



"""simple docstring"""
import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_lowerCAmelCase					:int  =      logging.get_logger(__name__)

_lowerCAmelCase					:Union[str, Any]  =      {
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_lowerCAmelCase					:Dict  =      [
    'lm_head',
]





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Optional[Any]							):
    for attribute in key.split('''.'''							):
        _UpperCAmelCase							:						List[Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							)

    if weight_type is not None:
        _UpperCAmelCase							:						Union[str, Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							).shape
    else:
        _UpperCAmelCase							:						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":
        _UpperCAmelCase							:						str       							=					value
    elif weight_type == "weight_g":
        _UpperCAmelCase							:						Tuple       							=					value
    elif weight_type == "weight_v":
        _UpperCAmelCase							:						Optional[Any]       							=					value
    elif weight_type == "bias":
        _UpperCAmelCase							:						List[Any]       							=					value
    else:
        _UpperCAmelCase							:						str       							=					value

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





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	Dict							):
    _UpperCAmelCase							:						Any       							=					[]
    _UpperCAmelCase							:						Any       							=					fairseq_model.state_dict()

    if not is_headless:
        _UpperCAmelCase							:						Tuple       							=					hf_model.dataavec_audio.feature_extractor
        _UpperCAmelCase							:						Tuple       							=					hf_model.dataavec_audio.encoder.pos_conv_embed

    else:
        _UpperCAmelCase							:						Dict       							=					hf_model.feature_extractor
        _UpperCAmelCase							:						Optional[int]       							=					hf_model.encoder.pos_conv_embed

    for name, value in fairseq_dict.items():
        _UpperCAmelCase							:						Union[str, Any]       							=					False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , )
            _UpperCAmelCase							:						Dict       							=					True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , )
            _UpperCAmelCase							:						int       							=					True
        else:
            for key, mapped_key in MAPPING.items():
                if not is_headless:
                    _UpperCAmelCase							:						List[str]       							=					'''data2vec_audio.''' + 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]:
                    _UpperCAmelCase							:						Tuple       							=					True
                    if "*" in mapped_key:
                        _UpperCAmelCase							:						Union[str, Any]       							=					name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                        _UpperCAmelCase							:						str       							=					mapped_key.replace('''*'''  , UpperCamelCase__							)
                    if "weight_g" in name:
                        _UpperCAmelCase							:						List[Any]       							=					'''weight_g'''
                    elif "weight_v" in name:
                        _UpperCAmelCase							:						Tuple       							=					'''weight_v'''
                    elif "bias" in name:
                        _UpperCAmelCase							:						List[str]       							=					'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _UpperCAmelCase							:						Optional[Any]       							=					'''weight'''
                    else:
                        _UpperCAmelCase							:						str       							=					None
                    set_recursively(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
                continue
        if not is_used:
            unused_weights.append(UpperCamelCase__							)

    logger.warning(F'Unused weights: {unused_weights}'							)





def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	List[Any]							):
    _UpperCAmelCase							:						Dict       							=					path.split('''.'''							)
    return reduce(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	List[str]							):
    _UpperCAmelCase							:						Tuple       							=					access_by_string(UpperCamelCase__  , UpperCamelCase__							)
    _UpperCAmelCase							:						str       							=					hf_weight.data

    if fsq_value.shape != hf_value.shape:
        raise ValueError(F'{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'							)
    _UpperCAmelCase							:						Tuple       							=					fsq_value
    logger.info(F'{full_name} was correctly initialized from {hf_weight_path}.'							)





def 				lowerCamelCase_		(UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	int							):
    _UpperCAmelCase							:						Tuple       							=					full_name.split('''conv_layers.'''							)[-1]
    _UpperCAmelCase							:						int       							=					name.split('''.'''							)
    _UpperCAmelCase							:						Dict       							=					int(items[0]							)
    _UpperCAmelCase							:						Any       							=					int(items[1]							)

    _UpperCAmelCase							:						Optional[int]       							=					name.split('''.'''							)[-1]
    if type_id == 0:
        _UpperCAmelCase							:						Optional[int]       							=					'''conv'''
    elif type_id == 2:
        _UpperCAmelCase							:						Dict       							=					'''layer_norm'''
    else:
        unused_weights.append(UpperCamelCase__							)
        return

    set_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , F'conv_layers.{layer_id}.{layer_type}.{weight_type}'							)





def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Union[str, Any]							):
    _UpperCAmelCase							:						int       							=					full_name.split('''pos_conv.'''							)[-1]
    _UpperCAmelCase							:						int       							=					name.split('''.'''							)
    _UpperCAmelCase							:						Dict       							=					int(items[0]							)
    _UpperCAmelCase							:						Union[str, Any]       							=					int(items[1]							)

    _UpperCAmelCase							:						List[str]       							=					name.split('''.'''							)[-1]
    if type_id != 0:
        unused_weights.append(UpperCamelCase__							)
        return
    else:
        _UpperCAmelCase							:						Tuple       							=					'''conv'''

    set_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , F'layers.{layer_id}.{layer_type}.{weight_type}'							)





@torch.no_grad()
def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	Union[str, Any]=None  , UpperCamelCase__	:	str=True							):
    if config_path is not None:
        _UpperCAmelCase							:						str       							=					DataaVecAudioConfig.from_pretrained(UpperCamelCase__							)
    else:
        _UpperCAmelCase							:						List[str]       							=					DataaVecAudioConfig()

    if not is_finetuned:
        # Modify final_proj layer name
        _UpperCAmelCase							:						Any       							=					DataaVecAudioModel(UpperCamelCase__							)
        _UpperCAmelCase							:						Optional[Any]       							=					os.path.dirname(UpperCamelCase__							)

        _UpperCAmelCase							:						Optional[int]       							=					torch.load(UpperCamelCase__							)
        _UpperCAmelCase							:						Optional[int]       							=					state_dict['''model'''].pop('''final_proj.0.weight'''							)
        _UpperCAmelCase							:						Union[str, Any]       							=					state_dict['''model'''].pop('''final_proj.0.bias'''							)
        _UpperCAmelCase							:						str       							=					os.path.join(UpperCamelCase__  , '''converted.pt'''							)
        torch.save(UpperCamelCase__  , UpperCamelCase__							)
    else:
        _UpperCAmelCase							:						Any       							=					DataaVecAudioForCTC(UpperCamelCase__							)
        _UpperCAmelCase							:						Any       							=					checkpoint_path

    def load_dataavec(UpperCamelCase__	:	Optional[Any]							):
        _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						List[Any]       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task([path]							)
        return model[0].eval()

    _UpperCAmelCase							:						str       							=					load_dataavec(UpperCamelCase__							)

    recursively_load_weights(UpperCamelCase__  , UpperCamelCase__  , not is_finetuned							)

    _UpperCAmelCase							:						Dict       							=					WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''							)

    _UpperCAmelCase							:						str       							=					load_dataset('''patrickvonplaten/librispeech_asr_dummy'''  , '''clean'''  , split='''validation'''							)
    _UpperCAmelCase							:						Optional[int]       							=					[x['''array'''] for x in ds[:4]['''audio''']]

    _UpperCAmelCase							:						Dict       							=					processor(UpperCamelCase__  , return_tensors='''pt'''  , padding=UpperCamelCase__							)

    _UpperCAmelCase							:						Union[str, Any]       							=					inputs.input_values
    _UpperCAmelCase							:						Any       							=					inputs.attention_mask
    #    input_values = inputs.input_values[:, :-1]
    #    attention_mask = inputs.attention_mask[:, :-1]

    hf_wavavec.eval()
    model.eval()
    if is_finetuned:
        _UpperCAmelCase							:						List[str]       							=					model(source=UpperCamelCase__  , padding_mask=(1 - attention_mask)  , mask=UpperCamelCase__  , features_only=UpperCamelCase__							)[
            '''encoder_out'''
        ].transpose(0  , 1							)
        _UpperCAmelCase							:						List[str]       							=					hf_wavavec(UpperCamelCase__  , attention_mask=UpperCamelCase__							)['''logits''']

        _UpperCAmelCase							:						int       							=					torch.argmax(UpperCamelCase__  , dim=-1							)
        _UpperCAmelCase							:						List[Any]       							=					processor.batch_decode(UpperCamelCase__							)

        print(F'Expected Output: {ds[:4]["text"]}, Pred: {output_string}'							)
    else:
        _UpperCAmelCase							:						str       							=					model(source=UpperCamelCase__  , padding_mask=(1 - attention_mask)  , mask=UpperCamelCase__  , features_only=UpperCamelCase__							)[
            '''layer_results'''
        ][-1][0].transpose(0  , 1							)
        _UpperCAmelCase							:						Optional[Any]       							=					hf_wavavec(UpperCamelCase__  , attention_mask=UpperCamelCase__							)['''last_hidden_state''']

    print(our_output.shape  , their_output.shape							)
    _UpperCAmelCase							:						str       							=					torch.max(torch.abs(our_output - their_output							)							).item()
    print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
    _UpperCAmelCase							:						List[Any]       							=					torch.allclose(UpperCamelCase__  , UpperCamelCase__  , atol=1E-3							)
    print('''Do both models output the same tensors?'''  , '''🔥''' if success else '''💩'''							)
    if not success:
        raise Exception('''Something went wRoNg'''							)

    hf_wavavec.save_pretrained(UpperCamelCase__							)

    if is_finetuned:
        processor.save_pretrained(UpperCamelCase__							)
    else:
        processor.feature_extractor.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase					:Union[str, 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('--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'
  )
  _lowerCAmelCase					:Optional[Any]  =      parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )







"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowerCAmelCase					:Union[str, Any]  =      logging.get_logger(__name__)

