


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 lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   ):
       snake_case_ =   parent
       snake_case_ =   13
       snake_case_ =   7
       snake_case_ =   30
       snake_case_ =   self.seq_length + self.mem_len
       snake_case_ =   15
       snake_case_ =   True
       snake_case_ =   True
       snake_case_ =   99
       snake_case_ =   [10, 50, 80]
       snake_case_ =   32
       snake_case_ =   32
       snake_case_ =   4
       snake_case_ =   8
       snake_case_ =   1_28
       snake_case_ =   2
       snake_case_ =   2
       snake_case_ =   None
       snake_case_ =   1
       snake_case_ =   0
       snake_case_ =   3
       snake_case_ =   self.vocab_size - 1
       snake_case_ =   0.01



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size      )
       snake_case_ =   ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size      )

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

       snake_case_ =   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 		UpperCamelCase__  (			self      ):
       random.seed(self.seed      )
       tf.random.set_seed(self.seed      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   TFTransfoXLModel(_UpperCAmelCase      )

       snake_case_ ,					snake_case_ =   model(_UpperCAmelCase      ).to_tuple()

       snake_case_ =   {'''input_ids''': input_ids_a, '''mems''': mems_a}

       snake_case_ ,					snake_case_ =   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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   TFTransfoXLLMHeadModel(_UpperCAmelCase      )

       snake_case_ ,					snake_case_ =   model(_UpperCAmelCase      ).to_tuple()

       snake_case_ =   {'''input_ids''': input_ids_a, '''labels''': lm_labels}
       snake_case_ ,					snake_case_ =   model(_UpperCAmelCase      ).to_tuple()

       snake_case_ ,					snake_case_ =   model([input_ids_a, mems_a]      ).to_tuple()

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

       snake_case_ ,					snake_case_ =   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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   TFTransfoXLForSequenceClassification(_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.prepare_config_and_inputs()
       ((snake_case_) ,					(snake_case_) ,					(snake_case_) ,					(snake_case_)) =   config_and_inputs
       snake_case_ =   {'''input_ids''': input_ids_a}
       return config, inputs_dict







@require_tf
class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 __snake_case				=						() if is_tf_available() else ()
 __snake_case				=						(
     {
         "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
 __snake_case				=						False
 __snake_case				=						False
 __snake_case				=						False
 __snake_case				=						False



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       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 		UpperCamelCase__  (			self      ):
       snake_case_ =   TFTransfoXLModelTester(self      )
       snake_case_ =   ConfigTester(self	,   config_class=_UpperCAmelCase	,   d_embed=37      )



 def 		UpperCamelCase__  (			self      ):
       self.config_tester.run_common_tests()



 def 		UpperCamelCase__  (			self      ):
       self.model_tester.set_seed()
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       self.model_tester.set_seed()
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_ =   [TFTransfoXLForSequenceClassification]

       for model_class in self.all_model_classes:
             snake_case_ =   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_ =   model.get_output_embeddings()
                   assert isinstance(_UpperCAmelCase	,   tf.keras.layers.Layer      )
                   snake_case_ =   model.get_bias()
                   assert name is None
             else:
                   snake_case_ =   model.get_output_embeddings()
                   assert x is None
                   snake_case_ =   model.get_bias()
                   assert name is None



 def 		UpperCamelCase__  (			self      ):
       # TODO JP: Make TransfoXL XLA compliant
       pass



 @slow
 def 		UpperCamelCase__  (			self      ):
       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_ =   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 		UpperCamelCase__  (			self      ):
       pass







@require_tf
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @unittest.skip('''Skip test until #12651 is resolved.'''      )
 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
       # fmt: off
       snake_case_ =   tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]	,   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_ =   [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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_ =   model.generate(_UpperCAmelCase	,   max_length=2_00	,   do_sample=_UpperCAmelCase      )
       self.assertListEqual(output_ids[0].numpy().tolist()	,   _UpperCAmelCase      )



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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.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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """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""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
UpperCAmelCase    =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      for attribute in key.split('''.'''      ):
            snake_case_ =   getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      if weight_type is not None:
            snake_case_ =   getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ).shape
      else:
            snake_case_ =   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":
            snake_case_ =   value
      elif weight_type == "weight_g":
            snake_case_ =   value
      elif weight_type == "weight_v":
            snake_case_ =   value
      elif weight_type == "bias":
            snake_case_ =   value
      else:
            snake_case_ =   value

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      snake_case_ =   []
      snake_case_ =   fairseq_model.state_dict()

      snake_case_ =   hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case_ =   False
            if "conv_layers" in name:
                  load_conv_layer(
                      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      hf_model.config.feat_extract_norm == '''group'''      ,      )
                  snake_case_ =   True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case_ =   '''unispeech_sat.''' + 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]:
                              if "layer_norm_for_extract" in name and (".".join(name.split('''.'''      )[:-1]      ) != key):
                                    # special case since naming is very similar
                                    continue
                              snake_case_ =   True
                              if "*" in mapped_key:
                                    snake_case_ =   name.split(SCREAMING_SNAKE_CASE      )[0].split('''.'''      )[-2]
                                    snake_case_ =   mapped_key.replace('''*'''      ,      SCREAMING_SNAKE_CASE      )
                              if "weight_g" in name:
                                    snake_case_ =   '''weight_g'''
                              elif "weight_v" in name:
                                    snake_case_ =   '''weight_v'''
                              elif "bias" in name:
                                    snake_case_ =   '''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    snake_case_ =   '''weight'''
                              else:
                                    snake_case_ =   None
                              set_recursively(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
                        continue
            if not is_used:
                  unused_weights.append(SCREAMING_SNAKE_CASE      )

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   full_name.split('''conv_layers.'''      )[-1]
      snake_case_ =   name.split('''.'''      )
      snake_case_ =   int(items[0]      )
      snake_case_ =   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.'''      )
                  snake_case_ =   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.'''      )
                  snake_case_ =   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[layer_id].layer_norm.bias.data.shape} was found.'''      )
                  snake_case_ =   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[layer_id].layer_norm.weight.data.shape} was found.'''      )
                  snake_case_ =   value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
      else:
            unused_weights.append(SCREAMING_SNAKE_CASE      )






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=True      )->						Dict:




      """simple docstring"""

      if config_path is not None:
            snake_case_ =   UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatConfig()

      snake_case_ =   ''''''

      if is_finetuned:
            snake_case_ =   UniSpeechSatForCTC(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE      )

      snake_case_ ,					snake_case_ ,					snake_case_ =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]      ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
      snake_case_ =   model[0].eval()

      recursively_load_weights(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE      )


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_unispeech_sat_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )