"""simple docstring"""





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def        a_  (   _lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       return 1.0 / (1.0 + np.exp(-_outputs  ))





def        a_  (   _lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       lowercase__    :	int     					=			np.max(_outputs     ,				axis=-1     ,				keepdims=_lowerCAmelCase  )
       lowercase__    :	List[Any]     					=			np.exp(_outputs - maxes  )
       return shifted_exp / shifted_exp.sum(axis=-1     ,				keepdims=_lowerCAmelCase  )


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					Tuple										=						"sigmoid"
      lowerCamelCase__	:					int										=						"softmax"
      lowerCamelCase__	:					Union[str, Any]										=						"none"





@add_end_docstrings(
    _a				,							R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "				,							)
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					str										=						False
      lowerCamelCase__	:					Optional[Any]										=						ClassificationFunction.NONE


      def __init__(     self	,    **a							) ->      List[str]:
             super().__init__(**a							)

             self.check_model_type(
                 TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
                 if self.framework == 'tf'
                 else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING							)


      def 	_UpperCAmelCase (     self	,    a=None	,    a=None	,    a=""	,    **a							) ->      Optional[Any]:
             # Using "" as default argument because we're going to use `top_k=None` in user code to declare
             # "No top_k"
             lowercase__    :	Optional[int]     					=			tokenizer_kwargs

             lowercase__    :	Any     					=			{}
             if hasattr(self.model.config	,    'return_all_scores'							) and return_all_scores is None:
                    lowercase__    :	Union[str, Any]     					=			self.model.config.return_all_scores

             if isinstance(a	,    a							) or top_k is None:
                    lowercase__    :	str     					=			top_k
                    lowercase__    :	str     					=			False
             elif return_all_scores is not None:
                    warnings.warn(
                        '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
                        ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'	,    a	,    )
                    if return_all_scores:
                           lowercase__    :	int     					=			None
                    else:
                           lowercase__    :	List[str]     					=			1

             if isinstance(a	,    a							):
                    lowercase__    :	List[Any]     					=			ClassificationFunction[function_to_apply.upper()]

             if function_to_apply is not None:
                    lowercase__    :	Any     					=			function_to_apply
             return preprocess_params, {}, postprocess_params


      def __call__(     self	,    *a	,    **a							) ->      Dict:
             lowercase__    :	Tuple     					=			super().__call__(*a	,    **a							)
             # TODO try and retrieve it in a nicer way from _sanitize_parameters.
             lowercase__    :	Optional[int]     					=			'top_k' not in kwargs
             if isinstance(args[0]	,    a							) and _legacy:
                    # This pipeline is odd, and return a list when single item is run
                    return [result]
             else:
                    return result


      def 	_UpperCAmelCase (     self	,    a	,    **a							) ->      Dict[str, GenericTensor]:
             lowercase__    :	str     					=			self.framework
             if isinstance(a	,    a							):
                    return self.tokenizer(**a	,    return_tensors=a	,    **a							)
             elif isinstance(a	,    a							) and len(a							) == 1 and isinstance(inputs[0]	,    a							) and len(inputs[0]							) == 2:
                    # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                    return self.tokenizer(
                        text=inputs[0][0]	,    text_pair=inputs[0][1]	,    return_tensors=a	,    **a							)
             elif isinstance(a	,    a							):
                    # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                    raise ValueError(
                        'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
                        ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'							)
             return self.tokenizer(a	,    return_tensors=a	,    **a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      List[Any]:
             return self.model(**a							)


      def 	_UpperCAmelCase (     self	,    a	,    a=None	,    a=1	,    a=True							) ->      List[Any]:
             # `_legacy` is used to determine if we're running the naked pipeline and in backward
             # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
             # the more natural result containing the list.
             # Default value before `set_parameters`
             if function_to_apply is None:
                    if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                           lowercase__    :	List[Any]     					=			ClassificationFunction.SIGMOID
                    elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                           lowercase__    :	Optional[int]     					=			ClassificationFunction.SOFTMAX
                    elif hasattr(self.model.config	,    'function_to_apply'							) and function_to_apply is None:
                           lowercase__    :	Tuple     					=			self.model.config.function_to_apply
                    else:
                           lowercase__    :	List[str]     					=			ClassificationFunction.NONE

