



'''simple docstring'''





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, 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 (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLModel,
    )



class  snake_case__						:
 def __init__(   self						:							List[str]      ,     __a						:							Dict      ,     )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Any													=    parent
       __snake_case :   Optional[int]													=    13
       __snake_case :   Union[str, Any]													=    7
       __snake_case :   str													=    30
       __snake_case :   Optional[int]													=    self.seq_length + self.mem_len
       __snake_case :   Dict													=    15
       __snake_case :   List[str]													=    True
       __snake_case :   Optional[int]													=    True
       __snake_case :   Tuple													=    99
       __snake_case :   str													=    [10, 50, 80]
       __snake_case :   List[Any]													=    32
       __snake_case :   Optional[int]													=    32
       __snake_case :   int													=    4
       __snake_case :   Any													=    8
       __snake_case :   List[Any]													=    128
       __snake_case :   List[str]													=    2
       __snake_case :   Tuple													=    2
       __snake_case :   int													=    None
       __snake_case :   Optional[int]													=    1
       __snake_case :   int													=    0
       __snake_case :   List[str]													=    3
       __snake_case :   str													=    self.vocab_size - 1
       __snake_case :   Tuple													=    0.0_1
 def A_   (   self						:							List[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   str													=    ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size  )
       __snake_case :   Tuple													=    ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size  )

       __snake_case :   str													=    None
       if self.use_labels:
             __snake_case :   Tuple													=    ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size  )

       __snake_case :   Union[str, Any]													=    TransfoXLConfig(
           vocab_size=self.vocab_size      ,     mem_len=self.mem_len      ,     clamp_len=self.clamp_len      ,     cutoffs=self.cutoffs      ,     d_model=self.hidden_size      ,     d_embed=self.d_embed      ,     n_head=self.num_attention_heads      ,     d_head=self.d_head      ,     d_inner=self.d_inner      ,     div_val=self.div_val      ,     n_layer=self.num_hidden_layers      ,     eos_token_id=self.eos_token_id      ,     pad_token_id=self.vocab_size - 1      ,     init_range=self.init_range      ,     num_labels=self.num_labels      ,     )

       return (config, input_ids_a, input_ids_a, lm_labels)
 def A_   (   self						:							Union[str, Any]  )      ->   Any:







       '''simple docstring'''
       random.seed(self.seed  )
       tf.random.set_seed(self.seed  )
 def A_   (   self						:							Union[str, Any]      ,     __a						:							Optional[Any]      ,     __a						:							Dict      ,     __a						:							List[str]      ,     __a						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Tuple													=    TFTransfoXLModel(UpperCAmelCase__  )

       __snake_case :   Optional[Any]													=    model(UpperCAmelCase__  ).to_tuple()

       __snake_case :   List[str]													=    {'''input_ids''': input_ids_a, '''mems''': mems_a}

       __snake_case :   Any													=    model(UpperCAmelCase__  ).to_tuple()

       self.parent.assertEqual(hidden_states_a.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)  )
       self.parent.assertEqual(hidden_states_a.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)  )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]      ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,     )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]      ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,     )
 def A_   (   self						:							int      ,     __a						:							List[str]      ,     __a						:							str      ,     __a						:							Dict      ,     __a						:							int  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   int													=    TFTransfoXLLMHeadModel(UpperCAmelCase__  )

       __snake_case :   Tuple													=    model(UpperCAmelCase__  ).to_tuple()

       __snake_case :   Optional[Any]													=    {'''input_ids''': input_ids_a, '''labels''': lm_labels}
       __snake_case :   Optional[int]													=    model(UpperCAmelCase__  ).to_tuple()

       __snake_case :   List[str]													=    model([input_ids_a, mems_a]  ).to_tuple()

       __snake_case :   int													=    {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

       __snake_case :   str													=    model(UpperCAmelCase__  ).to_tuple()

       self.parent.assertEqual(lm_logits_a.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)  )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]      ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,     )

       self.parent.assertEqual(lm_logits_a.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)  )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]      ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,     )
 def A_   (   self						:							str      ,     __a						:							int      ,     __a						:							Any      ,     __a						:							List[str]      ,     __a						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    TFTransfoXLForSequenceClassification(UpperCAmelCase__  )
       __snake_case :   Union[str, Any]													=    model(UpperCAmelCase__  )
       self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)  )





 def A_   (   self						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    self.prepare_config_and_inputs()
       (__snake_case) :   Optional[Any]													=    config_and_inputs
       __snake_case :   Union[str, Any]													=    {'''input_ids''': input_ids_a}
       return config, inputs_dict



