





"""simple docstring"""




import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
       SpeechaTextaDecoder,
       SpeechaTextaForCausalLM,
   )

@require_torch
class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=9_9   , snake_case_=1_3   , snake_case_=1_6   , snake_case_=7   , snake_case_=True   , snake_case_=True   , snake_case_=True   , snake_case_=False   , snake_case_=True   , snake_case_=2   , snake_case_=3_2   , snake_case_=4   , snake_case_=4   , snake_case_=3_0   , snake_case_=0   , snake_case_=1   , snake_case_=2   , snake_case_=None   , ):

        """simple docstring"""

        A_    :			Optional[Any]									=    parent
        A_    :			List[Any]									=    batch_size
        A_    :			Tuple									=    decoder_seq_length
        # For common tests
        A_    :			List[str]									=    self.decoder_seq_length
        A_    :			List[str]									=    is_training
        A_    :			Optional[Any]									=    use_attention_mask
        A_    :			List[Any]									=    use_labels

        A_    :			int									=    vocab_size
        A_    :			int									=    d_model
        A_    :			Dict									=    d_model
        A_    :			Tuple									=    decoder_layers
        A_    :			Union[str, Any]									=    decoder_layers
        A_    :			Any									=    decoder_ffn_dim
        A_    :			List[Any]									=    decoder_attention_heads
        A_    :			Any									=    decoder_attention_heads
        A_    :			Optional[int]									=    eos_token_id
        A_    :			List[str]									=    bos_token_id
        A_    :			Optional[int]									=    pad_token_id
        A_    :			Tuple									=    decoder_start_token_id
        A_    :			Dict									=    use_cache
        A_    :			Any									=    max_position_embeddings

        A_    :			List[Any]									=    None
        A_    :			List[Any]									=    decoder_seq_length
        A_    :			Optional[int]									=    2
        A_    :			int									=    1

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    ids_tensor([self.batch_size, self.decoder_seq_length]   , self.vocab_size    )

        A_    :			Optional[int]									=    None
        if self.use_attention_mask:
          A_    :			List[Any]									=    ids_tensor([self.batch_size, self.decoder_seq_length]   , vocab_size=2    )

        A_    :			Union[str, Any]									=    None
        if self.use_labels:
          A_    :			Dict									=    ids_tensor([self.batch_size, self.decoder_seq_length]   , self.vocab_size    )

        A_    :			Optional[Any]									=    SpeechaTextaConfig(
            vocab_size=self.vocab_size   , d_model=self.d_model   , decoder_layers=self.decoder_layers   , decoder_ffn_dim=self.decoder_ffn_dim   , decoder_attention_heads=self.decoder_attention_heads   , eos_token_id=self.eos_token_id   , bos_token_id=self.bos_token_id   , use_cache=self.use_cache   , pad_token_id=self.pad_token_id   , decoder_start_token_id=self.decoder_start_token_id   , max_position_embeddings=self.max_position_embeddings   , )

        return (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , ):

        """simple docstring"""

        A_    :			Dict									=    True
        A_    :			int									=    SpeechaTextaDecoder(config=snake_case_    ).to(snake_case_    ).eval()
        A_    :			Any									=    input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        A_    :			Optional[Any]									=    model(snake_case_   , use_cache=snake_case_    )
        A_    :			Any									=    model(snake_case_    )
        A_    :			List[str]									=    model(snake_case_   , use_cache=snake_case_    )

        self.parent.assertTrue(len(snake_case_    ) == len(snake_case_    )    )
        self.parent.assertTrue(len(snake_case_    ) == len(snake_case_    ) + 1    )

        A_    :			str									=    outputs['past_key_values']

        # create hypothetical next token and extent to next_input_ids
        A_    :			List[Any]									=    ids_tensor((2, 1)   , config.vocab_size - 1    ) + 1

        # append to next input_ids and
        A_    :			str									=    torch.cat([input_ids, next_tokens]   , dim=-1    )

        print(snake_case_    )

        A_    :			List[str]									=    model(snake_case_    )['last_hidden_state']
        A_    :			List[str]									=    model(snake_case_   , past_key_values=snake_case_    )['last_hidden_state']

        # select random slice
        A_    :			Optional[int]									=    ids_tensor((1,)   , output_from_past.shape[-1]    ).item()
        A_    :			Union[str, Any]									=    output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        A_    :			List[str]									=    output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(snake_case_   , snake_case_   , atol=1E-3    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    self.prepare_config_and_inputs()
        (
            (
            A_
        )		,			(
            A_
        )		,			(
            A_
        )		,			(
            A_
        )		,			
        )    :			Dict									=    config_and_inputs

        A_    :			Dict									=    {
            'input_ids': input_ids,
            'attention_mask': attention_mask,
        }
        return config, inputs_dict


