"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Optional[int]   =   {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def       A__    (	lowercase_     :	Tuple      , lowercase_     :	str      , lowercase_     :	Union[str, Any]      , lowercase_     :	Any      , lowercase_     :	Tuple						)  ->							Dict:
   for attribute in key.split('.'						):
      __lowerCamelCase    		=					getattr(lowercase_      , lowercase_						)

   if weight_type is not None:
      __lowerCamelCase    		=					getattr(lowercase_      , lowercase_						).shape
   else:
      __lowerCamelCase    		=					hf_pointer.shape

   assert hf_shape == value.shape, (
       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
   else:
      __lowerCamelCase    		=					value

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



def       A__    (	lowercase_     :	Dict      , lowercase_     :	Union[str, Any]      , lowercase_     :	int						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					[]
   __lowerCamelCase    		=					fairseq_model.state_dict()

   __lowerCamelCase    		=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __lowerCamelCase    		=					False
      if "conv_layers" in name:
         load_conv_layer(
             lowercase_      , lowercase_      , lowercase_      , lowercase_      , hf_model.config.feat_extract_norm == 'group'      , )
         __lowerCamelCase    		=					True
      else:
         for key, mapped_key in MAPPING.items():
            __lowerCamelCase    		=					'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') 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(lowercase_						)[0].split('.'						)[-2]
                  if not layer_index.isnumeric():
                     continue
                  __lowerCamelCase    		=					mapped_key.replace('*'      , lowercase_						)
               if "weight_g" in name:
                  __lowerCamelCase    		=					'weight_g'
               elif "weight_v" in name:
                  __lowerCamelCase    		=					'weight_v'
               elif "weight" in name:
                  __lowerCamelCase    		=					'weight'
               elif "bias" in name:
                  __lowerCamelCase    		=					'bias'
               else:
                  __lowerCamelCase    		=					None
               set_recursively(lowercase_      , lowercase_      , lowercase_      , lowercase_      , lowercase_						)
            continue
      if not is_used:
         unused_weights.append(lowercase_						)

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



def       A__    (	lowercase_     :	List[Any]      , lowercase_     :	int      , lowercase_     :	List[Any]      , lowercase_     :	int      , lowercase_     :	Union[str, Any]						)  ->							int:
   __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:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             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:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but {feature_extractor[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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor[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(lowercase_						)



def       A__    (	lowercase_     :	Dict      , lowercase_     :	int						)  ->							Optional[int]:
   __lowerCamelCase    		=					SEWDConfig()
   if is_finetuned:
      __lowerCamelCase    		=					model.wav_encoder.wav_model.cfg
   else:
      __lowerCamelCase    		=					model.cfg

   __lowerCamelCase    		=					fs_config.conv_bias
   __lowerCamelCase    		=					eval(fs_config.conv_feature_layers						)
   __lowerCamelCase    		=					[x[0] for x in conv_layers]
   __lowerCamelCase    		=					[x[1] for x in conv_layers]
   __lowerCamelCase    		=					[x[2] for x in conv_layers]
   __lowerCamelCase    		=					'gelu'
   __lowerCamelCase    		=					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
   __lowerCamelCase    		=					0.0
   __lowerCamelCase    		=					fs_config.activation_fn.name
   __lowerCamelCase    		=					fs_config.encoder_embed_dim
   __lowerCamelCase    		=					0.0_2
   __lowerCamelCase    		=					fs_config.encoder_ffn_embed_dim
   __lowerCamelCase    		=					1e-5
   __lowerCamelCase    		=					fs_config.encoder_layerdrop
   __lowerCamelCase    		=					fs_config.encoder_attention_heads
   __lowerCamelCase    		=					fs_config.conv_pos_groups
   __lowerCamelCase    		=					fs_config.conv_pos
   __lowerCamelCase    		=					len(lowercase_						)
   __lowerCamelCase    		=					fs_config.encoder_layers
   __lowerCamelCase    		=					fs_config.squeeze_factor
   # DeBERTa-specific parameters:
   __lowerCamelCase    		=					fs_config.max_position_embeddings
   __lowerCamelCase    		=					fs_config.position_buckets
   __lowerCamelCase    		=					fs_config.share_att_key
   __lowerCamelCase    		=					fs_config.relative_attention
   __lowerCamelCase    		=					fs_config.position_biased_input
   __lowerCamelCase    		=					tuple(fs_config.pos_att_type.split('|'						)						)
   __lowerCamelCase    		=					fs_config.norm_rel_ebd

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      __lowerCamelCase    		=					model.cfg
      __lowerCamelCase    		=					fs_config.final_dropout
      __lowerCamelCase    		=					fs_config.layerdrop
   __lowerCamelCase    		=					fs_config.activation_dropout
   __lowerCamelCase    		=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   __lowerCamelCase    		=					fs_config.attention_dropout
   __lowerCamelCase    		=					fs_config.dropout_input
   __lowerCamelCase    		=					fs_config.dropout
   __lowerCamelCase    		=					fs_config.mask_channel_length
   __lowerCamelCase    		=					fs_config.mask_channel_prob
   __lowerCamelCase    		=					fs_config.mask_length
   __lowerCamelCase    		=					fs_config.mask_prob

