from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= LEDConfig
 UpperCAmelCase_     			= {}
 UpperCAmelCase_     			= 'gelu'




 def __init__(      self :  List[str]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Optional[Any]=13      ,   lowercase__ :  List[Any]=7      ,   lowercase__ :  List[str]=True      ,   lowercase__ :  List[Any]=False      ,   lowercase__ :  Union[str, Any]=99      ,   lowercase__ :  Tuple=32      ,   lowercase__ :  Optional[Any]=2      ,   lowercase__ :  Optional[int]=4      ,   lowercase__ :  Tuple=37      ,   lowercase__ :  Optional[int]=0.1      ,   lowercase__ :  List[str]=0.1      ,   lowercase__ :  int=20      ,   lowercase__ :  Any=2      ,   lowercase__ :  Optional[int]=1      ,   lowercase__ :  int=0      ,   lowercase__ :  int=4      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = seq_length
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = intermediate_size

        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = eos_token_id
        lowerCAmelCase__					     = pad_token_id
        lowerCAmelCase__					     = bos_token_id
        lowerCAmelCase__					     = attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        lowerCAmelCase__					     = self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        lowerCAmelCase__					     = (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length - 1]      ,   self.vocab_size)
        lowerCAmelCase__					     = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)      ,   1)
        lowerCAmelCase__					     = tf.concat([input_ids, eos_tensor]      ,   axis=1)

        lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

        lowerCAmelCase__					     = self.config_cls(
            vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   encoder_layers=self.num_hidden_layers      ,   decoder_layers=self.num_hidden_layers      ,   encoder_attention_heads=self.num_attention_heads      ,   decoder_attention_heads=self.num_attention_heads      ,   encoder_ffn_dim=self.intermediate_size      ,   decoder_ffn_dim=self.intermediate_size      ,   dropout=self.hidden_dropout_prob      ,   attention_dropout=self.attention_probs_dropout_prob      ,   max_position_embeddings=self.max_position_embeddings      ,   eos_token_ids=[2]      ,   bos_token_id=self.bos_token_id      ,   pad_token_id=self.pad_token_id      ,   decoder_start_token_id=self.pad_token_id      ,   attention_window=self.attention_window      ,   **self.config_updates      ,   )
        lowerCAmelCase__					     = prepare_led_inputs_dict(lowercase__      ,   lowercase__      ,   lowercase__)
        lowerCAmelCase__					     = tf.concat(
            [tf.zeros_like(lowercase__)[:, :-1], tf.ones_like(lowercase__)[:, -1:]]      ,   axis=-1      ,   )
        lowerCAmelCase__					     = global_attention_mask
        return config, inputs_dict




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  str      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDModel(config=lowercase__).get_decoder()
        lowerCAmelCase__					     = inputs_dict['input_ids']

        lowerCAmelCase__					     = input_ids[:1, :]
        lowerCAmelCase__					     = inputs_dict['attention_mask'][:1, :]
        lowerCAmelCase__					     = 1

        # first forward pass
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   use_cache=lowercase__)

        lowerCAmelCase__     , lowerCAmelCase__					     = outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        lowerCAmelCase__					     = ids_tensor((self.batch_size, 3)      ,   config.vocab_size)
        lowerCAmelCase__					     = tf.cast(ids_tensor((self.batch_size, 3)      ,   2)      ,   tf.inta)

        # append to next input_ids and
        lowerCAmelCase__					     = tf.concat([input_ids, next_tokens]      ,   axis=-1)
        lowerCAmelCase__					     = tf.concat([attention_mask, next_attn_mask]      ,   axis=-1)

        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__)[0]
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   past_key_values=lowercase__)[0]

        self.parent.assertEqual(next_tokens.shape[1]      ,   output_from_past.shape[1])

        # select random slice
        lowerCAmelCase__					     = int(ids_tensor((1,)      ,   output_from_past.shape[-1]))
        lowerCAmelCase__					     = output_from_no_past[:, -3:, random_slice_idx]
        lowerCAmelCase__					     = output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowercase__      ,   lowercase__      ,   rtol=1e-3)






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  ):
       if attention_mask is None:
              lowerCAmelCase__					     = tf.cast(tf.math.not_equal(lowerCAmelCase__     ,  config.pad_token_id     )     ,  tf.inta     )
       if decoder_attention_mask is None:
              lowerCAmelCase__					     = tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape     ,  dtype=tf.inta     ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,  config.pad_token_id     )     ,  tf.inta     ),
                  ]     ,  axis=-1     ,  )
       if head_mask is None:
              lowerCAmelCase__					     = tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              lowerCAmelCase__					     = tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
           "decoder_input_ids": decoder_input_ids,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
       }
@require_tf
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 UpperCAmelCase_     			= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
 UpperCAmelCase_     			= (
     {
         'conversational': TFLEDForConditionalGeneration,
         'feature-extraction': TFLEDModel,
         'summarization': TFLEDForConditionalGeneration,
         'text2text-generation': TFLEDForConditionalGeneration,
         'translation': TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDModelTester(self)
        lowerCAmelCase__					     = ConfigTester(self      ,   config_class=lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase__)