@require_torch
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Union[str, Any]             =  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
      lowercase_ :					List[Any]             =  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
      lowercase_ :					Union[str, Any]             =  {"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
      lowercase_ :					int             =  True
      lowercase_ :					List[str]             =  False

      def    lowerCamelCase_     (	self   , ):

        """simple docstring"""

        A_    :			List[str]									=    SpeechaTextaStandaloneDecoderModelTester(self   , is_training=snake_case_    )
        A_    :			Dict									=    ConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return

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()
_A							=       logging.get_logger(__name__)

_A							=       {
    '''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''',
}
_A							=       [
    '''lm_head''',
]




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Optional[int]						,   a__	:	Optional[Any]						,   a__	:	Tuple  )		->						str:
       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

       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_											=							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 					lowerCamelCase__					(    a__	:	List[str]						,   a__	:	int						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							[]
       UpperCamelCase_											=							fairseq_model.state_dict()

       if not is_headless:
              UpperCamelCase_											=							hf_model.dataavec_audio.feature_extractor
              UpperCamelCase_											=							hf_model.dataavec_audio.encoder.pos_conv_embed

       else:
              UpperCamelCase_											=							hf_model.feature_extractor
              UpperCamelCase_											=							hf_model.encoder.pos_conv_embed

       for name, value in fairseq_dict.items():
              UpperCamelCase_											=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   UpperCamelCase_											=							"""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_											=							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 "bias" in name:
                                          UpperCamelCase_											=							"""bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCamelCase_											=							"""weight"""
                                   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 					lowerCamelCase__					(    a__	:	Optional[Any]						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							path.split("""."""  )
       return reduce(a__						,   a__						,   a__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]						,   a__	:	int						,   a__	:	Optional[int]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							access_by_string(a__						,   a__  )
       UpperCamelCase_											=							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_											=							fsq_value
       logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	str						,   a__	:	str  )		->						int:
       UpperCamelCase_											=							full_name.split("""conv_layers."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id == 0:
              UpperCamelCase_											=							"""conv"""
       elif type_id == 2:
              UpperCamelCase_											=							"""layer_norm"""
       else:
              unused_weights.append(a__  )
              return

       set_weights(a__						,   a__						,   a__						,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Tuple						,   a__	:	Dict  )		->						Any:
       UpperCamelCase_											=							full_name.split("""pos_conv."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id != 0:
              unused_weights.append(a__  )
              return
       else:
              UpperCamelCase_											=							"""conv"""

       set_weights(a__						,   a__						,   a__						,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''  )




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	List[Any]						,   a__	:	Optional[int]=None						,   a__	:	List[str]=None						,   a__	:	Optional[int]=True  )		->						str:
       if config_path is not None:
              UpperCamelCase_											=							DataaVecAudioConfig.from_pretrained(a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioConfig()

       if not is_finetuned:
              # Modify final_proj layer name
              UpperCamelCase_											=							DataaVecAudioModel(a__  )
              UpperCamelCase_											=							os.path.dirname(a__  )

              UpperCamelCase_											=							torch.load(a__  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.weight"""  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.bias"""  )
              UpperCamelCase_											=							os.path.join(a__						,   """converted.pt"""  )
              torch.save(a__						,   a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioForCTC(a__  )
              UpperCamelCase_											=							checkpoint_path

       def load_dataavec(a__	:	Optional[int]  ):
              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       UpperCamelCase_											=							load_dataavec(a__  )

       recursively_load_weights(a__						,   a__						,   not is_finetuned  )

       UpperCamelCase_											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

       UpperCamelCase_											=							load_dataset("""patrickvonplaten/librispeech_asr_dummy"""						,   """clean"""						,   split="""validation"""  )
       UpperCamelCase_											=							[x["""array"""] for x in ds[:4]["""audio"""]]

       UpperCamelCase_											=							processor(a__						,   return_tensors="""pt"""						,   padding=a__  )

       UpperCamelCase_											=							inputs.input_values
       UpperCamelCase_											=							inputs.attention_mask
       #    input_values = inputs.input_values[:, :-1]
       #    attention_mask = inputs.attention_mask[:, :-1]

       hf_wavavec.eval()
       model.eval()
       if is_finetuned:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """encoder_out"""
              ].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""logits"""]

              UpperCamelCase_											=							torch.argmax(a__						,   dim=-1  )
              UpperCamelCase_											=							processor.batch_decode(a__  )

              print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
       else:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """layer_results"""
              ][-1][0].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""last_hidden_state"""]

       print(our_output.shape						,   their_output.shape  )
       UpperCamelCase_											=							torch.max(torch.abs(our_output - their_output  )  ).item()
       print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       UpperCamelCase_											=							torch.allclose(a__						,   a__						,   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(a__  )

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


if __name__ == "__main__":
      _A							=       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'''
      )
      _A							=       parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



