



"""simple docstring"""






import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_lowercase				:				List[str]   		=		(
    "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)


_lowercase				:				int   		=		logging.get_logger(__name__)  # pylint: disable=invalid-name






def   snake_case__     (	):
   """simple docstring"""
   lowerCamelCase__	:	Union[str, Any]				="https://pypi.org/pypi/diffusers/json"
   lowerCamelCase__	:	List[Any]				=json.loads(request.urlopen(_lowerCamelCase			).read()			)["releases"].keys()
   return sorted(_lowerCamelCase							,	key=lambda __lowerCamelCase			: version.Version(_lowerCamelCase			)			)






def   snake_case__     (	):
   """simple docstring"""
   # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
   if HF_MODULES_CACHE in sys.path:
      return

   sys.path.append(_lowerCamelCase			)
   os.makedirs(_lowerCamelCase							,	exist_ok=_lowerCamelCase			)
   lowerCamelCase__	:	List[Any]				=Path(_lowerCamelCase			) / "__init__.py"
   if not init_path.exists():
      init_path.touch()






def   snake_case__     (	__lowerCamelCase       :					List[str]			):
   """simple docstring"""
   init_hf_modules()
   lowerCamelCase__	:	Tuple				=Path(_lowerCamelCase			) / name
   # If the parent module does not exist yet, recursively create it.
   if not dynamic_module_path.parent.exists():
      create_dynamic_module(dynamic_module_path.parent			)
   os.makedirs(_lowerCamelCase							,	exist_ok=_lowerCamelCase			)
   lowerCamelCase__	:	Optional[int]				=dynamic_module_path / "__init__.py"
   if not init_path.exists():
      init_path.touch()






def   snake_case__     (	__lowerCamelCase       :					List[str]			):
   """simple docstring"""
   with open(_lowerCamelCase							,	'''r'''							,	encoding='''utf-8'''			) as f:
      lowerCamelCase__	:	Union[str, Any]				=f.read()

   # Imports of the form `import .xxx`
   lowerCamelCase__	:	Union[str, Any]				=re.findall('''^\s*import\s+\.(\S+)\s*$'''							,	_lowerCamelCase							,	flags=re.MULTILINE			)
   # Imports of the form `from .xxx import yyy`
   relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import'''							,	_lowerCamelCase							,	flags=re.MULTILINE			)
   # Unique-ify
   return list(set(_lowerCamelCase			)			)






def   snake_case__     (	__lowerCamelCase       :					List[str]			):
   """simple docstring"""
   lowerCamelCase__	:	Optional[int]				=False
   lowerCamelCase__	:	Tuple				=[module_file]
   lowerCamelCase__	:	Optional[int]				=[]

   # Let's recurse through all relative imports
   while not no_change:
      lowerCamelCase__	:	Optional[Any]				=[]
      for f in files_to_check:
         new_imports.extend(get_relative_imports(_lowerCamelCase			)			)

      lowerCamelCase__	:	Optional[Any]				=Path(_lowerCamelCase			).parent
      lowerCamelCase__	:	List[str]				=[str(module_path / m			) for m in new_imports]
      lowerCamelCase__	:	Optional[Any]				=[f for f in new_import_files if f not in all_relative_imports]
      lowerCamelCase__	:	Union[str, Any]				=[f'''{f}.py''' for f in new_import_files]

      lowerCamelCase__	:	Tuple				=len(_lowerCamelCase			) == 0
      all_relative_imports.extend(_lowerCamelCase			)

   return all_relative_imports






def   snake_case__     (	__lowerCamelCase       :					int			):
   """simple docstring"""
   with open(_lowerCamelCase							,	'''r'''							,	encoding='''utf-8'''			) as f:
      lowerCamelCase__	:	Dict				=f.read()

   # Imports of the form `import xxx`
   lowerCamelCase__	:	List[str]				=re.findall('''^\s*import\s+(\S+)\s*$'''							,	_lowerCamelCase							,	flags=re.MULTILINE			)
   # Imports of the form `from xxx import yyy`
   imports += re.findall('''^\s*from\s+(\S+)\s+import'''							,	_lowerCamelCase							,	flags=re.MULTILINE			)
   # Only keep the top-level module
   lowerCamelCase__	:	Optional[int]				=[imp.split('''.'''			)[0] for imp in imports if not imp.startswith('''.'''			)]

