





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCAmelCase_							=				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



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

							if weight_type is not None:
														lowercase_	:   Tuple  				=     getattr(lowercase				,       lowercase				).shape
							else:
														lowercase_	:   Any  				=     hf_pointer.shape

							assert hf_shape == value.shape, (
							    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_	:   Optional[Any]  				=     value
							elif weight_type == "weight_g":
														lowercase_	:   Tuple  				=     value
							elif weight_type == "weight_v":
														lowercase_	:   Dict  				=     value
							elif weight_type == "bias":
														lowercase_	:   Optional[int]  				=     value
							else:
														lowercase_	:   Tuple  				=     value

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





def  __magic_name__  (				lowercase				,       lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   Dict  				=     []
							lowercase_	:   Optional[Any]  				=     fairseq_model.state_dict()

							lowercase_	:   List[Any]  				=     hf_model.feature_extractor
							lowercase_	:   Tuple  				=     hf_model.adapter

							for name, value in fairseq_dict.items():
														lowercase_	:   List[Any]  				=     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowercase				,       lowercase				,       lowercase				,       lowercase				,       hf_model.config.feat_extract_norm == """group"""				,       )
																					lowercase_	:   str  				=     True
														elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]				):
																					load_adapter(lowercase				,       lowercase				,       lowercase				,       lowercase				)
																					lowercase_	:   Optional[int]  				=     True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0]:
																																			lowercase_	:   List[Any]  				=     True
																																			if "*" in mapped_key:
																																										lowercase_	:   Dict  				=     name.split(lowercase				)[0].split("""."""				)[-2]
																																										lowercase_	:   List[str]  				=     mapped_key.replace("""*"""				,       lowercase				)
																																			if "weight_g" in name:
																																										lowercase_	:   Optional[int]  				=     """weight_g"""
																																			elif "weight_v" in name:
																																										lowercase_	:   Optional[Any]  				=     """weight_v"""
																																			elif "bias" in name:
																																										lowercase_	:   Optional[Any]  				=     """bias"""
																																			elif "weight" in name:
																																										lowercase_	:   int  				=     """weight"""
																																			else:
																																										lowercase_	:   Union[str, Any]  				=     None
																																			set_recursively(lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				)
																												continue
														if not is_used:
																					unused_weights.append(lowercase				)

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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     full_name.split("""conv_layers."""				)[-1]
							lowercase_	:   Union[str, Any]  				=     name.split("""."""				)
							lowercase_	:   Optional[int]  				=     int(items[0]				)
							lowercase_	:   Tuple  				=     int(items[1]				)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    f"""{full_name} has size {value.shape}, but"""
																					    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																					)
																					lowercase_	:   Tuple  				=     value
																					logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""				)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    f"""{full_name} has size {value.shape}, but"""
																					    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																					)
																					lowercase_	:   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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																					    " found."
																					)
																					lowercase_	:   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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    f"""{full_name} has size {value.shape}, but"""
																					    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																					)
																					lowercase_	:   Tuple  				=     value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(lowercase				)





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   List[str]  				=     full_name.split("""adaptor."""				)[-1]
							lowercase_	:   Union[str, Any]  				=     name.split("""."""				)

							if items[1].isdigit():
														lowercase_	:   Dict  				=     int(items[1]				)
							else:
														lowercase_	:   Optional[int]  				=     None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																												lowercase_	:   Union[str, Any]  				=     value
																												logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""				)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																												lowercase_	:   List[Any]  				=     value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																												lowercase_	:   int  				=     value
																												logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""				)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																												lowercase_	:   Optional[int]  				=     value
																												logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""				)
							elif isinstance(lowercase				,       lowercase				):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
																					lowercase_	:   str  				=     value
																					logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""				)
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
																					lowercase_	:   int  				=     value
																					logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""				)
							else:
														unused_weights.append(lowercase				)





def  __magic_name__  (				lowercase				)				-> str:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   Tuple  				=     emb.weight.shape
							lowercase_	:   Optional[int]  				=     nn.Linear(lowercase				,       lowercase				,       bias=lowercase				)
							lowercase_	:   Optional[Any]  				=     emb.weight.data
							return lin_layer





@torch.no_grad()
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				,       )				-> Dict:

							"""simple docstring"""



							lowercase_	:   List[str]  				=     WavaVecaConfig.from_pretrained(
							    lowercase				,       add_adapter=lowercase				,       adapter_stride=lowercase				,       adapter_kernel_size=lowercase				,       use_auth_token=lowercase				,       output_hidden_size=lowercase				,       )
							lowercase_	:   int  				=     MBartConfig.from_pretrained(lowercase				)

							# load model
							lowercase_				,		lowercase_				,		lowercase_	:   Optional[int]  				=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]				,       arg_overrides={
							        """config_yaml""": config_yaml_path,
							        """data""": """/""".join(dict_path.split("""/"""				)[:-1]				),
							        """w2v_path""": checkpoint_path,
							        """load_pretrained_decoder_from""": None,
							    }				,       )
							lowercase_	:   Any  				=     model[0].eval()

							# load feature extractor
							lowercase_	:   Union[str, Any]  				=     WavaVecaFeatureExtractor.from_pretrained(lowercase				,       use_auth_token=lowercase				)

							# set weights for wav2vec2 encoder
							lowercase_	:   Any  				=     WavaVecaModel(lowercase				)

							recursively_load_weights_wavaveca(model.encoder				,       lowercase				)

							# load decoder weights
							lowercase_	:   int  				=     MBartForCausalLM(lowercase				)
							lowercase_				,		lowercase_	:   List[Any]  				=     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,       strict=lowercase				)
							logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""				)
							logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""				)

							lowercase_	:   Optional[int]  				=     SpeechEncoderDecoderModel(encoder=lowercase				,       decoder=lowercase				)
							lowercase_	:   Any  				=     False

							lowercase_	:   str  				=     MBartaaTokenizer(lowercase				)
							tokenizer.save_pretrained(lowercase				)

							lowercase_	:   str  				=     hf_wavavec.config.to_dict()
							lowercase_	:   Optional[Any]  				=     tokenizer.pad_token_id
							lowercase_	:   Tuple  				=     tokenizer.bos_token_id
							lowercase_	:   Dict  				=     tokenizer.eos_token_id
							lowercase_	:   List[Any]  				=     """mbart50"""
							lowercase_	:   List[str]  				=     """wav2vec2"""

							lowercase_	:   Tuple  				=     tokenizer.eos_token_id
							lowercase_	:   str  				=     250004
							lowercase_	:   Union[str, Any]  				=     tokenizer.eos_token_id

							lowercase_	:   List[Any]  				=     SpeechEncoderDecoderConfig.from_dict(lowercase				)

							hf_wavavec.save_pretrained(lowercase				)
							feature_extractor.save_pretrained(lowercase				)


if __name__ == "__main__":
							UpperCAmelCase_							=				argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
							parser.add_argument(
							    """--encoder_config_path""",
							    default="""facebook/wav2vec2-xls-r-1b""",
							    type=str,
							    help="""Path to hf encoder wav2vec2 checkpoint config""",
							)
							parser.add_argument(
							    """--decoder_config_path""",
							    default="""facebook/mbart-large-50-one-to-many-mmt""",
							    type=str,
							    help="""Path to hf decoder checkpoint config""",
							)
							parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
							parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
							parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
							parser.add_argument("""--encoder_output_dim""", default=1_024, type=int, help="""encoder output dim""")
							parser.add_argument("""--start_token_id""", default=250_004, type=int, help="""`decoder_start_token_id` of model config""")

							UpperCAmelCase_							=				parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)






import qiskit





def  __magic_name__  (				lowercase				,       lowercase				)				-> qiskit.result.counts.Counts:

							"""simple docstring"""



							lowercase_	:   Dict  				=     qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							lowercase_	:   str  				=     qiskit.QuantumCircuit(lowercase				,       lowercase				)

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

							# Execute the circuit on the simulator
							lowercase_	:   List[Any]  				=     qiskit.execute(lowercase				,       lowercase				,       shots=1000				)

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


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