import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCAmelCase__     						=			['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
lowerCAmelCase__     						=			{'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
    raise Exception('requires fairseq >= 0.9.0')


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

lowerCAmelCase__     						=			' Hello world! cécé herlolip'

lowerCAmelCase__     						=			[
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(lowerCAmelCase__     ,  lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = dct.pop(lowerCAmelCase__     )
       lowerCAmelCase__					     = val






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = torch.load(lowerCAmelCase__     ,  map_location='cpu'     )
       lowerCAmelCase__					     = torch.hub.load('pytorch/fairseq'     ,  'bart.large.cnn'     ).eval()
       hub_interface.model.load_state_dict(sd['model']     )
       return hub_interface






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__     , lowerCAmelCase__					     = emb.weight.shape
       lowerCAmelCase__					     = nn.Linear(lowerCAmelCase__     ,  lowerCAmelCase__     ,  bias=lowerCAmelCase__     )
       lowerCAmelCase__					     = emb.weight.data
       return lin_layer






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ):
       if not os.path.exists(lowerCAmelCase__     ):
              lowerCAmelCase__					     = torch.hub.load('pytorch/fairseq'     ,  lowerCAmelCase__     ).eval()
       else:
              lowerCAmelCase__					     = load_xsum_checkpoint(lowerCAmelCase__     )

       bart.model.upgrade_state_dict(bart.model.state_dict()     )
       if hf_checkpoint_name is None:
              lowerCAmelCase__					     = checkpoint_path.replace('.'     ,  '-'     )
       lowerCAmelCase__					     = BartConfig.from_pretrained(lowerCAmelCase__     )
       lowerCAmelCase__					     = bart.encode(lowerCAmelCase__     ).unsqueeze(0     )
       lowerCAmelCase__					     = BartTokenizer.from_pretrained(lowerCAmelCase__     ).encode(lowerCAmelCase__     ,  return_tensors='pt'     ).unsqueeze(0     )
       if not torch.eq(lowerCAmelCase__     ,  lowerCAmelCase__     ).all():
              raise ValueError(
                  F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""     )

       if checkpoint_path == "bart.large.mnli":
              lowerCAmelCase__					     = bart.state_dict()
              remove_ignore_keys_(lowerCAmelCase__     )
              lowerCAmelCase__					     = state_dict['model.decoder.embed_tokens.weight']
              for src, dest in mnli_rename_keys:
                     rename_key(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = BartForSequenceClassification(lowerCAmelCase__     ).eval()
              model.load_state_dict(lowerCAmelCase__     )
              lowerCAmelCase__					     = bart.predict('mnli'     ,  lowerCAmelCase__     ,  return_logits=lowerCAmelCase__     )
              lowerCAmelCase__					     = model(lowerCAmelCase__     )[0]  # logits
       else:  # no classification heads to worry about
              lowerCAmelCase__					     = bart.model.state_dict()
              remove_ignore_keys_(lowerCAmelCase__     )
              lowerCAmelCase__					     = state_dict['decoder.embed_tokens.weight']
              lowerCAmelCase__					     = bart.extract_features(lowerCAmelCase__     )
              if hf_checkpoint_name == "facebook/bart-large":
                     lowerCAmelCase__					     = BartModel(lowerCAmelCase__     ).eval()
                     model.load_state_dict(lowerCAmelCase__     )
                     lowerCAmelCase__					     = model(lowerCAmelCase__     ).model[0]
              else:
                     lowerCAmelCase__					     = BartForConditionalGeneration(lowerCAmelCase__     ).eval()  # an existing summarization ckpt
                     model.model.load_state_dict(lowerCAmelCase__     )
                     if hasattr(lowerCAmelCase__     ,  'lm_head'     ):
                            lowerCAmelCase__					     = make_linear_from_emb(model.model.shared     )
                     lowerCAmelCase__					     = model.model(lowerCAmelCase__     )[0]

