





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 		__magic_name__				( lowercase_				)		->					int:


						'''simple docstring'''


						return 1.0 / (1.0 + np.exp(-_outputs				))
def 		__magic_name__				( lowercase_				)		->					Any:


						'''simple docstring'''


						UpperCamelCase  							= np.max(_outputs						,		axis=-1						,		keepdims=lowercase_				)
						UpperCamelCase  							= np.exp(_outputs - maxes				)
						return shifted_exp / shifted_exp.sum(axis=-1						,		keepdims=lowercase_				)


class 							__UpperCAmelCase							(						snake_case__   ):



						"""simple docstring"""







						lowercase							  =    """sigmoid"""
						lowercase							  =    """softmax"""
						lowercase							  =    """none"""


@add_end_docstrings(
    snake_case__    ,  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							(						snake_case__   ):



						"""simple docstring"""







						lowercase							  =    False
						lowercase							  =    ClassificationFunction.NONE




						def __init__(   self	,		**SCREAMING_SNAKE_CASE				) ->      Optional[int]:





												"""simple docstring"""
												super().__init__(**A_				)

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




						def 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE=None	,		SCREAMING_SNAKE_CASE=None	,		SCREAMING_SNAKE_CASE=""	,		**SCREAMING_SNAKE_CASE				) ->      Optional[int]:





												"""simple docstring"""
												# Using "" as default argument because we're going to use `top_k=None` in user code to declare
												# "No top_k"
												UpperCamelCase  							= tokenizer_kwargs

												UpperCamelCase  							= {}
												if hasattr(self.model.config	,		"return_all_scores"				) and return_all_scores is None:
																		UpperCamelCase  							= self.model.config.return_all_scores

												if isinstance(A_	,		A_				) or top_k is None:
																		UpperCamelCase  							= top_k
																		UpperCamelCase  							= 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:
																								UpperCamelCase  							= None
																		else:
																								UpperCamelCase  							= 1

												if isinstance(A_	,		A_				):
																		UpperCamelCase  							= ClassificationFunction[function_to_apply.upper()]

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




						def __call__(   self	,		*SCREAMING_SNAKE_CASE	,		**SCREAMING_SNAKE_CASE				) ->      str:





												"""simple docstring"""
												UpperCamelCase  							= super().__call__(*A_	,		**A_				)
												# TODO try and retrieve it in a nicer way from _sanitize_parameters.
												UpperCamelCase  							= "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 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE	,		**SCREAMING_SNAKE_CASE				) ->      Dict[str, GenericTensor]:





												"""simple docstring"""
												UpperCamelCase  							= 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 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE				) ->      Dict:





												"""simple docstring"""
												return self.model(**A_				)




						def 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE	,		SCREAMING_SNAKE_CASE=None	,		SCREAMING_SNAKE_CASE=1	,		SCREAMING_SNAKE_CASE=True				) ->      Any:





												"""simple docstring"""
												# `_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:
																								UpperCamelCase  							= ClassificationFunction.SIGMOID
																		elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																								UpperCamelCase  							= ClassificationFunction.SOFTMAX
																		elif hasattr(self.model.config	,		"function_to_apply"				) and function_to_apply is None:
																								UpperCamelCase  							= self.model.config.function_to_apply
																		else:
																								UpperCamelCase  							= ClassificationFunction.NONE

												UpperCamelCase  							= model_outputs["logits"][0]
												UpperCamelCase  							= outputs.numpy()

												if function_to_apply == ClassificationFunction.SIGMOID:
																		UpperCamelCase  							= sigmoid(A_				)
												elif function_to_apply == ClassificationFunction.SOFTMAX:
																		UpperCamelCase  							= softmax(A_				)
												elif function_to_apply == ClassificationFunction.NONE:
																		UpperCamelCase  							= 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()}

												UpperCamelCase  							= [
												    {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(A_				)
												]
												if not _legacy:
																		dict_scores.sort(key=lambda SCREAMING_SNAKE_CASE				: x["score"]	,		reverse=A_				)
																		if top_k is not None:
																								UpperCamelCase  							= dict_scores[:top_k]
												return dict_scores











import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase_												=	{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

   for attribute in key.split("."     ):
      UpperCAmelCase__						=getattr(A				,							A     )

   if weight_type is not None:
      UpperCAmelCase__						=getattr(A				,							A     ).shape
   else:
      UpperCAmelCase__						=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":
      UpperCAmelCase__						=value
   elif weight_type == "weight_g":
      UpperCAmelCase__						=value
   elif weight_type == "weight_v":
      UpperCAmelCase__						=value
   elif weight_type == "bias":
      UpperCAmelCase__						=value
   else:
      UpperCAmelCase__						=value

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






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=[]
   UpperCAmelCase__						=fairseq_model.state_dict()

   UpperCAmelCase__						=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCAmelCase__						=False
      if "conv_layers" in name:
         load_conv_layer(
             A				,							A				,							A				,							A				,							hf_model.config.feat_extract_norm == "group"				,							)
         UpperCAmelCase__						=True
      else:
         for key, mapped_key in MAPPING.items():
            UpperCAmelCase__						="sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

            if key in name or key.split("w2v_model."     )[-1] == name.split("."     )[0]:
               UpperCAmelCase__						=True
               if "*" in mapped_key:
                  UpperCAmelCase__						=name.split(A     )[0].split("."     )[-2]
                  UpperCAmelCase__						=mapped_key.replace("*"				,							A     )
               if "weight_g" in name:
                  UpperCAmelCase__						="weight_g"
               elif "weight_v" in name:
                  UpperCAmelCase__						="weight_v"
               elif "weight" in name:
                  UpperCAmelCase__						="weight"
               elif "bias" in name:
                  UpperCAmelCase__						="bias"
               else:
                  UpperCAmelCase__						=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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=full_name.split("conv_layers."     )[-1]
   UpperCAmelCase__						=name.split("."     )
   UpperCAmelCase__						=int(items[0]     )
   UpperCAmelCase__						=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."""
         )
         UpperCAmelCase__						=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."""
         )
         UpperCAmelCase__						=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."
         )
         UpperCAmelCase__						=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."""
         )
         UpperCAmelCase__						=value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
   else:
      unused_weights.append(A     )






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=SEWConfig()
   if is_finetuned:
      UpperCAmelCase__						=model.wav_encoder.wav_model.cfg
   else:
      UpperCAmelCase__						=model.cfg

   UpperCAmelCase__						=fs_config.conv_bias
   UpperCAmelCase__						=eval(fs_config.conv_feature_layers     )
   UpperCAmelCase__						=[x[0] for x in conv_layers]
   UpperCAmelCase__						=[x[1] for x in conv_layers]
   UpperCAmelCase__						=[x[2] for x in conv_layers]
   UpperCAmelCase__						="gelu"
   UpperCAmelCase__						="layer" if fs_config.extractor_mode == "layer_norm" else "group"
   UpperCAmelCase__						=0.0
   UpperCAmelCase__						=fs_config.activation_fn.name
   UpperCAmelCase__						=fs_config.encoder_embed_dim
   UpperCAmelCase__						=0.02
   UpperCAmelCase__						=fs_config.encoder_ffn_embed_dim
   UpperCAmelCase__						=1e-5
   UpperCAmelCase__						=fs_config.encoder_layerdrop
   UpperCAmelCase__						=fs_config.encoder_attention_heads
   UpperCAmelCase__						=fs_config.conv_pos_groups
   UpperCAmelCase__						=fs_config.conv_pos
   UpperCAmelCase__						=len(A     )
   UpperCAmelCase__						=fs_config.encoder_layers
   UpperCAmelCase__						=fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      UpperCAmelCase__						=model.cfg
      UpperCAmelCase__						=fs_config.final_dropout
      UpperCAmelCase__						=fs_config.layerdrop
   UpperCAmelCase__						=fs_config.activation_dropout
   UpperCAmelCase__						=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   UpperCAmelCase__						=fs_config.attention_dropout
   UpperCAmelCase__						=fs_config.dropout_input
   UpperCAmelCase__						=fs_config.dropout
   UpperCAmelCase__						=fs_config.mask_channel_length
   UpperCAmelCase__						=fs_config.mask_channel_prob
   UpperCAmelCase__						=fs_config.mask_length
   UpperCAmelCase__						=fs_config.mask_prob

   UpperCAmelCase__						="Wav2Vec2FeatureExtractor"
   UpperCAmelCase__						="Wav2Vec2CTCTokenizer"

   return config






@torch.no_grad()
def 				_UpperCAmelCase			(    A				,							A				,							A=None				,							A=None				,							A=True     ):




   '''simple docstring'''

   if is_finetuned:
      UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]				,							arg_overrides={"data": "/".join(dict_path.split("/"     )[:-1]     )}     )
   else:
      UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

   if config_path is not None:
      UpperCAmelCase__						=SEWConfig.from_pretrained(A     )
   else:
      UpperCAmelCase__						=convert_config(model[0]				,							A     )
   UpperCAmelCase__						=model[0].eval()

   UpperCAmelCase__						=True if config.feat_extract_norm == "layer" else False
   UpperCAmelCase__						=WavaVecaFeatureExtractor(
       feature_size=1				,							sampling_rate=16000				,							padding_value=0				,							do_normalize=A				,							return_attention_mask=A				,							)

   if is_finetuned:
      if dict_path:
         UpperCAmelCase__						=Dictionary.load(A     )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCAmelCase__						=target_dict.pad_index
         UpperCAmelCase__						=target_dict.bos_index
         UpperCAmelCase__						=target_dict.pad_index
         UpperCAmelCase__						=target_dict.bos_index
         UpperCAmelCase__						=target_dict.eos_index
         UpperCAmelCase__						=len(target_dict.symbols     )
         UpperCAmelCase__						=os.path.join(A				,							"vocab.json"     )
         if not os.path.isdir(A     ):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A     )     )
            return
         os.makedirs(A				,							exist_ok=A     )
         with open(A				,							"w"				,							encoding="utf-8"     ) as vocab_handle:
            json.dump(target_dict.indices				,							A     )
         UpperCAmelCase__						=WavaVecaCTCTokenizer(
             A				,							unk_token=target_dict.unk_word				,							pad_token=target_dict.pad_word				,							bos_token=target_dict.bos_word				,							eos_token=target_dict.eos_word				,							word_delimiter_token="|"				,							do_lower_case=A				,							)
         UpperCAmelCase__						=WavaVecaProcessor(feature_extractor=A				,							tokenizer=A     )
         processor.save_pretrained(A     )

      UpperCAmelCase__						=SEWForCTC(A     )
   else:
      UpperCAmelCase__						=SEWModel(A     )
      feature_extractor.save_pretrained(A     )

   recursively_load_weights(A				,							A				,							A     )

   hf_model.save_pretrained(A     )


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_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    UpperCamelCase_												=	parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )



