


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

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




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Union[str, Any]:
       for attribute in key.split('.'					):
              lowercase__:       str        =       getattr(snake_case      ,							snake_case					)

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

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




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




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Dict:
       lowercase__:       Any        =       []

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

              lowercase__:       List[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'      ,							)
                     lowercase__:       str        =       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__:       List[str]        =       key.split('.*.'					)
                                   if prefix in name and suffix in name:
                                          lowercase__:       Union[str, Any]        =       suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   lowercase__:       Union[str, Any]        =       True
                                   if "*" in mapped_key:
                                          lowercase__:       int        =       name.split(snake_case					)[0].split('.'					)[-2]
                                          lowercase__:       List[Any]        =       mapped_key.replace('*'      ,							snake_case					)
                                   if "weight_g" in name:
                                          lowercase__:       Optional[int]        =       'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__:       int        =       'weight_v'
                                   elif "bias" in name:
                                          lowercase__:       Optional[Any]        =       'bias'
                                   elif "weight" in name:
                                          lowercase__:       Optional[int]        =       'weight'
                                   elif "running_mean" in name:
                                          lowercase__:       int        =       'running_mean'
                                   elif "running_var" in name:
                                          lowercase__:       List[Any]        =       'running_var'
                                   elif "num_batches_tracked" in name:
                                          lowercase__:       int        =       'num_batches_tracked'
                                   else:
                                          lowercase__:       Any        =       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        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    List[str]:
       lowercase__:       Optional[Any]        =       full_name.split('conv_layers.'					)[-1]
       lowercase__:       int        =       name.split('.'					)
       lowercase__:       Tuple        =       int(items[0]					)
       lowercase__:       List[str]        =       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__:       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.'					)
                     lowercase__:       List[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.'					)
                     lowercase__:       List[str]        =       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__:       Optional[int]        =       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        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case=None      ,							snake_case=None      ,							snake_case=None      ,							)  ->    int:
       if config_path is not None:
              lowercase__:       List[Any]        =       SpeechTaConfig.from_pretrained(snake_case					)
       else:
              lowercase__:       str        =       SpeechTaConfig()

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

       if vocab_path:
              lowercase__:       List[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
              lowercase__:       List[Any]        =       AddedToken('<mask>'      ,							lstrip=snake_case      ,							rstrip=snake_case					)
              lowercase__:       Optional[Any]        =       mask_token
              tokenizer.add_special_tokens({'mask_token': mask_token}					)
              tokenizer.add_tokens(['<ctc_blank>']					)

       lowercase__:       List[Any]        =       SpeechTaFeatureExtractor()
       lowercase__:       str        =       SpeechTaProcessor(tokenizer=snake_case      ,							feature_extractor=snake_case					)
       processor.save_pretrained(snake_case					)

       lowercase__:       Optional[Any]        =       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__":
 __lowerCAmelCase			=							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			=							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,
 )








import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def        snake_case_					(  snake_case = 3					)  ->    qiskit.result.counts.Counts:
       if isinstance(snake_case      ,							snake_case					):
              raise TypeError('number of qubits must be a integer.'					)
       if number_of_qubits <= 0:
              raise ValueError('number of qubits must be > 0.'					)
       if math.floor(snake_case					) != number_of_qubits:
              raise ValueError('number of qubits must be exact integer.'					)
       if number_of_qubits > 10:
              raise ValueError('number of qubits too large to simulate(>10).'					)

       lowercase__:       str        =       QuantumRegister(snake_case      ,							'qr'					)
       lowercase__:       str        =       ClassicalRegister(snake_case      ,							'cr'					)

       lowercase__:       List[Any]        =       QuantumCircuit(snake_case      ,							snake_case					)

       lowercase__:       int        =       number_of_qubits

       for i in range(snake_case					):
              quantum_circuit.h(number_of_qubits - i - 1					)
              counter -= 1
              for j in range(snake_case					):
                     quantum_circuit.cp(np.pi / 2 ** (counter - j)      ,							snake_case      ,							snake_case					)

       for k in range(number_of_qubits // 2					):
              quantum_circuit.swap(snake_case      ,							number_of_qubits - k - 1					)

       # measure all the qubits
       quantum_circuit.measure(snake_case      ,							snake_case					)
       # simulate with 10000 shots
       lowercase__:       str        =       Aer.get_backend('qasm_simulator'					)
       lowercase__:       Union[str, Any]        =       execute(snake_case      ,							snake_case      ,							shots=1_00_00					)

       return job.result().get_counts(snake_case					)


if __name__ == "__main__":
 print(
     F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
 )


