerCamelCase    ,      _lowerCamelCase			):
         self.assertTrue(np.allclose(_lowerCamelCase    ,      _lowerCamelCase    ,      atol=1e-3			)			)

       # Test 2-D numpy arrays are batched.
       _snake_case					     =       [floats_list((1, x)			)[0] for x in (800, 800, 800)]
       _snake_case					     =       np.asarray(_lowerCamelCase			)
       _snake_case					     =       feat_extract(_lowerCamelCase    ,      return_tensors='''np'''			).input_values
       _snake_case					     =       feat_extract(_lowerCamelCase    ,      return_tensors='''np'''			).input_values
       for enc_seq_a, enc_seq_a in zip(_lowerCamelCase    ,      _lowerCamelCase			):
         self.assertTrue(np.allclose(_lowerCamelCase    ,      _lowerCamelCase    ,      atol=1e-3			)			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       _snake_case					     =       [floats_list((1, x)			)[0] for x in range(800    ,      1400    ,      200			)]

       _snake_case					     =       ['''longest''', '''max_length''', '''do_not_pad''']
       _snake_case					     =       [None, 1600, None]
       for max_length, padding in zip(_lowerCamelCase    ,      _lowerCamelCase			):
         _snake_case					     =       feat_extract(_lowerCamelCase    ,      padding=_lowerCamelCase    ,      max_length=_lowerCamelCase    ,      return_tensors='''np'''			)
         _snake_case					     =       processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:800]			)
         self.assertTrue(input_values[0][800:].sum() < 1e-6			)
         self._check_zero_mean_unit_variance(input_values[1][:1000]			)
         self.assertTrue(input_values[0][1000:].sum() < 1e-6			)
         self._check_zero_mean_unit_variance(input_values[2][:1200]			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       _snake_case					     =       range(800    ,      1400    ,      200			)
       _snake_case					     =       [floats_list((1, x)			)[0] for x in lengths]

       _snake_case					     =       ['''longest''', '''max_length''', '''do_not_pad''']
       _snake_case					     =       [None, 1600, None]

       for max_length, padding in zip(_lowerCamelCase    ,      _lowerCamelCase			):
         _snake_case					     =       feat_extract(_lowerCamelCase    ,      max_length=_lowerCamelCase    ,      padding=_lowerCamelCase			)
         _snake_case					     =       processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:800]			)
         self._check_zero_mean_unit_variance(input_values[1][:1000]			)
         self._check_zero_mean_unit_variance(input_values[2][:1200]			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       _snake_case					     =       [floats_list((1, x)			)[0] for x in range(800    ,      1400    ,      200			)]
       _snake_case					     =       feat_extract(
           _lowerCamelCase    ,      truncation=_lowerCamelCase    ,      max_length=1000    ,      padding='''max_length'''    ,      return_tensors='''np'''			)
       _snake_case					     =       processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]			)
       self._check_zero_mean_unit_variance(input_values[1]			)
       self._check_zero_mean_unit_variance(input_values[2]			)







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       _snake_case					     =       [floats_list((1, x)			)[0] for x in range(800    ,      1400    ,      200			)]
       _snake_case					     =       feat_extract(
           _lowerCamelCase    ,      truncation=_lowerCamelCase    ,      max_length=1000    ,      padding='''longest'''    ,      return_tensors='''np'''			)
       _snake_case					     =       processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]			)
       self._check_zero_mean_unit_variance(input_values[1, :1000]			)
       self._check_zero_mean_unit_variance(input_values[2]			)

       # make sure that if max_length < longest -> then pad to max_length
       self.assertTrue(input_values.shape == (3, 1000)			)

       _snake_case					     =       [floats_list((1, x)			)[0] for x in range(800    ,      1400    ,      200			)]
       _snake_case					     =       feat_extract(
           _lowerCamelCase    ,      truncation=_lowerCamelCase    ,      max_length=2000    ,      padding='''longest'''    ,      return_tensors='''np'''			)
       _snake_case					     =       processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]			)
       self._check_zero_mean_unit_variance(input_values[1, :1000]			)
       self._check_zero_mean_unit_variance(input_values[2]			)

       # make sure that if max_length > longest -> then pad to longest
       self.assertTrue(input_values.shape == (3, 1200)			)







     @require_torch
     def      lowercase	(							self			:			Dict			):
       import torch

