



"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase				:				Optional[int]   		=		logging.get_logger(__name__)

_lowercase				:				Optional[Any]   		=		{
    "post_extract_proj": "feature_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.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   snake_case__     (	__lowerCamelCase       :					Dict							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Optional[Any]			):
   """simple docstring"""
   for attribute in key.split('''.'''			):
      lowerCamelCase__	:	Dict				=getattr(__lowerCamelCase							,	__lowerCamelCase			)

   if weight_type is not None:
      lowerCamelCase__	:	Tuple				=getattr(__lowerCamelCase							,	__lowerCamelCase			).shape
   else:
      lowerCamelCase__	:	int				=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__	:	List[str]				=value
   elif weight_type == "weight_g":
      lowerCamelCase__	:	List[Any]				=value
   elif weight_type == "weight_v":
      lowerCamelCase__	:	str				=value
   elif weight_type == "bias":
      lowerCamelCase__	:	List[Any]				=value
   else:
      lowerCamelCase__	:	Optional[int]				=value

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






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Any			):
   """simple docstring"""
   lowerCamelCase__	:	Tuple				=[]
   lowerCamelCase__	:	Dict				=fairseq_model.state_dict()

   lowerCamelCase__	:	Optional[int]				=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowerCamelCase__	:	Union[str, Any]				=False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	hf_model.config.feat_extract_norm == '''group'''							,	)
         lowerCamelCase__	:	List[str]				=True
      else:
         for key, mapped_key in MAPPING.items():
            lowerCamelCase__	:	Tuple				='''sew.''' + 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__	:	Dict				=True
               if "*" in mapped_key:
                  lowerCamelCase__	:	Any				=name.split(__lowerCamelCase			)[0].split('''.'''			)[-2]
                  lowerCamelCase__	:	Optional[Any]				=mapped_key.replace('''*'''							,	__lowerCamelCase			)
               if "weight_g" in name:
                  lowerCamelCase__	:	Optional[Any]				='''weight_g'''
               elif "weight_v" in name:
                  lowerCamelCase__	:	Dict				='''weight_v'''
               elif "weight" in name:
                  lowerCamelCase__	:	Union[str, Any]				='''weight'''
               elif "bias" in name:
                  lowerCamelCase__	:	Tuple				='''bias'''
               else:
                  lowerCamelCase__	:	Tuple				=None
               set_recursively(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
            continue
      if not is_used:
         unused_weights.append(__lowerCamelCase			)

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






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Dict							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Union[str, Any]			):
   """simple docstring"""
   lowerCamelCase__	:	Union[str, Any]				=full_name.split('''conv_layers.'''			)[-1]
   lowerCamelCase__	:	List[str]				=name.split('''.'''			)
   lowerCamelCase__	:	Union[str, Any]				=int(items[0]			)
   lowerCamelCase__	:	Union[str, Any]				=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__	:	Optional[int]				=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__	:	Optional[int]				=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__	:	List[str]				=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__	:	Any				=value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
   else:
      unused_weights.append(__lowerCamelCase			)






def   snake_case__     (	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					Optional[int]			):
   """simple docstring"""
   lowerCamelCase__	:	int				=SEWConfig()
   if is_finetuned:
      lowerCamelCase__	:	Any				=model.wav_encoder.wav_model.cfg
   else:
      lowerCamelCase__	:	Optional[int]				=model.cfg

