




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__ :   str						=							logging.get_logger('''transformers.models.speecht5''')

UpperCamelCase__ :   Tuple						=							{
    '''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__ :   List[Any]						=							{
    '''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__ :   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''',
}
UpperCamelCase__ :   Any						=							{
    '''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__ :   Union[str, Any]						=							{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
UpperCamelCase__ :   List[str]						=							{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
UpperCamelCase__ :   List[Any]						=							{
    '''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__ :   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''',
}
UpperCamelCase__ :   Optional[int]						=							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCamelCase__ :   Optional[int]						=							{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__ :   Tuple						=							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__ :   str						=							[]
UpperCamelCase__ :   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''',
]
UpperCamelCase__ :   Union[str, Any]						=							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
UpperCamelCase__ :   str						=							IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
UpperCamelCase__ :   Optional[int]						=							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     List[Any]  )       ->  List[Any]:







			"""simple docstring"""





			for attribute in key.split('.'  ):
						SCREAMING_SNAKE_CASE_   : Tuple					  =      getattr(lowerCamelCase_  ,							lowerCamelCase_  )

			if weight_type is not None:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      getattr(lowerCamelCase_  ,							lowerCamelCase_  ).shape
			else:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      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":
						SCREAMING_SNAKE_CASE_   : List[str]					  =      value
			elif weight_type == "weight_g":
						SCREAMING_SNAKE_CASE_   : Any					  =      value
			elif weight_type == "weight_v":
						SCREAMING_SNAKE_CASE_   : Dict					  =      value
			elif weight_type == "bias":
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      value
			elif weight_type == "running_mean":
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      value
			elif weight_type == "running_var":
						SCREAMING_SNAKE_CASE_   : List[str]					  =      value
			elif weight_type == "num_batches_tracked":
						SCREAMING_SNAKE_CASE_   : str					  =      value
			else:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      value

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





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Any:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      []

			if task == "s2t":
						SCREAMING_SNAKE_CASE_   : Dict					  =      hf_model.speechta.encoder.prenet.feature_encoder
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      MAPPING_S2T
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      IGNORE_KEYS_S2T
			elif task == "t2s":
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      None
						SCREAMING_SNAKE_CASE_   : str					  =      MAPPING_T2S
						SCREAMING_SNAKE_CASE_   : Dict					  =      IGNORE_KEYS_T2S
			elif task == "s2s":
						SCREAMING_SNAKE_CASE_   : Dict					  =      hf_model.speechta.encoder.prenet.feature_encoder
						SCREAMING_SNAKE_CASE_   : Dict					  =      MAPPING_S2S
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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

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

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







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      full_name.split('conv_layers.'  )[-1]
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      name.split('.'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      int(items[0]  )
			SCREAMING_SNAKE_CASE_   : List[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.'  )
									SCREAMING_SNAKE_CASE_   : List[str]					  =      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.'  )
									SCREAMING_SNAKE_CASE_   : Any					  =      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.'  )
									SCREAMING_SNAKE_CASE_   : List[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.'  )
									SCREAMING_SNAKE_CASE_   : Dict					  =      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_ :     List[str]  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Optional[int]=None  ,							lowerCamelCase_ :     int=None  ,							lowerCamelCase_ :     List[Any]=None  ,							)       ->  int:







			"""simple docstring"""





			if config_path is not None:
						SCREAMING_SNAKE_CASE_   : List[str]					  =      SpeechTaConfig.from_pretrained(lowerCamelCase_  )
			else:
						SCREAMING_SNAKE_CASE_   : List[str]					  =      SpeechTaConfig()

			if task == "s2t":
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      config.max_text_positions
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      SpeechTaForSpeechToText(lowerCamelCase_  )
			elif task == "t2s":
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      18_76
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      6_00
						SCREAMING_SNAKE_CASE_   : Dict					  =      config.max_speech_positions
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      SpeechTaForTextToSpeech(lowerCamelCase_  )
			elif task == "s2s":
						SCREAMING_SNAKE_CASE_   : int					  =      18_76
						SCREAMING_SNAKE_CASE_   : int					  =      config.max_speech_positions
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      SpeechTaForSpeechToSpeech(lowerCamelCase_  )
			else:
						raise ValueError(F'Unknown task name: {task}'  )

			if vocab_path:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      SpeechTaTokenizer(lowerCamelCase_  ,							model_max_length=config.max_text_positions  )

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

			SCREAMING_SNAKE_CASE_   : Dict					  =      SpeechTaFeatureExtractor()
			SCREAMING_SNAKE_CASE_   : Any					  =      SpeechTaProcessor(tokenizer=lowerCamelCase_  ,							feature_extractor=lowerCamelCase_  )
			processor.save_pretrained(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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__ :   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.'''
						)

						UpperCamelCase__ :   Optional[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_tokenizers_available, is_torch_available


UpperCamelCase__ :   Optional[int]						=							{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							['''BloomTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Any						=							[
						    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BloomForCausalLM''',
						    '''BloomModel''',
						    '''BloomPreTrainedModel''',
						    '''BloomForSequenceClassification''',
						    '''BloomForTokenClassification''',
						    '''BloomForQuestionAnswering''',
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Tuple						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



