


"""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()
SCREAMING_SNAKE_CASE :    Dict									=				logging.get_logger("""transformers.models.speecht5""")

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






def 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      List[Any]     ,   _snake_case						:      List[str]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      Optional[Any]     )     ->List[Any]:
	"""simple docstring"""





	for attribute in key.split('''.'''     ):
		__snake_case	:						Union[str, Any]						=				getattr(_snake_case     ,   _snake_case     )

	if weight_type is not None:
		__snake_case	:						str						=				getattr(_snake_case     ,   _snake_case     ).shape
	else:
		__snake_case	:						str						=				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	:						List[Any]						=				value
	elif weight_type == "weight_g":
		__snake_case	:						Dict						=				value
	elif weight_type == "weight_v":
		__snake_case	:						List[Any]						=				value
	elif weight_type == "bias":
		__snake_case	:						Dict						=				value
	elif weight_type == "running_mean":
		__snake_case	:						Tuple						=				value
	elif weight_type == "running_var":
		__snake_case	:						Dict						=				value
	elif weight_type == "num_batches_tracked":
		__snake_case	:						Any						=				value
	else:
		__snake_case	:						Dict						=				value

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






def 		lowercase							(     _snake_case						:      List[str]     ,   _snake_case						:      int     )     ->List[str]:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Optional[int]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						Tuple						=				[]

	if task == "s2t":
		__snake_case	:						List[Any]						=				hf_model.speechta.encoder.prenet.feature_encoder
		__snake_case	:						Optional[Any]						=				MAPPING_S2T
		__snake_case	:						int						=				IGNORE_KEYS_S2T
	elif task == "t2s":
		__snake_case	:						Union[str, Any]						=				None
		__snake_case	:						Tuple						=				MAPPING_T2S
		__snake_case	:						Union[str, Any]						=				IGNORE_KEYS_T2S
	elif task == "s2s":
		__snake_case	:						Union[str, Any]						=				hf_model.speechta.encoder.prenet.feature_encoder
		__snake_case	:						Any						=				MAPPING_S2S
		__snake_case	:						List[str]						=				IGNORE_KEYS_S2S
	else:
		raise ValueError(f"""Unsupported task: {task}"""     )

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

		__snake_case	:						Any						=				False
		if "conv_layers" in name:
			load_conv_layer(
			    _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   hf_model.config.feat_extract_norm == '''group'''     ,   )
			__snake_case	:						Dict						=				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	:						List[str]						=				key.split('''.*.'''     )
					if prefix in name and suffix in name:
						__snake_case	:						int						=				suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					__snake_case	:						Optional[int]						=				True
					if "*" in mapped_key:
						__snake_case	:						Union[str, Any]						=				name.split(_snake_case     )[0].split('''.'''     )[-2]
						__snake_case	:						Optional[Any]						=				mapped_key.replace('''*'''     ,   _snake_case     )
					if "weight_g" in name:
						__snake_case	:						str						=				'''weight_g'''
					elif "weight_v" in name:
						__snake_case	:						List[Any]						=				'''weight_v'''
					elif "bias" in name:
						__snake_case	:						List[str]						=				'''bias'''
					elif "weight" in name:
						__snake_case	:						Optional[Any]						=				'''weight'''
					elif "running_mean" in name:
						__snake_case	:						Optional[Any]						=				'''running_mean'''
					elif "running_var" in name:
						__snake_case	:						Optional[int]						=				'''running_var'''
					elif "num_batches_tracked" in name:
						__snake_case	:						Union[str, Any]						=				'''num_batches_tracked'''
					else:
						__snake_case	:						str						=				None
					set_recursively(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )
				continue
		if not is_used:
			unused_weights.append(_snake_case     )

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






def 		lowercase							(     _snake_case						:      Any     ,   _snake_case						:      str     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Any     ,   _snake_case						:      int     )     ->List[str]:
	"""simple docstring"""





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






@torch.no_grad()
def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      str     ,   _snake_case						:      int     ,   _snake_case						:      Optional[int]=None     ,   _snake_case						:      Optional[Any]=None     ,   _snake_case						:      Optional[Any]=None     ,   )     ->int:
	"""simple docstring"""





	if config_path is not None:
		__snake_case	:						str						=				SpeechTaConfig.from_pretrained(_snake_case     )
	else:
		__snake_case	:						Union[str, Any]						=				SpeechTaConfig()

	if task == "s2t":
		__snake_case	:						int						=				config.max_text_positions
		__snake_case	:						List[Any]						=				SpeechTaForSpeechToText(_snake_case     )
	elif task == "t2s":
		__snake_case	:						int						=				1_876
		__snake_case	:						List[str]						=				600
		__snake_case	:						Union[str, Any]						=				config.max_speech_positions
		__snake_case	:						Union[str, Any]						=				SpeechTaForTextToSpeech(_snake_case     )
	elif task == "s2s":
		__snake_case	:						Union[str, Any]						=				1_876
		__snake_case	:						Tuple						=				config.max_speech_positions
		__snake_case	:						List[str]						=				SpeechTaForSpeechToSpeech(_snake_case     )
	else:
		raise ValueError(f"""Unknown task name: {task}"""     )

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

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

	__snake_case	:						str						=				SpeechTaFeatureExtractor()
	__snake_case	:						int						=				SpeechTaProcessor(tokenizer=_snake_case     ,   feature_extractor=_snake_case     )
	processor.save_pretrained(_snake_case     )

	__snake_case	:						Optional[int]						=				torch.load(_snake_case     )
	recursively_load_weights(fairseq_checkpoint['''model''']     ,   _snake_case     ,   _snake_case     )

	model.save_pretrained(_snake_case     )

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Tuple									=				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."""
			)

			SCREAMING_SNAKE_CASE :    List[Any]									=				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,
			)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