   lowerCamelCase__	:	Dict				=fs_config.conv_bias
   lowerCamelCase__	:	List[str]				=eval(fs_config.conv_feature_layers			)
   lowerCamelCase__	:	List[str]				=[x[0] for x in conv_layers]
   lowerCamelCase__	:	Tuple				=[x[1] for x in conv_layers]
   lowerCamelCase__	:	Optional[int]				=[x[2] for x in conv_layers]
   lowerCamelCase__	:	int				='''gelu'''
   lowerCamelCase__	:	Union[str, Any]				='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   lowerCamelCase__	:	Union[str, Any]				=0.0
   lowerCamelCase__	:	Union[str, Any]				=fs_config.activation_fn.name
   lowerCamelCase__	:	Union[str, Any]				=fs_config.encoder_embed_dim
   lowerCamelCase__	:	Any				=0.02
   lowerCamelCase__	:	Any				=fs_config.encoder_ffn_embed_dim
   lowerCamelCase__	:	Any				=1e-5
   lowerCamelCase__	:	int				=fs_config.encoder_layerdrop
   lowerCamelCase__	:	int				=fs_config.encoder_attention_heads
   lowerCamelCase__	:	str				=fs_config.conv_pos_groups
   lowerCamelCase__	:	Any				=fs_config.conv_pos
   lowerCamelCase__	:	Optional[Any]				=len(__lowerCamelCase			)
   lowerCamelCase__	:	str				=fs_config.encoder_layers
   lowerCamelCase__	:	List[Any]				=fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      lowerCamelCase__	:	Union[str, Any]				=model.cfg
      lowerCamelCase__	:	Union[str, Any]				=fs_config.final_dropout
      lowerCamelCase__	:	str				=fs_config.layerdrop
   lowerCamelCase__	:	str				=fs_config.activation_dropout
   lowerCamelCase__	:	int				=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   lowerCamelCase__	:	List[str]				=fs_config.attention_dropout
   lowerCamelCase__	:	int				=fs_config.dropout_input
   lowerCamelCase__	:	int				=fs_config.dropout
   lowerCamelCase__	:	Optional[int]				=fs_config.mask_channel_length
   lowerCamelCase__	:	Dict				=fs_config.mask_channel_prob
   lowerCamelCase__	:	Optional[Any]				=fs_config.mask_length
   lowerCamelCase__	:	str				=fs_config.mask_prob

   lowerCamelCase__	:	str				='''Wav2Vec2FeatureExtractor'''
   lowerCamelCase__	:	List[Any]				='''Wav2Vec2CTCTokenizer'''

   return config






@torch.no_grad()
def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Optional[Any]=None							,	__lowerCamelCase       :					str=None							,	__lowerCamelCase       :					str=True			):
   """simple docstring"""

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

   if config_path is not None:
      lowerCamelCase__	:	List[str]				=SEWConfig.from_pretrained(__lowerCamelCase			)
   else:
      lowerCamelCase__	:	str				=convert_config(model[0]							,	__lowerCamelCase			)
   lowerCamelCase__	:	Union[str, Any]				=model[0].eval()

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

   if is_finetuned:
      if dict_path:
         lowerCamelCase__	:	List[Any]				=Dictionary.load(__lowerCamelCase			)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         lowerCamelCase__	:	List[Any]				=target_dict.pad_index
         lowerCamelCase__	:	str				=target_dict.bos_index
         lowerCamelCase__	:	Optional[int]				=target_dict.pad_index
         lowerCamelCase__	:	List[str]				=target_dict.bos_index
         lowerCamelCase__	:	str				=target_dict.eos_index
         lowerCamelCase__	:	int				=len(target_dict.symbols			)
         lowerCamelCase__	:	List[Any]				=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			)
         with open(__lowerCamelCase							,	'''w'''							,	encoding='''utf-8'''			) as vocab_handle:
            json.dump(target_dict.indices							,	__lowerCamelCase			)
         lowerCamelCase__	:	Optional[int]				=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__	:	str				=WavaVecaProcessor(feature_extractor=__lowerCamelCase							,	tokenizer=__lowerCamelCase			)
         processor.save_pretrained(__lowerCamelCase			)

      lowerCamelCase__	:	Dict				=SEWForCTC(__lowerCamelCase			)
   else:
      lowerCamelCase__	:	Union[str, Any]				=SEWModel(__lowerCamelCase			)
      feature_extractor.save_pretrained(__lowerCamelCase			)

   recursively_load_weights(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

   hf_model.save_pretrained(__lowerCamelCase			)


if __name__ == "__main__":
  _lowercase				:				int   		=		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"
  )
  _lowercase				:				Any   		=		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 BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							BlenderbotSmallConfig
					_a         =							{}
					_a         =							'gelu'