   __lowerCamelCase    		=					'Wav2Vec2FeatureExtractor'
   __lowerCamelCase    		=					'Wav2Vec2CTCTokenizer'

   return config



@torch.no_grad()
def       A__    (	lowercase_     :	Any      , lowercase_     :	Tuple      , lowercase_     :	Dict=None      , lowercase_     :	Any=None      , lowercase_     :	Optional[int]=True						)  ->							Optional[int]:

   if is_finetuned:
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]      , arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						)}						)
   else:
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

   if config_path is not None:
      __lowerCamelCase    		=					SEWDConfig.from_pretrained(lowercase_						)
   else:
      __lowerCamelCase    		=					convert_config(model[0]      , lowercase_						)
   __lowerCamelCase    		=					model[0].eval()

   __lowerCamelCase    		=					True if config.feat_extract_norm == 'layer' else False
   __lowerCamelCase    		=					WavaVecaFeatureExtractor(
       feature_size=1      , sampling_rate=16000      , padding_value=0      , do_normalize=lowercase_      , return_attention_mask=lowercase_      , )

   if is_finetuned:
      if dict_path:
         __lowerCamelCase    		=					Dictionary.load(lowercase_						)

         # 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.pad_index
         __lowerCamelCase    		=					target_dict.bos_index
         __lowerCamelCase    		=					target_dict.eos_index
         __lowerCamelCase    		=					len(target_dict.symbols						)
         __lowerCamelCase    		=					os.path.join(lowercase_      , 'vocab.json'						)
         if not os.path.isdir(lowercase_						):
            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase_						)						)
            return
         os.makedirs(lowercase_      , exist_ok=lowercase_						)
         with open(lowercase_      , 'w'      , encoding='utf-8'						) as vocab_handle:
            json.dump(target_dict.indices      , lowercase_						)
         __lowerCamelCase    		=					WavaVecaCTCTokenizer(
             lowercase_      , 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=lowercase_      , )
         __lowerCamelCase    		=					WavaVecaProcessor(feature_extractor=lowercase_      , tokenizer=lowercase_						)
         processor.save_pretrained(lowercase_						)

      __lowerCamelCase    		=					SEWDForCTC(lowercase_						)
   else:
      __lowerCamelCase    		=					SEWDModel(lowercase_						)
      feature_extractor.save_pretrained(lowercase_						)

   recursively_load_weights(lowercase_      , lowercase_      , lowercase_						)

   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		List[Any]   =   argparse.ArgumentParser()
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
     parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
     parser.add_argument(
         '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )







"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					13
      __lowerCamelCase    		=					7
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					99
      __lowerCamelCase    		=					32
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					37
      __lowerCamelCase    		=					'gelu'
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					512
      __lowerCamelCase    		=					16
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					0.02
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					self.vocab_size - 1







   def    _A				(	self	):


      '''simple docstring'''

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

      __lowerCamelCase    		=					None
      if self.use_input_mask:
         __lowerCamelCase    		=					random_attention_mask([self.batch_size, self.seq_length]	)

      __lowerCamelCase    		=					None
      if self.use_token_type_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

      __lowerCamelCase    		=					None
      if self.use_mc_token_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.num_choices] ,				self.seq_length	)

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					CTRLConfig(
          vocab_size=self.vocab_size ,				n_embd=self.hidden_size ,				n_layer=self.num_hidden_layers ,				n_head=self.num_attention_heads ,				n_positions=self.max_position_embeddings ,				pad_token_id=self.pad_token_id ,				)

      __lowerCamelCase    		=					ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,				2	)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					[input_ids, None, input_mask]  # None is the input for 'past'
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'token_type_ids': token_type_ids,
          'labels': sequence_labels,
      }
      __lowerCamelCase    		=					TFCTRLForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()

      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs

      __lowerCamelCase    		=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_tf
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   __UpperCAmelCase	:							List[str]    =    (TFCTRLLMHeadModel,) if is_tf_available() else ()
   __UpperCAmelCase	:							Dict    =    (
       {
           "feature-extraction": TFCTRLModel,
           "text-classification": TFCTRLForSequenceClassification,
           "text-generation": TFCTRLLMHeadModel,
           "zero-shot": TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase	:							str    =    False
   __UpperCAmelCase	:							Tuple    =    False







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
         # config could not be created.
         return True

      return False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				n_embd=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_lm_head(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					[TFCTRLLMHeadModel]
      __lowerCamelCase    		=					[TFCTRLForSequenceClassification]

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.build()  # may be needed for the get_bias() call below
         assert isinstance(model.get_input_embeddings() ,				tf.keras.layers.Layer	)

         if model_class in list_lm_models:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            for k, v in name.items():
               assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.Variable	)
         elif model_class in list_other_models_with_output_ebd:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert name is None
         else:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert x is None
            __lowerCamelCase    		=					model.get_bias()
            assert name is None







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					TFCTRLModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_tf
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel.from_pretrained('ctrl'	)
      __lowerCamelCase    		=					tf.convert_to_tensor([[11859, 0, 1611, 8]] ,				dtype=tf.intaa	)  # Legal the president is
      __lowerCamelCase    		=					[
          11859,
          0,
          1611,
          8,
          5,
          150,
          26449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          20740,
          246533,
          246533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      __lowerCamelCase    		=					model.generate(_SCREAMING_SNAKE_CASE ,				do_sample=_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(output_ids[0].numpy().tolist() ,				_SCREAMING_SNAKE_CASE	)