    # Check results
       if fairseq_output.shape != new_model_outputs.shape:
              raise ValueError(
                  F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""     )
       if (fairseq_output != new_model_outputs).any().item():
              raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'     )
       Path(lowerCAmelCase__     ).mkdir(exist_ok=lowerCAmelCase__     )
       model.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
    )
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument(
        '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
    )
    lowerCAmelCase__     						=			parser.parse_args()
    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)

import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['input_values', 'attention_mask']




 def __init__(      self :  Union[str, Any]      ,   lowercase__ :  int = 1      ,   lowercase__ :  int = 16_000      ,   lowercase__ :  float = 0.0      ,   lowercase__ :  bool = False      ,   lowercase__ :  int = 80      ,   lowercase__ :  int = 16      ,   lowercase__ :  int = 64      ,   lowercase__ :  str = "hann_window"      ,   lowercase__ :  float = 1.0      ,   lowercase__ :  float = 80      ,   lowercase__ :  float = 7_600      ,   lowercase__ :  float = 1e-10      ,   lowercase__ :  int = 2      ,   lowercase__ :  bool = True      ,   **lowercase__ :  Optional[int]      ,   ):







        '''simple docstring'''



        super().__init__(feature_size=lowercase__      ,   sampling_rate=lowercase__      ,   padding_value=lowercase__      ,   **lowercase__)
        lowerCAmelCase__					     = do_normalize
        lowerCAmelCase__					     = return_attention_mask

        lowerCAmelCase__					     = num_mel_bins
        lowerCAmelCase__					     = hop_length
        lowerCAmelCase__					     = win_length
        lowerCAmelCase__					     = win_function
        lowerCAmelCase__					     = frame_signal_scale
        lowerCAmelCase__					     = fmin
        lowerCAmelCase__					     = fmax
        lowerCAmelCase__					     = mel_floor
        lowerCAmelCase__					     = reduction_factor

        lowerCAmelCase__					     = win_length * sampling_rate // 1_000
        lowerCAmelCase__					     = hop_length * sampling_rate // 1_000
        lowerCAmelCase__					     = optimal_fft_length(self.sample_size)
        lowerCAmelCase__					     = (self.n_fft // 2) + 1

        lowerCAmelCase__					     = window_function(window_length=self.sample_size      ,   name=self.win_function      ,   periodic=lowercase__)

        lowerCAmelCase__					     = mel_filter_bank(
            num_frequency_bins=self.n_freqs      ,   num_mel_filters=self.num_mel_bins      ,   min_frequency=self.fmin      ,   max_frequency=self.fmax      ,   sampling_rate=self.sampling_rate      ,   norm='slaney'      ,   mel_scale='slaney'      ,   )

        if frame_signal_scale != 1.0:
               warnings.warn(
                   'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'      ,   lowercase__      ,   )
        if reduction_factor != 2.0:
               warnings.warn(
                   'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'      ,   lowercase__      ,   )




 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 		__snake_case						(      lowercase__ :  List[np.ndarray]      ,   lowercase__ :  List[np.ndarray]      ,   lowercase__ :  float = 0.0):







        '''simple docstring'''



        if attention_mask is not None:
               lowerCAmelCase__					     = np.array(lowercase__      ,   np.intaa)
               lowerCAmelCase__					     = []

               for vector, length in zip(lowercase__      ,   attention_mask.sum(-1)):
                      lowerCAmelCase__					     = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                      if length < normed_slice.shape[0]:
                             lowerCAmelCase__					     = padding_value

                      normed_input_values.append(lowercase__)
        else:
               lowerCAmelCase__					     = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

        return normed_input_values




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  np.ndarray      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = spectrogram(
            lowercase__      ,   window=self.window      ,   frame_length=self.sample_size      ,   hop_length=self.sample_stride      ,   fft_length=self.n_fft      ,   mel_filters=self.mel_filters      ,   mel_floor=self.mel_floor      ,   log_mel='log10'      ,   )
        return log_mel_spec.T




 def __call__(      self :  str      ,   lowercase__ :  Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,   lowercase__ :  Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,   lowercase__ :  Union[bool, str, PaddingStrategy] = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  bool = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  Optional[int] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        if audio is None and audio_target is None:
               raise ValueError('You must provide either `audio` or `audio_target` values.')

        if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                          F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                          F""" {self.sampling_rate} and not {sampling_rate}.""")
        else:
               logger.warning(
                   'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
                   'Failing to do so can result in silent errors that might be hard to debug.')