					def __init__( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict=13,						lowerCamelCase							:						Optional[Any]=7,						lowerCamelCase							:						Optional[int]=True,						lowerCamelCase							:						int=False,						lowerCamelCase							:						Union[str, Any]=99,						lowerCamelCase							:						str=32,						lowerCamelCase							:						List[Any]=2,						lowerCamelCase							:						Optional[int]=4,						lowerCamelCase							:						Union[str, Any]=37,						lowerCamelCase							:						str=0.1,						lowerCamelCase							:						Optional[int]=0.1,						lowerCamelCase							:						Optional[Any]=20,						lowerCamelCase							:						int=2,						lowerCamelCase							:						Any=1,						lowerCamelCase							:						Optional[Any]=0,						)->      List[str]:
								lowerCamelCase__	:	Any				=parent
								lowerCamelCase__	:	Dict				=batch_size
								lowerCamelCase__	:	Optional[int]				=seq_length
								lowerCamelCase__	:	Tuple				=is_training
								lowerCamelCase__	:	Dict				=use_labels
								lowerCamelCase__	:	List[Any]				=vocab_size
								lowerCamelCase__	:	str				=hidden_size
								lowerCamelCase__	:	str				=num_hidden_layers
								lowerCamelCase__	:	Union[str, Any]				=num_attention_heads
								lowerCamelCase__	:	Any				=intermediate_size

								lowerCamelCase__	:	Dict				=hidden_dropout_prob
								lowerCamelCase__	:	List[Any]				=attention_probs_dropout_prob
								lowerCamelCase__	:	str				=max_position_embeddings
								lowerCamelCase__	:	Optional[int]				=eos_token_id
								lowerCamelCase__	:	str				=pad_token_id
								lowerCamelCase__	:	Union[str, Any]				=bos_token_id



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	Any				=ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ),						1  )
								lowerCamelCase__	:	Any				=tf.concat([input_ids, eos_tensor],						axis=1  )

								lowerCamelCase__	:	Optional[int]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

								lowerCamelCase__	:	int				=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,						**self.config_updates,						)
								lowerCamelCase__	:	Optional[int]				=prepare_blenderbot_small_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=TFBlenderbotSmallModel(config=lowerCamelCase  ).get_decoder()
								lowerCamelCase__	:	List[Any]				=inputs_dict['''input_ids''']

								lowerCamelCase__	:	Optional[int]				=input_ids[:1, :]
								lowerCamelCase__	:	str				=inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__	:	Union[str, Any]				=inputs_dict['''head_mask''']
								lowerCamelCase__	:	Optional[Any]				=1

								# first forward pass
								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						head_mask=lowerCamelCase,						use_cache=lowerCamelCase  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase__	:	Union[str, Any]				=ids_tensor((self.batch_size, 3),						config.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.cast(ids_tensor((self.batch_size, 3),						2  ),						tf.inta  )

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

								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
								lowerCamelCase__	:	Union[str, Any]				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase  )[0]

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

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

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






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=None							,	__lowerCamelCase       :					Dict=None							,	__lowerCamelCase       :					Optional[int]=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	List[str]				=tf.cast(tf.math.not_equal(__lowerCamelCase							,	config.pad_token_id			)							,	tf.inta			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	str				=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__	:	int				=tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	List[str]				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}

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


					'''simple docstring'''





					_a         =							(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					_a         =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					_a         =							(
					    {
					        'conversational': TFBlenderbotSmallForConditionalGeneration,
					        'feature-extraction': TFBlenderbotSmallModel,
					        'summarization': TFBlenderbotSmallForConditionalGeneration,
					        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
					        'translation': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a         =							True
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      str:
								lowerCamelCase__	:	Tuple				=TFBlenderbotSmallModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								self.config_tester.run_common_tests()




					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase  )





@require_tokenizers
@require_tf
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							[
					    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
					    '  i\'m going to throw up.\nand why is that?'
					]
					_a         =							'facebook/blenderbot_small-90M'



					@cached_property
					def 	snake_case ( self							:						Any  )->      List[Any]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )



					@cached_property
					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	str				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model




					@slow
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Dict				=self.tokenizer(self.src_text,						return_tensors='''tf'''  )
								lowerCamelCase__	:	Any				=self.model.generate(
								    model_inputs.input_ids,						attention_mask=model_inputs.attention_mask,						num_beams=2,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=self.tokenizer.batch_decode(generated_ids.numpy(),						skip_special_tokens=lowerCamelCase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)