   # Unique-ify and test we got them all
   lowerCamelCase__	:	Any				=list(set(_lowerCamelCase			)			)
   lowerCamelCase__	:	Tuple				=[]
   for imp in imports:
      try:
         importlib.import_module(_lowerCamelCase			)
      except ImportError:
         missing_packages.append(_lowerCamelCase			)

   if len(_lowerCamelCase			) > 0:
      raise ImportError(
          '''This modeling file requires the following packages that were not found in your environment: '''
          f'''{", ".join(_lowerCamelCase			)}. Run `pip install {" ".join(_lowerCamelCase			)}`'''			)

   return get_relative_imports(_lowerCamelCase			)






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					List[str]			):
   """simple docstring"""
   lowerCamelCase__	:	int				=module_path.replace(os.path.sep							,	'''.'''			)
   lowerCamelCase__	:	Optional[Any]				=importlib.import_module(_lowerCamelCase			)

   if class_name is None:
      return find_pipeline_class(_lowerCamelCase			)
   return getattr(_lowerCamelCase							,	_lowerCamelCase			)






def   snake_case__     (	__lowerCamelCase       :					List[str]			):
   """simple docstring"""
   from ..pipelines import DiffusionPipeline

   lowerCamelCase__	:	int				=dict(inspect.getmembers(_lowerCamelCase							,	inspect.isclass			)			)

   lowerCamelCase__	:	Union[str, Any]				=None
   for cls_name, cls in cls_members.items():
      if (
          cls_name != DiffusionPipeline.__name__
          and issubclass(cls							,	_lowerCamelCase			)
          and cls.__module__.split('''.'''			)[0] != "diffusers"
      ):
         if pipeline_class is not None:
            raise ValueError(
                f'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
                f''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
                f''' {loaded_module}.'''			)
         lowerCamelCase__	:	Any				=cls

   return pipeline_class






def   snake_case__     (	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Union[str, Any] = None							,	__lowerCamelCase       :					Any = False							,	__lowerCamelCase       :					Optional[Any] = False							,	__lowerCamelCase       :					str = None							,	__lowerCamelCase       :					Union[str, Any] = None							,	__lowerCamelCase       :					Optional[Any] = None							,	__lowerCamelCase       :					List[str] = False							,	):
   """simple docstring"""
   lowerCamelCase__	:	List[Any]				=str(_lowerCamelCase			)

   lowerCamelCase__	:	Any				=os.path.join(_lowerCamelCase							,	_lowerCamelCase			)

   if os.path.isfile(_lowerCamelCase			):
      lowerCamelCase__	:	Union[str, Any]				=module_file_or_url
      lowerCamelCase__	:	Any				="local"
   elif pretrained_model_name_or_path.count('''/'''			) == 0:
      lowerCamelCase__	:	Optional[Any]				=get_diffusers_versions()
      # cut ".dev0"
      lowerCamelCase__	:	Union[str, Any]				="v" + ".".join(__version__.split('''.'''			)[:3]			)

      # retrieve github version that matches
      if revision is None:
         lowerCamelCase__	:	List[Any]				=latest_version if latest_version[1:] in available_versions else "main"
         logger.info(f'''Defaulting to latest_version: {revision}.'''			)
      elif revision in available_versions:
         lowerCamelCase__	:	Optional[Any]				=f'''v{revision}'''
      elif revision == "main":
         lowerCamelCase__	:	Dict				=revision
      else:
         raise ValueError(
             f'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
             f''' {", ".join(available_versions + ["main"]			)}.'''			)

