"""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()
__lowerCAmelCase    :       str					     =logging.get_logger("""transformers.models.speecht5""")

__lowerCAmelCase    :       Dict					     ={
    """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""",
}
__lowerCAmelCase    :       Optional[int]					     ={
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
__lowerCAmelCase    :       int					     ={
    """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""",
}
__lowerCAmelCase    :       Dict					     ={
    """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""",
}
__lowerCAmelCase    :       Dict					     ={
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
__lowerCAmelCase    :       Optional[Any]					     ={
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
__lowerCAmelCase    :       int					     ={
    """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""",
}
__lowerCAmelCase    :       Dict					     ={
    """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""",
}
__lowerCAmelCase    :       str					     ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCAmelCase    :       List[str]					     ={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCAmelCase    :       Tuple					     ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCAmelCase    :       Union[str, Any]					     =[]
__lowerCAmelCase    :       int					     =[
    """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""",
]
__lowerCAmelCase    :       str					     =IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
__lowerCAmelCase    :       List[Any]					     =IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
__lowerCAmelCase    :       Any					     =IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :int		) ->    Dict:
      '''simple docstring'''


      for attribute in key.split("""."""		):
            lowercase														=	getattr(lowerCAmelCase__					,		lowerCAmelCase__		)

      if weight_type is not None:
            lowercase														=	getattr(lowerCAmelCase__					,		lowerCAmelCase__		).shape
      else:
            lowercase														=	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":
            lowercase														=	value
      elif weight_type == "weight_g":
            lowercase														=	value
      elif weight_type == "weight_v":
            lowercase														=	value
      elif weight_type == "bias":
            lowercase														=	value
      elif weight_type == "running_mean":
            lowercase														=	value
      elif weight_type == "running_var":
            lowercase														=	value
      elif weight_type == "num_batches_tracked":
            lowercase														=	value
      else:
            lowercase														=	value

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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :List[str]		) ->    List[Any]:
      '''simple docstring'''


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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :List[Any]		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	[]

      if task == "s2t":
            lowercase														=	hf_model.speechta.encoder.prenet.feature_encoder
            lowercase														=	MAPPING_S2T
            lowercase														=	IGNORE_KEYS_S2T
      elif task == "t2s":
            lowercase														=	None
            lowercase														=	MAPPING_T2S
            lowercase														=	IGNORE_KEYS_T2S
      elif task == "s2s":
            lowercase														=	hf_model.speechta.encoder.prenet.feature_encoder
            lowercase														=	MAPPING_S2S
            lowercase														=	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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                        if key in name:
                              lowercase														=	True
                              if "*" in mapped_key:
                                    lowercase														=	name.split(lowerCAmelCase__		)[0].split("""."""		)[-2]
                                    lowercase														=	mapped_key.replace("""*"""					,		lowerCAmelCase__		)
                              if "weight_g" in name:
                                    lowercase														=	"""weight_g"""
                              elif "weight_v" in name:
                                    lowercase														=	"""weight_v"""
                              elif "bias" in name:
                                    lowercase														=	"""bias"""
                              elif "weight" in name:
                                    lowercase														=	"""weight"""
                              elif "running_mean" in name:
                                    lowercase														=	"""running_mean"""
                              elif "running_var" in name:
                                    lowercase														=	"""running_var"""
                              elif "num_batches_tracked" in name:
                                    lowercase														=	"""num_batches_tracked"""
                              else:
                                    lowercase														=	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__     :List[str]					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :List[str]		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	full_name.split("""conv_layers."""		)[-1]
      lowercase														=	name.split("""."""		)
      lowercase														=	int(items[0]		)
      lowercase														=	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.'		)
                  lowercase														=	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.'		)
                  lowercase														=	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.'		)
                  lowercase														=	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.'		)
                  lowercase														=	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__     :Tuple					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Tuple=None					,		lowerCAmelCase__     :Optional[int]=None					,		lowerCAmelCase__     :Optional[int]=None					,		) ->    str:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=	SpeechTaConfig.from_pretrained(lowerCAmelCase__		)
      else:
            lowercase														=	SpeechTaConfig()

      if task == "s2t":
            lowercase														=	config.max_text_positions
            lowercase														=	SpeechTaForSpeechToText(lowerCAmelCase__		)
      elif task == "t2s":
            lowercase														=	1_8_7_6
            lowercase														=	6_0_0
            lowercase														=	config.max_speech_positions
            lowercase														=	SpeechTaForTextToSpeech(lowerCAmelCase__		)
      elif task == "s2s":
            lowercase														=	1_8_7_6
            lowercase														=	config.max_speech_positions
            lowercase														=	SpeechTaForSpeechToSpeech(lowerCAmelCase__		)
      else:
            raise ValueError(f'Unknown task name: {task}'		)

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

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

      lowercase														=	SpeechTaFeatureExtractor()
      lowercase														=	SpeechTaProcessor(tokenizer=lowerCAmelCase__					,		feature_extractor=lowerCAmelCase__		)
      processor.save_pretrained(lowerCAmelCase__		)

      lowercase														=	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__":
       __lowerCAmelCase    :       List[str]					     =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."""
       )

       __lowerCAmelCase    :       int					     =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,
       )




"""simple docstring"""






from __future__ import annotations

from scipy.special import comb  # type: ignore





class  _A						:

      def __init__(						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	list_of_points
            # Degree determines the flexibility of the curve.
            # Degree = 1 will produce a straight line.
            lowercase														=	len(__lowerCAmelCase						) - 1

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            assert 0 <= t <= 1, "Time t must be between 0 and 1."
            lowercase														=	[]
            for i in range(len(self.list_of_points						)						):
                  # basis function for each i
                  output_values.append(
                      comb(self.degree		,       __lowerCAmelCase						) * ((1 - t) ** (self.degree - i)) * (t**i)						)
            # the basis must sum up to 1 for it to produce a valid Bezier curve.
            assert round(sum(__lowerCAmelCase						)		,       5						) == 1
            return output_values

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            assert 0 <= t <= 1, "Time t must be between 0 and 1."

            lowercase														=	self.basis_function(__lowerCAmelCase						)
            lowercase														=	0.0
            lowercase														=	0.0
            for i in range(len(self.list_of_points						)						):
                  # For all points, sum up the product of i-th basis function and i-th point.
                  x += basis_function[i] * self.list_of_points[i][0]
                  y += basis_function[i] * self.list_of_points[i][1]
            return (x, y)







      def 				A__       (						self		,       __lowerCAmelCase = 0.0_1						):







            """simple docstring"""



            from matplotlib import pyplot as plt  # type: ignore

            lowercase														=	[]  # x coordinates of points to plot
            lowercase														=	[]  # y coordinates of points to plot

            lowercase														=	0.0
            while t <= 1:
                  lowercase														=	self.bezier_curve_function(__lowerCAmelCase						)
                  to_plot_x.append(value[0]						)
                  to_plot_y.append(value[1]						)
                  t += step_size

            lowercase														=	[i[0] for i in self.list_of_points]
            lowercase														=	[i[1] for i in self.list_of_points]

            plt.plot(
                __lowerCAmelCase		,       __lowerCAmelCase		,       color="""blue"""		,       label="""Curve of Degree """ + str(self.degree						)		,       )
            plt.scatter(__lowerCAmelCase		,       __lowerCAmelCase		,       color="""red"""		,       label="""Control Points"""						)
            plt.legend()
            plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
       BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
       BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