@require_tf
class  snake_case__						(    lowercase__  ,     lowercase__  ,     unittest.TestCase				):
 A__				= (
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 A__				= () if is_tf_available() else ()
 A__				= (
     {
         '''feature-extraction''': TFTransfoXLModel,
         '''text-classification''': TFTransfoXLForSequenceClassification,
         '''text-generation''': TFTransfoXLLMHeadModel,
         '''zero-shot''': TFTransfoXLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
 A__				= False
 A__				= False
 A__				= False
 A__				= False
 def A_   (   self						:							Dict      ,     __a						:							List[Any]      ,     __a						:							Optional[int]      ,     __a						:							int      ,     __a						:							Optional[int]      ,     __a						:							int  )      ->   Any:







       '''simple docstring'''
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

       return False
 def A_   (   self						:							int  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Any													=    TFTransfoXLModelTester(self  )
       __snake_case :   Optional[int]													=    ConfigTester(self      ,     config_class=UpperCAmelCase__      ,     d_embed=37  )
 def A_   (   self						:							Any  )      ->   Optional[int]:







       '''simple docstring'''
       self.config_tester.run_common_tests()
 def A_   (   self						:							Optional[int]  )      ->   Optional[int]:







       '''simple docstring'''
       self.model_tester.set_seed()
       __snake_case :   List[str]													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__  )
 def A_   (   self						:							List[str]  )      ->   Optional[int]:







       '''simple docstring'''
       self.model_tester.set_seed()
       __snake_case :   Any													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__  )
 def A_   (   self						:							List[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Dict													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__  )
 def A_   (   self						:							Optional[Any]  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.model_tester.prepare_config_and_inputs_for_common()
       __snake_case :   int													=    [TFTransfoXLForSequenceClassification]

       for model_class in self.all_model_classes:
             __snake_case :   str													=    model_class(UpperCAmelCase__  )
             assert isinstance(model.get_input_embeddings()      ,     tf.keras.layers.Layer  )
             if model_class in list_other_models_with_output_ebd:
                   __snake_case :   Union[str, Any]													=    model.get_output_embeddings()
                   assert isinstance(UpperCAmelCase__      ,     tf.keras.layers.Layer  )
                   __snake_case :   Any													=    model.get_bias()
                   assert name is None
             else:
                   __snake_case :   Optional[int]													=    model.get_output_embeddings()
                   assert x is None
                   __snake_case :   Optional[int]													=    model.get_bias()
                   assert name is None
 def A_   (   self						:							Any  )      ->   Optional[Any]:







       '''simple docstring'''
       pass
 @slow
 def A_   (   self						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __snake_case :   int													=    TFTransfoXLModel.from_pretrained(UpperCAmelCase__  )
             self.assertIsNotNone(UpperCAmelCase__  )





 @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'  )
 def A_   (   self						:							int  )      ->   Optional[Any]:







       '''simple docstring'''
       pass



@require_tf
class  snake_case__						(    unittest.TestCase				):
 @unittest.skip('Skip test until #12651 is resolved.'  )
 @slow
 def A_   (   self						:							Tuple  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'  )
       # fmt: off
       __snake_case :   Tuple													=    tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]      ,     dtype=tf.intaa  )  # noqa: E231
       # fmt: on
       #  In 1991 , the remains of Russian Tsar Nicholas II and his family
       #  ( except for Alexei and Maria ) are discovered .
       #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
       #  remainder of the story . 1883 Western Siberia ,
       #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
       #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
       #  father initially slaps him for making such an accusation , Rasputin watches as the
       #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
       #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
       #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

       # fmt: off
       __snake_case :   Optional[int]													=    [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # noqa: E231
       # fmt: on
       #  In 1991, the remains of Russian Tsar Nicholas II and his family (
       #  except for Alexei and Maria ) are discovered. The voice of young son,
       #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
       #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
       #  and a group of men to perform magic. Rasputin has a vision and
       #  denounces one of the men as a horse thief. Although his father initially
       #  slaps him for making such an accusation, Rasputin watches as the man
       #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
       #  of the Virgin Mary, prompting him to become a priest.
       #  Rasputin quickly becomes famous, with people, even a bishop, begging for
       #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
       # Nicholas II and his family were discovered. The voice of <unk> young son,
       # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

       __snake_case :   int													=    model.generate(UpperCAmelCase__      ,     max_length=200      ,     do_sample=UpperCAmelCase__  )
       self.assertListEqual(output_ids[0].numpy().tolist()      ,     UpperCAmelCase__  )






'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase_    						=       {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase_    						=       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Tuple						)				->  str:
						for attribute in key.split('''.'''						):
												lowercase :							Tuple    		=getattr(__magic_name__   , __magic_name__						)

						if weight_type is not None:
												lowercase :							Optional[int]    		=getattr(__magic_name__   , __magic_name__						).shape
						else:
												lowercase :							List[Any]    		=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":
												lowercase :							Any    		=value
						elif weight_type == "weight_g":
												lowercase :							List[Any]    		=value
						elif weight_type == "weight_v":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "bias":
												lowercase :							Tuple    		=value
						elif weight_type == "running_mean":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "running_var":
												lowercase :							str    		=value
						elif weight_type == "num_batches_tracked":
												lowercase :							Tuple    		=value
						elif weight_type == "inv_freq":
												lowercase :							Optional[Any]    		=value
						else:
												lowercase :							Tuple    		=value

