




"""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__				(       snake_case__       )		->      List[Any]:
			return 1.0 / (1.0 + np.exp(-_outputs       ))



def 					a__				(       snake_case__       )		->      int:
			lowerCamelCase       =      np.max(_outputs ,				axis=-1 ,				keepdims=snake_case__       )
			lowerCamelCase       =      np.exp(_outputs - maxes       )
			return shifted_exp / shifted_exp.sum(axis=-1 ,				keepdims=snake_case__       )






class 					__magic_name__							( _lowerCAmelCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		'sigmoid'
				__UpperCamelCase    						=		'softmax'
				__UpperCamelCase    						=		'none'






@add_end_docstrings(
    _lowerCAmelCase		,      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 					__magic_name__							( _lowerCAmelCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		ClassificationFunction.NONE





				def __init__( self	,				**_a				):





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

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





				def      _lowerCAmelCase       ( self	,				_a=None	,				_a=None	,				_a=""	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      tokenizer_kwargs

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

							if isinstance(_lowerCAmelCase	,				_lowerCAmelCase				) or top_k is None:
										lowerCamelCase       =      top_k
										lowerCamelCase       =      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`."""	,				_lowerCAmelCase	,				)
										if return_all_scores:
													lowerCamelCase       =      None
										else:
													lowerCamelCase       =      1

							if isinstance(_lowerCAmelCase	,				_lowerCAmelCase				):
										lowerCamelCase       =      ClassificationFunction[function_to_apply.upper()]

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





				def __call__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      super().__call__(*_lowerCAmelCase	,				**_lowerCAmelCase				)
							# TODO try and retrieve it in a nicer way from _sanitize_parameters.
							lowerCamelCase       =      """top_k""" not in kwargs
							if isinstance(args[0]	,				_lowerCAmelCase				) and _legacy:
										# This pipeline is odd, and return a list when single item is run
										return [result]
							else:
										return result





				def      _lowerCAmelCase       ( self	,				_a	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.framework
							if isinstance(_lowerCAmelCase	,				_lowerCAmelCase				):
										return self.tokenizer(**_lowerCAmelCase	,				return_tensors=_lowerCAmelCase	,				**_lowerCAmelCase				)
							elif isinstance(_lowerCAmelCase	,				_lowerCAmelCase				) and len(_lowerCAmelCase				) == 1 and isinstance(inputs[0]	,				_lowerCAmelCase				) 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=_lowerCAmelCase	,				**_lowerCAmelCase				)
							elif isinstance(_lowerCAmelCase	,				_lowerCAmelCase				):
										# 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(_lowerCAmelCase	,				return_tensors=_lowerCAmelCase	,				**_lowerCAmelCase				)





				def      _lowerCAmelCase       ( self	,				_a				):





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





				def      _lowerCAmelCase       ( self	,				_a	,				_a=None	,				_a=1	,				_a=True				):





							"""simple docstring"""







							if function_to_apply is None:
										if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
													lowerCamelCase       =      ClassificationFunction.SIGMOID
										elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
													lowerCamelCase       =      ClassificationFunction.SOFTMAX
										elif hasattr(self.model.config	,				"""function_to_apply"""				) and function_to_apply is None:
													lowerCamelCase       =      self.model.config.function_to_apply
										else:
													lowerCamelCase       =      ClassificationFunction.NONE

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

							if function_to_apply == ClassificationFunction.SIGMOID:
										lowerCamelCase       =      sigmoid(_lowerCAmelCase				)
							elif function_to_apply == ClassificationFunction.SOFTMAX:
										lowerCamelCase       =      softmax(_lowerCAmelCase				)
							elif function_to_apply == ClassificationFunction.NONE:
										lowerCamelCase       =      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()}

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









"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase							:    Optional[int]  							=							logging.get_logger(__name__)

lowerCAmelCase							:    Optional[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""",
    """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""",
}
lowerCAmelCase							:    Optional[Any]  							=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      List[Any]:
			for attribute in key.split("""."""       ):
						lowerCamelCase       =      getattr(snake_case__ ,				snake_case__       )

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

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



def 					a__				(       snake_case__ ,				snake_case__       )		->      Tuple:
			lowerCamelCase       =      []
			lowerCamelCase       =      fairseq_model.state_dict()

			lowerCamelCase       =      hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			lowerCamelCase       =      None

			for name, value in fairseq_dict.items():
						lowerCamelCase       =      False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				hf_model.config.feat_extract_norm == """group""" ,				)
									lowerCamelCase       =      True
						elif name.split("""."""       )[0] == "proj":
									lowerCamelCase       =      fairseq_model.proj
									lowerCamelCase       =      True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
															lowerCamelCase       =      True
															if "*" in mapped_key:
																		lowerCamelCase       =      name.split(snake_case__       )[0].split("""."""       )[-2]
																		lowerCamelCase       =      mapped_key.replace("""*""" ,				snake_case__       )
															if "weight_g" in name:
																		lowerCamelCase       =      """weight_g"""
															elif "weight_v" in name:
																		lowerCamelCase       =      """weight_v"""
															elif "bias" in name:
																		lowerCamelCase       =      """bias"""
															elif "weight" in name:
																		lowerCamelCase       =      """weight"""
															else:
																		lowerCamelCase       =      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}'       )

			return proj_weight



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[int]:
			lowerCamelCase       =      full_name.split("""conv_layers."""       )[-1]
			lowerCamelCase       =      name.split("""."""       )
			lowerCamelCase       =      int(items[0]       )
			lowerCamelCase       =      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.'
									)
									lowerCamelCase       =      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.'
									)
									lowerCamelCase       =      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."
									)
									lowerCamelCase       =      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.'
									)
									lowerCamelCase       =      value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
			else:
						unused_weights.append(snake_case__       )



def 					a__				(       snake_case__       )		->      Tuple:
			lowerCamelCase   ,     lowerCamelCase       =      emb.weight.shape
			lowerCamelCase       =      nn.Linear(snake_case__ ,				snake_case__ ,				bias=snake_case__       )
			lowerCamelCase       =      emb.weight.data
			return lin_layer



def 					a__				(       snake_case__       )		->      Optional[int]:
			with open(snake_case__ ,				"""r""" ,				encoding="""utf-8"""       ) as f:
						lowerCamelCase       =      f.readlines()
						lowerCamelCase       =      [line.split(""" """       )[0] for line in lines]

			lowerCamelCase       =      len(snake_case__       )

			lowerCamelCase       =      {
			    """<s>""": 0,
			    """<pad>""": 1,
			    """</s>""": 2,
			    """<unk>""": 3,
			}

			vocab_dict.update(dict(zip(snake_case__ ,				range(4 ,				num_words + 4       )       )       )       )
			return vocab_dict



@torch.no_grad()
def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				)		->      Optional[int]:
			lowerCamelCase       =      WavaVecaConfig.from_pretrained(snake_case__       )
			lowerCamelCase       =      SpeechaTextaConfig.from_pretrained(
			    snake_case__ ,				vocab_size=snake_case__ ,				decoder_layers=snake_case__ ,				do_stable_layer_norm=snake_case__       )

			lowerCamelCase       =      WavaVecaFeatureExtractor(
			    feature_size=1 ,				sampling_rate=1_60_00 ,				padding_value=0 ,				do_normalize=snake_case__ ,				return_attention_mask=snake_case__ ,				)

			lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path] ,				arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
			lowerCamelCase       =      model[0].eval()

			# set weights for wav2vec2 encoder
			lowerCamelCase       =      WavaVecaModel(snake_case__       )
			lowerCamelCase       =      recursively_load_weights_wavaveca(model.encoder ,				snake_case__       )

			lowerCamelCase       =      SpeechaTextaForCausalLM(snake_case__       )
			lowerCamelCase   ,     lowerCamelCase       =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,				strict=snake_case__       )

			# set output linear layer
			unexpected_keys.remove("""embed_out"""       )
			lowerCamelCase       =      nn.Parameter(model.decoder.embed_out.detach()       )

			# layer norm is init to identity matrix so leaving it is fine
			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}'       )

			lowerCamelCase       =      SpeechEncoderDecoderModel(encoder=snake_case__ ,				decoder=snake_case__       )
			lowerCamelCase       =      False

			# add projection layer
			lowerCamelCase       =      nn.Parameter(projection_layer.weight       )
			lowerCamelCase       =      nn.Parameter(projection_layer.bias       )

			lowerCamelCase       =      create_vocab_dict(snake_case__       )

			with open(os.path.join(snake_case__ ,				"""vocab.json"""       ) ,				"""w"""       ) as fp:
						json.dump(snake_case__ ,				snake_case__       )

			lowerCamelCase       =      SpeechaTextaTokenizer(os.path.join(snake_case__ ,				"""vocab.json"""       )       )
			tokenizer.save_pretrained(snake_case__       )

			lowerCamelCase       =      hf_wavavec.config.to_dict()
			lowerCamelCase       =      tokenizer.pad_token_id
			lowerCamelCase       =      tokenizer.bos_token_id
			lowerCamelCase       =      tokenizer.eos_token_id
			lowerCamelCase       =      """speech_to_text_2"""
			lowerCamelCase       =      """wav2vec2"""

			lowerCamelCase       =      SpeechEncoderDecoderConfig.from_dict(snake_case__       )

			hf_wavavec.save_pretrained(snake_case__       )
			feature_extractor.save_pretrained(snake_case__       )


if __name__ == "__main__":
					lowerCAmelCase							:    Any  							=							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(
					    """--encoder_config_path""",
					    default="""facebook/wav2vec2-large-lv60""",
					    type=str,
					    help="""Path to hf encoder wav2vec2 checkpoint config""",
					)
					parser.add_argument(
					    """--decoder_config_path""",
					    default="""facebook/s2t-small-mustc-en-fr-st""",
					    type=str,
					    help="""Path to hf decoder s2t checkpoint config""",
					)
					parser.add_argument("""--vocab_size""", default=1_0224, type=int, help="""Vocab size of decoder""")
					parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

					lowerCAmelCase							:    str  							=							parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    vocab_size=args.vocab_size,
					    num_decoder_layers=args.num_decoder_layers,
					)