_lowerCAmelCase					:List[str]  =      {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}
class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     a__             ='''gpt2'''
     a__             =['''past_key_values''']
     a__             ={
         '''hidden_size''': '''n_embd''',
         '''max_position_embeddings''': '''n_positions''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }





     def __init__(      self ,			A=5_0_2_5_7 ,			A=1_0_2_4 ,			A=7_6_8 ,			A=1_2 ,			A=1_2 ,			A=None ,			A="gelu_new" ,			A=0.1 ,			A=0.1 ,			A=0.1 ,			A=1E-5 ,			A=0.02 ,			A="cls_index" ,			A=True ,			A=None ,			A=True ,			A=0.1 ,			A=True ,			A=True ,			A=5_0_2_5_6 ,			A=5_0_2_5_6 ,			A=False ,			A=False ,			**A ,			)							->      Union[str, Any]:
         _UpperCAmelCase							:						Optional[int]       							=					vocab_size
         _UpperCAmelCase							:						Union[str, Any]       							=					n_positions
         _UpperCAmelCase							:						Optional[Any]       							=					n_embd
         _UpperCAmelCase							:						Tuple       							=					n_layer
         _UpperCAmelCase							:						Any       							=					n_head
         _UpperCAmelCase							:						Union[str, Any]       							=					n_inner
         _UpperCAmelCase							:						Optional[int]       							=					activation_function
         _UpperCAmelCase							:						Optional[int]       							=					resid_pdrop
         _UpperCAmelCase							:						Dict       							=					embd_pdrop
         _UpperCAmelCase							:						List[Any]       							=					attn_pdrop
         _UpperCAmelCase							:						Tuple       							=					layer_norm_epsilon
         _UpperCAmelCase							:						Optional[int]       							=					initializer_range
         _UpperCAmelCase							:						List[str]       							=					summary_type
         _UpperCAmelCase							:						Tuple       							=					summary_use_proj
         _UpperCAmelCase							:						List[Any]       							=					summary_activation
         _UpperCAmelCase							:						Optional[int]       							=					summary_first_dropout
         _UpperCAmelCase							:						int       							=					summary_proj_to_labels
         _UpperCAmelCase							:						Union[str, Any]       							=					scale_attn_weights
         _UpperCAmelCase							:						Any       							=					use_cache
         _UpperCAmelCase							:						Tuple       							=					scale_attn_by_inverse_layer_idx
         _UpperCAmelCase							:						Optional[Any]       							=					reorder_and_upcast_attn

         _UpperCAmelCase							:						Union[str, Any]       							=					bos_token_id
         _UpperCAmelCase							:						Optional[int]       							=					eos_token_id

         super().__init__(bos_token_id=A ,			eos_token_id=A ,			**A	)



class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     def __init__(      self ,			A ,			A = "default" ,			A = None ,			A = False ,			)							->      str:
         super().__init__(A ,			task=A ,			patching_specs=A ,			use_past=A	)
         if not getattr(self._config ,			'''pad_token_id''' ,			A	):
             # TODO: how to do that better?
             _UpperCAmelCase							:						Optional[int]       							=					0





     @property
     def 				__lowerCAmelCase			(      self	)							->      Mapping[str, Mapping[int, str]]:
         _UpperCAmelCase							:						List[str]       							=					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
         if self.use_past:
             self.fill_with_past_key_values_(A ,			direction='''inputs'''	)
             _UpperCAmelCase							:						Union[str, Any]       							=					{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             _UpperCAmelCase							:						Tuple       							=					{0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def 				__lowerCAmelCase			(      self	)							->      int:
         return self._config.n_layer





     @property
     def 				__lowerCAmelCase			(      self	)							->      int:
         return self._config.n_head





     def 				__lowerCAmelCase			(      self ,			A ,			A = -1 ,			A = -1 ,			A = False ,			A = None ,			)							->      Mapping[str, Any]:
         _UpperCAmelCase							:						int       							=					super(A ,			self	).generate_dummy_inputs(
             A ,			batch_size=A ,			seq_length=A ,			is_pair=A ,			framework=A	)

         # We need to order the input in the way they appears in the forward()
         _UpperCAmelCase							:						List[Any]       							=					OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

         # Need to add the past_keys
         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
             else:
                 import torch

                 _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[Any]       							=					common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 _UpperCAmelCase							:						Optional[Any]       							=					seqlen + 2
                 _UpperCAmelCase							:						Union[str, Any]       							=					(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 _UpperCAmelCase							:						List[Any]       							=					[
                     (torch.zeros(A	), torch.zeros(A	)) for _ in range(self.num_layers	)
                 ]

         _UpperCAmelCase							:						int       							=					common_inputs['''attention_mask''']
         if self.use_past:
             _UpperCAmelCase							:						int       							=					ordered_inputs['''attention_mask'''].dtype
             _UpperCAmelCase							:						Dict       							=					torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(A ,			A ,			dtype=A	)] ,			dim=1	)

         return ordered_inputs





     @property
     def 				__lowerCAmelCase			(      self	)							->      int:
         return 1_3