        if audio is not None:
               lowerCAmelCase__					     = self._process_audio(
                   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   **lowercase__      ,   )
        else:
               lowerCAmelCase__					     = None

        if audio_target is not None:
               lowerCAmelCase__					     = self._process_audio(
                   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   **lowercase__      ,   )

               if inputs is None:
                      return inputs_target
               else:
                      lowerCAmelCase__					     = inputs_target['input_values']
                      lowerCAmelCase__					     = inputs_target.get('attention_mask')
                      if decoder_attention_mask is not None:
                             lowerCAmelCase__					     = decoder_attention_mask

        return inputs




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,   lowercase__ :  bool = False      ,   lowercase__ :  Union[bool, str, PaddingStrategy] = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  bool = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   **lowercase__ :  Optional[Any]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = isinstance(lowercase__      ,   np.ndarray) and len(speech.shape) > 1
        if is_batched_numpy and len(speech.shape) > 2:
               raise ValueError(F"""Only mono-channel audio is supported for input to {self}""")
        lowerCAmelCase__					     = is_batched_numpy or (
            isinstance(lowercase__      ,   (list, tuple)) and (isinstance(speech[0]      ,   (np.ndarray, tuple, list)))
        )

        if is_batched:
               lowerCAmelCase__					     = [np.asarray(lowercase__      ,   dtype=np.floataa) for speech in speech]
        elif not is_batched and not isinstance(lowercase__      ,   np.ndarray):
               lowerCAmelCase__					     = np.asarray(lowercase__      ,   dtype=np.floataa)
        elif isinstance(lowercase__      ,   np.ndarray) and speech.dtype is np.dtype(np.floataa):
               lowerCAmelCase__					     = speech.astype(np.floataa)

        # always return batch
        if not is_batched:
               lowerCAmelCase__					     = [speech]

        # needed to make pad() work on spectrogram inputs
        lowerCAmelCase__					     = self.feature_size

        # convert into correct format for padding
        if is_target:
               lowerCAmelCase__					     = [self._extract_mel_features(lowercase__) for waveform in speech]
               lowerCAmelCase__					     = BatchFeature({'input_values': features})
               lowerCAmelCase__					     = self.num_mel_bins
        else:
               lowerCAmelCase__					     = BatchFeature({'input_values': speech})

        lowerCAmelCase__					     = self.pad(
            lowercase__      ,   padding=lowercase__      ,   max_length=lowercase__      ,   truncation=lowercase__      ,   pad_to_multiple_of=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )

        lowerCAmelCase__					     = feature_size_hack

        # convert input values to correct format
        lowerCAmelCase__					     = padded_inputs['input_values']
        if not isinstance(input_values[0]      ,   np.ndarray):
               lowerCAmelCase__					     = [np.asarray(lowercase__      ,   dtype=np.floataa) for array in input_values]
        elif (
            not isinstance(lowercase__      ,   np.ndarray)
            and isinstance(input_values[0]      ,   np.ndarray)
            and input_values[0].dtype is np.dtype(np.floataa)
        ):
               lowerCAmelCase__					     = [array.astype(np.floataa) for array in input_values]
        elif isinstance(lowercase__      ,   np.ndarray) and input_values.dtype is np.dtype(np.floataa):
               lowerCAmelCase__					     = input_values.astype(np.floataa)

        # convert attention_mask to correct format
        lowerCAmelCase__					     = padded_inputs.get('attention_mask')
        if attention_mask is not None:
               lowerCAmelCase__					     = [np.asarray(lowercase__      ,   dtype=np.intaa) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if not is_target and self.do_normalize:
               lowerCAmelCase__					     = (
                   attention_mask
                   if self._get_padding_strategies(lowercase__      ,   max_length=lowercase__) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               lowerCAmelCase__					     = self.zero_mean_unit_var_norm(
                   padded_inputs['input_values']      ,   attention_mask=lowercase__      ,   padding_value=self.padding_value)

        if return_tensors is not None:
               lowerCAmelCase__					     = padded_inputs.convert_to_tensors(lowercase__)

        return padded_inputs




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = super().to_dict()

        # Don't serialize these as they are derived from the other properties.
        lowerCAmelCase__					     = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
        for name in names:
               if name in output:
                      del output[name]

        return output