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






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						lowercase :							Optional[int]    		=[]
						lowercase :							Tuple    		=fairseq_model.state_dict()

						lowercase :							List[Any]    		=hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase :							Tuple    		=False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , hf_model.config.feat_extract_norm == '''group'''   , )
																		lowercase :							List[Any]    		=True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase :							Optional[int]    		='''wav2vec2_conformer.''' + 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]:
																														lowercase :							Union[str, Any]    		=True
																														if "*" in mapped_key:
																																				lowercase :							Optional[int]    		=name.split(__magic_name__						)[0].split('''.'''						)[-2]
																																				lowercase :							List[str]    		=mapped_key.replace('''*'''   , __magic_name__						)
																														if "pos_bias_u" in name:
																																				lowercase :							Optional[Any]    		=None
																														elif "pos_bias_v" in name:
																																				lowercase :							Union[str, Any]    		=None
																														elif "weight_g" in name:
																																				lowercase :							Any    		='''weight_g'''
																														elif "weight_v" in name:
																																				lowercase :							Tuple    		='''weight_v'''
																														elif "bias" in name:
																																				lowercase :							Optional[int]    		='''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowercase :							Optional[int]    		='''weight'''
																														elif "running_mean" in name:
																																				lowercase :							Union[str, Any]    		='''running_mean'''
																														elif "inv_freq" in name:
																																				lowercase :							Any    		='''inv_freq'''
																														elif "running_var" in name:
																																				lowercase :							Tuple    		='''running_var'''
																														elif "num_batches_tracked" in name:
																																				lowercase :							Dict    		='''num_batches_tracked'''
																														else:
																																				lowercase :							str    		=None
																														set_recursively(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)
																								continue
												if not is_used:
																		unused_weights.append(__magic_name__						)

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






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple						)				->  int:
						lowercase :							Optional[Any]    		=full_name.split('''conv_layers.'''						)[-1]
						lowercase :							Any    		=name.split('''.'''						)
						lowercase :							List[str]    		=int(items[0]						)
						lowercase :							Union[str, Any]    		=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.'''						)
																		lowercase :							Union[str, Any]    		=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.'''						)
																		lowercase :							Optional[Any]    		=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.'''						)
																		lowercase :							Optional[int]    		=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.'''						)
																		lowercase :							str    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(__magic_name__						)






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple=None   , __magic_name__						:     Optional[Any]=None   , __magic_name__						:     Union[str, Any]=True						)				->  Union[str, Any]:
						if config_path is not None:
												lowercase :							Optional[Any]    		=WavaVecaConformerConfig.from_pretrained(__magic_name__   , hidden_act='''swish'''						)
						else:
												lowercase :							Optional[int]    		=WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												lowercase :							Dict    		='''rotary'''

						if is_finetuned:
												if dict_path:
																		lowercase :							Optional[Any]    		=Dictionary.load(__magic_name__						)

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

																		# fairseq has the <pad> and <s> switched
																		lowercase :							str    		=0
																		lowercase :							List[Any]    		=1
																		with open(__magic_name__   , '''w'''   , encoding='''utf-8'''						) as vocab_handle:
																								json.dump(__magic_name__   , __magic_name__						)
																		lowercase :							List[str]    		=WavaVecaCTCTokenizer(
																		    __magic_name__   , 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=__magic_name__   , )
																		lowercase :							Optional[int]    		=True if config.feat_extract_norm == '''layer''' else False
																		lowercase :							str    		=WavaVecaFeatureExtractor(
																		    feature_size=1   , sampling_rate=16000   , padding_value=0   , do_normalize=__magic_name__   , return_attention_mask=__magic_name__   , )
																		lowercase :							Tuple    		=WavaVecaProcessor(feature_extractor=__magic_name__   , tokenizer=__magic_name__						)
																		processor.save_pretrained(__magic_name__						)

												lowercase :							str    		=WavaVecaConformerForCTC(__magic_name__						)
						else:
												lowercase :							Tuple    		=WavaVecaConformerForPreTraining(__magic_name__						)

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

												lowercase		,	lowercase		,	lowercase :							str    		=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   , task=__magic_name__						)

						lowercase :							List[Any]    		=model[0].eval()

						recursively_load_weights(__magic_name__   , __magic_name__   , not is_finetuned						)

						hf_wavavec.save_pretrained(__magic_name__						)


if __name__ == "__main__":
							UpperCamelCase_    						=       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"""
							)
							UpperCamelCase_    						=       parser.parse_args()
							convert_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)