             lowercase__    :	Any     					=			model_outputs['logits'][0]
             lowercase__    :	List[str]     					=			outputs.numpy()

             if function_to_apply == ClassificationFunction.SIGMOID:
                    lowercase__    :	Tuple     					=			sigmoid(a							)
             elif function_to_apply == ClassificationFunction.SOFTMAX:
                    lowercase__    :	Optional[Any]     					=			softmax(a							)
             elif function_to_apply == ClassificationFunction.NONE:
                    lowercase__    :	Any     					=			outputs
             else:
                    raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}"""							)

             if top_k == 1 and _legacy:
                    return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

             lowercase__    :	Optional[int]     					=			[
                 {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(a							)
             ]
             if not _legacy:
                    dict_scores.sort(key=lambda a							: x["score"]	,    reverse=a							)
                    if top_k is not None:
                           lowercase__    :	List[Any]     					=			dict_scores[:top_k]
             return dict_scores


"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)

_UpperCamelCase     :		List[Any]											=					{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_UpperCamelCase     :		List[str]											=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       for attribute in key.split('.'  ):
              lowercase__    :	Dict     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  )

       if weight_type is not None:
              lowercase__    :	Optional[int]     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  ).shape
       else:
              lowercase__    :	Optional[int]     					=			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__    :	Optional[Any]     					=			value
       elif weight_type == "weight_g":
              lowercase__    :	Dict     					=			value
       elif weight_type == "weight_v":
              lowercase__    :	List[str]     					=			value
       elif weight_type == "bias":
              lowercase__    :	Optional[Any]     					=			value
       else:
              lowercase__    :	List[str]     					=			value

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





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[]
       lowercase__    :	List[str]     					=			fairseq_model.state_dict()

       lowercase__    :	Union[str, Any]     					=			hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__    :	Optional[int]     					=			False
              if "conv_layers" in name:
                     load_conv_layer(
                         _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                     lowercase__    :	Optional[Any]     					=			True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__    :	List[Any]     					=			'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                            if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('.'  )[:-1]  ) != key):
                                          # special case since naming is very similar
                                          continue
                                   lowercase__    :	int     					=			True
                                   if "*" in mapped_key:
                                          lowercase__    :	Optional[int]     					=			name.split(_lowerCAmelCase  )[0].split('.'  )[-2]
                                          lowercase__    :	List[str]     					=			mapped_key.replace('*'     ,				_lowerCAmelCase  )
                                   if "weight_g" in name:
                                          lowercase__    :	List[Any]     					=			'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__    :	int     					=			'weight_v'
                                   elif "bias" in name:
                                          lowercase__    :	Dict     					=			'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase__    :	Union[str, Any]     					=			'weight'
                                   else:
                                          lowercase__    :	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        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	int     					=			full_name.split('conv_layers.'  )[-1]
       lowercase__    :	int     					=			name.split('.'  )
       lowercase__    :	int     					=			int(items[0]  )
       lowercase__    :	Dict     					=			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__    :	Union[str, 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__    :	Optional[int]     					=			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[layer_id].layer_norm.bias.data.shape} was found."""  )
                     lowercase__    :	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[layer_id].layer_norm.weight.data.shape} was found."""  )
                     lowercase__    :	int     					=			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        a_  (   _lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				List[Any]=None     ,				_lowerCAmelCase						:				str=None     ,				_lowerCAmelCase						:				Tuple=True  ):





       '''simple docstring'''

       if config_path is not None:
              lowercase__    :	Any     					=			UniSpeechSatConfig.from_pretrained(_lowerCAmelCase  )
       else:
              lowercase__    :	Any     					=			UniSpeechSatConfig()

       lowercase__    :	Union[str, Any]     					=			''

       if is_finetuned:
              lowercase__    :	Optional[Any]     					=			UniSpeechSatForCTC(_lowerCAmelCase  )
       else:
              lowercase__    :	List[Any]     					=			UniSpeechSatForPreTraining(_lowerCAmelCase  )

       lowercase__					,   lowercase__					,   lowercase__    :	int     					=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
       lowercase__    :	Union[str, Any]     					=			model[0].eval()

       recursively_load_weights(_lowerCAmelCase     ,				_lowerCAmelCase  )

       hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		Tuple											=					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_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
       )
       _UpperCamelCase     :		str											=					parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