      # community pipeline on GitHub
      lowerCamelCase__	:	Dict				=COMMUNITY_PIPELINES_URL.format(revision=_lowerCamelCase							,	pipeline=_lowerCamelCase			)
      try:
         lowerCamelCase__	:	Optional[int]				=cached_download(
             _lowerCamelCase							,	cache_dir=_lowerCamelCase							,	force_download=_lowerCamelCase							,	proxies=_lowerCamelCase							,	resume_download=_lowerCamelCase							,	local_files_only=_lowerCamelCase							,	use_auth_token=_lowerCamelCase							,	)
         lowerCamelCase__	:	Optional[Any]				="git"
         lowerCamelCase__	:	Any				=pretrained_model_name_or_path + ".py"
      except EnvironmentError:
         logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''			)
         raise
   else:
      try:
         # Load from URL or cache if already cached
         lowerCamelCase__	:	Any				=hf_hub_download(
             _lowerCamelCase							,	_lowerCamelCase							,	cache_dir=_lowerCamelCase							,	force_download=_lowerCamelCase							,	proxies=_lowerCamelCase							,	resume_download=_lowerCamelCase							,	local_files_only=_lowerCamelCase							,	use_auth_token=_lowerCamelCase							,	)
         lowerCamelCase__	:	Optional[Any]				=os.path.join('''local'''							,	'''--'''.join(pretrained_model_name_or_path.split('''/'''			)			)			)
      except EnvironmentError:
         logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''			)
         raise

    # Check we have all the requirements in our environment
   lowerCamelCase__	:	List[str]				=check_imports(_lowerCamelCase			)

   # Now we move the module inside our cached dynamic modules.
   lowerCamelCase__	:	List[str]				=DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
   create_dynamic_module(_lowerCamelCase			)
   lowerCamelCase__	:	Optional[int]				=Path(_lowerCamelCase			) / full_submodule
   if submodule == "local" or submodule == "git":
      # We always copy local files (we could hash the file to see if there was a change, and give them the name of
      # that hash, to only copy when there is a modification but it seems overkill for now).
      # The only reason we do the copy is to avoid putting too many folders in sys.path.
      shutil.copy(_lowerCamelCase							,	submodule_path / module_file			)
      for module_needed in modules_needed:
         lowerCamelCase__	:	int				=f'''{module_needed}.py'''
         shutil.copy(os.path.join(_lowerCamelCase							,	_lowerCamelCase			)							,	submodule_path / module_needed			)
   else:
      # Get the commit hash
      # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
      if isinstance(_lowerCamelCase							,	_lowerCamelCase			):
         lowerCamelCase__	:	Optional[Any]				=use_auth_token
      elif use_auth_token is True:
         lowerCamelCase__	:	Dict				=HfFolder.get_token()
      else:
         lowerCamelCase__	:	Tuple				=None

      lowerCamelCase__	:	List[str]				=model_info(_lowerCamelCase							,	revision=_lowerCamelCase							,	token=_lowerCamelCase			).sha

      # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
      # benefit of versioning.
      lowerCamelCase__	:	str				=submodule_path / commit_hash
      lowerCamelCase__	:	List[str]				=full_submodule + os.path.sep + commit_hash
      create_dynamic_module(_lowerCamelCase			)

      if not (submodule_path / module_file).exists():
         shutil.copy(_lowerCamelCase							,	submodule_path / module_file			)
      # Make sure we also have every file with relative
      for module_needed in modules_needed:
         if not (submodule_path / module_needed).exists():
            get_cached_module_file(
                _lowerCamelCase							,	f'''{module_needed}.py'''							,	cache_dir=_lowerCamelCase							,	force_download=_lowerCamelCase							,	resume_download=_lowerCamelCase							,	proxies=_lowerCamelCase							,	use_auth_token=_lowerCamelCase							,	revision=_lowerCamelCase							,	local_files_only=_lowerCamelCase							,	)
   return os.path.join(_lowerCamelCase							,	_lowerCamelCase			)






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Union[str, Any] = None							,	__lowerCamelCase       :					int = None							,	__lowerCamelCase       :					List[str] = False							,	__lowerCamelCase       :					Optional[int] = False							,	__lowerCamelCase       :					Tuple = None							,	__lowerCamelCase       :					List[Any] = None							,	__lowerCamelCase       :					Any = None							,	__lowerCamelCase       :					Dict = False							,	**__lowerCamelCase       :					Tuple							,	):
   """simple docstring"""
   lowerCamelCase__	:	int				=get_cached_module_file(
       _lowerCamelCase							,	_lowerCamelCase							,	cache_dir=_lowerCamelCase							,	force_download=_lowerCamelCase							,	resume_download=_lowerCamelCase							,	proxies=_lowerCamelCase							,	use_auth_token=_lowerCamelCase							,	revision=_lowerCamelCase							,	local_files_only=_lowerCamelCase							,	)
   return get_class_in_module(_lowerCamelCase							,	final_module.replace('''.py'''							,	''''''			)			)









"""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.",
								)




