


'''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()
__snake_case							   =  logging.get_logger('''transformers.models.speecht5''')

__snake_case							   =  {
    '''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''',
}
__snake_case							   =  {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__snake_case							   =  {
    '''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''',
}
__snake_case							   =  {
    '''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''',
}
__snake_case							   =  {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__snake_case							   =  {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__snake_case							   =  {
    '''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''',
}
__snake_case							   =  {
    '''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''',
}
__snake_case							   =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__snake_case							   =  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case							   =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case							   =  []
__snake_case							   =  [
    '''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''',
]
__snake_case							   =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__snake_case							   =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__snake_case							   =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


def     a   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			Any:



					'''simple docstring'''






					for attribute in key.split('''.'''     ):
										UpperCamelCase__     :Any							      =				getattr(__a				,      __a     )

					if weight_type is not None:
										UpperCamelCase__     :Tuple							      =				getattr(__a				,      __a     ).shape
					else:
										UpperCamelCase__     :Dict							      =				hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
										    f''' {value.shape} for {full_name}'''     )

					if weight_type == "weight":
										UpperCamelCase__     :List[str]							      =				value
					elif weight_type == "weight_g":
										UpperCamelCase__     :int							      =				value
					elif weight_type == "weight_v":
										UpperCamelCase__     :List[str]							      =				value
					elif weight_type == "bias":
										UpperCamelCase__     :List[Any]							      =				value
					elif weight_type == "running_mean":
										UpperCamelCase__     :str							      =				value
					elif weight_type == "running_var":
										UpperCamelCase__     :Union[str, Any]							      =				value
					elif weight_type == "num_batches_tracked":
										UpperCamelCase__     :Optional[Any]							      =				value
					else:
										UpperCamelCase__     :int							      =				value

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


def     a   (    __a				,      __a     )						->			List[str]:



					'''simple docstring'''






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


def     a   (    __a				,      __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				[]

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

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

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

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

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


def     a   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			List[str]:



					'''simple docstring'''






					UpperCamelCase__     :int							      =				full_name.split('''conv_layers.'''     )[-1]
					UpperCamelCase__     :str							      =				name.split('''.'''     )
					UpperCamelCase__     :Dict							      =				int(items[0]     )
					UpperCamelCase__     :Optional[int]							      =				int(items[1]     )

					if type_id == 0:
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''     )
															UpperCamelCase__     :Any							      =				value
															logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''     )
															UpperCamelCase__     :Optional[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.'''     )
															UpperCamelCase__     :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.'''     )
															UpperCamelCase__     :List[str]							      =				value
															logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
					else:
										unused_weights.append(__a     )


@torch.no_grad()
def     a   (    __a				,      __a				,      __a				,      __a=None				,      __a=None				,      __a=None				,      )						->			Optional[Any]:



					'''simple docstring'''






					if config_path is not None:
										UpperCamelCase__     :Optional[int]							      =				SpeechTaConfig.from_pretrained(__a     )
					else:
										UpperCamelCase__     :Any							      =				SpeechTaConfig()

					if task == "s2t":
										UpperCamelCase__     :Dict							      =				config.max_text_positions
										UpperCamelCase__     :Optional[Any]							      =				SpeechTaForSpeechToText(__a     )
					elif task == "t2s":
										UpperCamelCase__     :Optional[int]							      =				1876
										UpperCamelCase__     :List[Any]							      =				600
										UpperCamelCase__     :int							      =				config.max_speech_positions
										UpperCamelCase__     :str							      =				SpeechTaForTextToSpeech(__a     )
					elif task == "s2s":
										UpperCamelCase__     :Union[str, Any]							      =				1876
										UpperCamelCase__     :Union[str, Any]							      =				config.max_speech_positions
										UpperCamelCase__     :List[str]							      =				SpeechTaForSpeechToSpeech(__a     )
					else:
										raise ValueError(f'''Unknown task name: {task}'''     )

					if vocab_path:
										UpperCamelCase__     :List[Any]							      =				SpeechTaTokenizer(__a				,      model_max_length=config.max_text_positions     )

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

					UpperCamelCase__     :List[str]							      =				SpeechTaFeatureExtractor()
					UpperCamelCase__     :Union[str, Any]							      =				SpeechTaProcessor(tokenizer=__a				,      feature_extractor=__a     )
					processor.save_pretrained(__a     )

					UpperCamelCase__     :Any							      =				torch.load(__a     )
					recursively_load_weights(fairseq_checkpoint['''model''']				,      __a				,      __a     )

					model.save_pretrained(__a     )

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


if __name__ == "__main__":
		__snake_case							   =  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.'''
		)

		__snake_case							   =  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'''
import qiskit


def     a   (    __a				,      __a     )						->			qiskit.result.counts.Counts:



					'''simple docstring'''






					UpperCamelCase__     :int							      =				qiskit.Aer.get_backend('''aer_simulator'''     )

					# Create a Quantum Circuit acting on the q register
					UpperCamelCase__     :Any							      =				qiskit.QuantumCircuit(__a				,      __a     )

					# Map the quantum measurement to the classical bits
					circuit.measure([0]				,      [0]     )

					# Execute the circuit on the simulator
					UpperCamelCase__     :Optional[int]							      =				qiskit.execute(__a				,      __a				,      shots=1000     )

					# Return the histogram data of the results of the experiment.
					return job.result().get_counts(__a     )


if __name__ == "__main__":
		print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")