       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       _snake_case					     =       np.random.rand(100			).astype(np.floataa			)
       _snake_case					     =       np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
         _snake_case					     =       feature_extractor.pad([{'''input_values''': inputs}]    ,      return_tensors='''np'''			)
         self.assertTrue(np_processed.input_values.dtype == np.floataa			)
         _snake_case					     =       feature_extractor.pad([{'''input_values''': inputs}]    ,      return_tensors='''pt'''			)
         self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)







     @slow
     @require_torch
     def      lowercase	(							self			:			List[Any]			):
       # this test makes sure that models that are using
       # group norm don't have their feature extractor return the
       # attention_mask
       for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
         _snake_case					     =       WavaVecaConfig.from_pretrained(_lowerCamelCase			)
         _snake_case					     =       WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase			)

         # only "layer" feature extraction norm should make use of
         # attention_mask
         self.assertEqual(feat_extract.return_attention_mask    ,      config.feat_extract_norm == '''layer'''			)





"""simple docstring"""



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 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       Dict		)     ->					List[Any]:
  for attribute in key.split('''.'''		):
    _snake_case					     =       getattr(__lowerCamelCase ,					__lowerCamelCase		)

  if weight_type is not None:
    _snake_case					     =       getattr(__lowerCamelCase ,					__lowerCamelCase		).shape
  else:
    _snake_case					     =       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":
    _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
  elif weight_type == "running_mean":
    _snake_case					     =       value
  elif weight_type == "running_var":
    _snake_case					     =       value
  elif weight_type == "num_batches_tracked":
    _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 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[int]		)     ->					List[str]:
  for key in ignore_keys:
    if key.endswith('''.*'''		):
      if name.startswith(key[:-1]		):
        return True
    elif ".*." in key:
      _snake_case     ,					_snake_case					     =       key.split('''.*.'''		)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple		)     ->					Optional[Any]:
  _snake_case					     =       []

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

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

    _snake_case					     =       False
    if "conv_layers" in name:
      load_conv_layer(
          __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					hf_model.config.feat_extract_norm == '''group''' ,					)
      _snake_case					     =       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:
          _snake_case     ,					_snake_case					     =       key.split('''.*.'''		)
          if prefix in name and suffix in name:
            _snake_case					     =       suffix

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

  logger.warning(f'''Unused weights: {unused_weights}'''		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple		)     ->					List[Any]:
  _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:
      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.'''		)
      _snake_case					     =       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.'''		)
      _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:
      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.'''		)
      _snake_case					     =       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.'''		)
      _snake_case					     =       value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  else:
    unused_weights.append(__lowerCamelCase		)
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[int]=None ,					__lowerCamelCase   :       int=None ,					__lowerCamelCase   :       Union[str, Any]=None ,					)     ->					Dict:
  if config_path is not None:
    _snake_case					     =       SpeechTaConfig.from_pretrained(__lowerCamelCase		)
  else:
    _snake_case					     =       SpeechTaConfig()

  if task == "s2t":
    _snake_case					     =       config.max_text_positions
    _snake_case					     =       SpeechTaForSpeechToText(__lowerCamelCase		)
  elif task == "t2s":
    _snake_case					     =       18_76
    _snake_case					     =       6_00
    _snake_case					     =       config.max_speech_positions
    _snake_case					     =       SpeechTaForTextToSpeech(__lowerCamelCase		)
  elif task == "s2s":
    _snake_case					     =       18_76
    _snake_case					     =       config.max_speech_positions
    _snake_case					     =       SpeechTaForSpeechToSpeech(__lowerCamelCase		)
  else:
    raise ValueError(f'''Unknown task name: {task}'''		)

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

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

  _snake_case					     =       SpeechTaFeatureExtractor()
  _snake_case					     =       SpeechTaProcessor(tokenizer=__lowerCamelCase ,					feature_extractor=__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

  _snake_case					     =       torch.load(__lowerCamelCase		)
  recursively_load_weights(fairseq_checkpoint['''model'''] ,					__lowerCamelCase ,					__lowerCamelCase		)

  model.save_pretrained(__lowerCamelCase		)

  if repo_id:
    print('''Pushing to the hub...'''		)
    processor.push_to_hub(__lowerCamelCase		)
    model.push_to_hub(__lowerCamelCase		)


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,
 )