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__					     = tf.zeros_like(inputs_dict['attention_mask'])
        lowerCAmelCase__					     = 2
        lowerCAmelCase__					     = tf.where(
            tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices      ,   1      ,   inputs_dict['global_attention_mask']      ,   )

        lowerCAmelCase__					     = True
        lowerCAmelCase__					     = self.model_tester.seq_length
        lowerCAmelCase__					     = self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(lowercase__ :  Union[str, Any]):
               lowerCAmelCase__					     = outputs.decoder_attentions
               self.assertEqual(len(lowercase__)      ,   self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(decoder_attentions[0].shape[-3:])      ,   [self.model_tester.num_attention_heads, seq_length, seq_length]      ,   )

        def check_encoder_attentions_output(lowercase__ :  Optional[int]):
               lowerCAmelCase__					     = [t.numpy() for t in outputs.encoder_attentions]
               lowerCAmelCase__					     = [t.numpy() for t in outputs.encoder_global_attentions]
               self.assertEqual(len(lowercase__)      ,   self.model_tester.num_hidden_layers)
               self.assertEqual(len(lowercase__)      ,   self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(attentions[0].shape[-3:])      ,   [self.model_tester.num_attention_heads, seq_length, seq_length]      ,   )
               self.assertListEqual(
                   list(global_attentions[0].shape[-3:])      ,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]      ,   )

        for model_class in self.all_model_classes:
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = False
               lowerCAmelCase__					     = False
               lowerCAmelCase__					     = model_class(lowercase__)
               lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))
               lowerCAmelCase__					     = len(lowercase__)
               self.assertEqual(config.output_hidden_states      ,   lowercase__)
               check_encoder_attentions_output(lowercase__)

               if self.is_encoder_decoder:
                      lowerCAmelCase__					     = model_class(lowercase__)
                      lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))
                      self.assertEqual(config.output_hidden_states      ,   lowercase__)
                      check_decoder_attentions_output(lowercase__)

               # Check that output attentions can also be changed via the config
               del inputs_dict["output_attentions"]
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = model_class(lowercase__)
               lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))
               self.assertEqual(config.output_hidden_states      ,   lowercase__)
               check_encoder_attentions_output(lowercase__)

               # Check attention is always last and order is fine
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = model_class(lowercase__)
               lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))

               self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)      ,   len(lowercase__))
               self.assertEqual(model.config.output_hidden_states      ,   lowercase__)
               check_encoder_attentions_output(lowercase__)




 @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.')
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        pass






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return tf.constant(lowerCAmelCase__     ,  dtype=tf.intaa     )


lowerCAmelCase__     						=			1e-4



@slow
@require_tf
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384').led

        # change to intended input here
        lowerCAmelCase__					     = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = prepare_led_inputs_dict(model.config      ,   lowercase__      ,   lowercase__)
        lowerCAmelCase__					     = model(**lowercase__)[0]
        lowerCAmelCase__					     = (1, 1_024, 768)
        self.assertEqual(output.shape      ,   lowercase__)
        # change to expected output here
        lowerCAmelCase__					     = tf.convert_to_tensor(
            [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]      ,   )
        tf.debugging.assert_near(output[:, :3, :3]      ,   lowercase__      ,   atol=1e-3)




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')

        # change to intended input here
        lowerCAmelCase__					     = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = prepare_led_inputs_dict(model.config      ,   lowercase__      ,   lowercase__)
        lowerCAmelCase__					     = model(**lowercase__)[0]
        lowerCAmelCase__					     = (1, 1_024, model.config.vocab_size)
        self.assertEqual(output.shape      ,   lowercase__)
        # change to expected output here
        lowerCAmelCase__					     = tf.convert_to_tensor(
            [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]      ,   )
        tf.debugging.assert_near(output[:, :3, :3]      ,   lowercase__      ,   atol=1e-3      ,   rtol=1e-3)

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

lowerCAmelCase__     						=			{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase__     						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = {}
       with open(lowerCAmelCase__     ,  'r'     ) as file:
              for line_number, line in enumerate(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = line.strip()
                     if line:
                            lowerCAmelCase__					     = line.split()
                            lowerCAmelCase__					     = line_number
                            lowerCAmelCase__					     = words[0]
                            lowerCAmelCase__					     = value
       return result






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for attribute in key.split('.'     ):
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )

       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     ).shape
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = hf_pointer
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = shape_pointer.shape

              # let's reduce dimension
              lowerCAmelCase__					     = value[0]
       else:
              lowerCAmelCase__					     = 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":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_g":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_v":
              lowerCAmelCase__					     = value
       elif weight_type == "bias":
              lowerCAmelCase__					     = value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = value
       else:
              lowerCAmelCase__					     = value

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = '.'.join([key, weight_type]     )
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = '.'.join([key, hf_param_name]     )
       else:
              lowerCAmelCase__					     = key

       lowerCAmelCase__					     = value if 'lm_head' in full_key else value[0]


lowerCAmelCase__     						=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ):
       lowerCAmelCase__					     = False
       for key, mapped_key in MAPPING.items():
              lowerCAmelCase__					     = 'wav2vec2.' + 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]:
                     lowerCAmelCase__					     = True
                     if "*" in mapped_key:
                            lowerCAmelCase__					     = name.split(lowerCAmelCase__     )[0].split('.'     )[-2]
                            lowerCAmelCase__					     = mapped_key.replace('*'     ,  lowerCAmelCase__     )
                     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:
                            # TODO: don't match quantizer.weight_proj
                            lowerCAmelCase__					     = 'weight'
                     else:
                            lowerCAmelCase__					     = None
                     if hf_dict is not None:
                            rename_dict(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     else:
                            set_recursively(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     return is_used
       return is_used






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = []
       lowerCAmelCase__					     = fairseq_model.state_dict()

       lowerCAmelCase__					     = hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCAmelCase__					     = False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
                     lowerCAmelCase__					     = True
              else:
                     lowerCAmelCase__					     = load_wavaveca_layer(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
              if not is_used:
                     unused_weights.append(lowerCAmelCase__     )

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       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:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                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:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[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(lowerCAmelCase__     )






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=True     ,  lowerCAmelCase__=False     ):
       if config_path is not None:
              lowerCAmelCase__					     = WavaVecaConfig.from_pretrained(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaConfig()

       if is_seq_class:
              lowerCAmelCase__					     = read_txt_into_dict(lowerCAmelCase__     )
              lowerCAmelCase__					     = idalabel
              lowerCAmelCase__					     = WavaVecaForSequenceClassification(lowerCAmelCase__     )
              lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                  feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
              feature_extractor.save_pretrained(lowerCAmelCase__     )

       elif is_finetuned:
              if dict_path:
                     lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCAmelCase__					     = target_dict.pad_index
                     lowerCAmelCase__					     = target_dict.bos_index
                     lowerCAmelCase__					     = target_dict.eos_index
                     lowerCAmelCase__					     = len(target_dict.symbols     )
                     lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  'vocab.json'     )
                     if not os.path.isdir(lowerCAmelCase__     ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase__     )     )
                            return
                     os.makedirs(lowerCAmelCase__     ,  exist_ok=lowerCAmelCase__     )
                     lowerCAmelCase__					     = target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     lowerCAmelCase__					     = 0
                     lowerCAmelCase__					     = 1
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as vocab_handle:
                            json.dump(lowerCAmelCase__     ,  lowerCAmelCase__     )
                     lowerCAmelCase__					     = WavaVecaCTCTokenizer(
                         lowerCAmelCase__     ,  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=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = True if config.feat_extract_norm == 'layer' else False
                     lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                         feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = WavaVecaProcessor(feature_extractor=lowerCAmelCase__     ,  tokenizer=lowerCAmelCase__     )
                     processor.save_pretrained(lowerCAmelCase__     )

              lowerCAmelCase__					     = WavaVecaForCTC(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaForPreTraining(lowerCAmelCase__     )

       if is_finetuned or is_seq_class:
              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]     ,  arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
       else:
              lowerCAmelCase__					     = argparse.Namespace(task='audio_pretraining'     )
              lowerCAmelCase__					     = fairseq.tasks.setup_task(lowerCAmelCase__     )

              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,  task=lowerCAmelCase__     )

       lowerCAmelCase__					     = model[0].eval()

       recursively_load_weights(lowerCAmelCase__     ,  lowerCAmelCase__     ,  not is_finetuned     )

       hf_wavavec.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			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'
    )
    parser.add_argument(
        '--is_seq_class',
        action='store_true',
        help='Whether the model to convert is a fine-tuned sequence classification model or not',
    )
    lowerCAmelCase__     						=			parser.parse_args()

    lowerCAmelCase__     						=			not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )
