
"""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


_snake_case          =						(
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


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






def 	__snake_case  (    ):






       """simple docstring"""



       _lowerCAmelCase  	=			'https://pypi.org/pypi/diffusers/json'
       _lowerCAmelCase  	=			json.loads(request.urlopen(SCREAMING_SNAKE_CASE						).read()						)['releases'].keys()
       return sorted(SCREAMING_SNAKE_CASE     ,  key=lambda SCREAMING_SNAKE_CASE						: version.Version(SCREAMING_SNAKE_CASE						)						)






def 	__snake_case  (    ):






       """simple docstring"""



       if HF_MODULES_CACHE in sys.path:
              return

       sys.path.append(SCREAMING_SNAKE_CASE						)
       os.makedirs(SCREAMING_SNAKE_CASE     ,  exist_ok=SCREAMING_SNAKE_CASE						)
       _lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						) / '__init__.py'
       if not init_path.exists():
              init_path.touch()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, os.PathLike]						):






       """simple docstring"""



       init_hf_modules()
       _lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						) / 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(SCREAMING_SNAKE_CASE     ,  exist_ok=SCREAMING_SNAKE_CASE						)
       _lowerCAmelCase  	=			dynamic_module_path / '__init__.py'
       if not init_path.exists():
              init_path.touch()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]						):






       """simple docstring"""



       with open(SCREAMING_SNAKE_CASE     ,  'r'     ,  encoding='utf-8'						) as f:
              _lowerCAmelCase  	=			f.read()

       # Imports of the form `import .xxx`
       _lowerCAmelCase  	=			re.findall('^\s*import\s+\.(\S+)\s*$'     ,  SCREAMING_SNAKE_CASE     ,  flags=re.MULTILINE						)
       # Imports of the form `from .xxx import yyy`
       relative_imports += re.findall('^\s*from\s+\.(\S+)\s+import'     ,  SCREAMING_SNAKE_CASE     ,  flags=re.MULTILINE						)
       # Unique-ify
       return list(set(SCREAMING_SNAKE_CASE						)						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






       """simple docstring"""



       _lowerCAmelCase  	=			False
       _lowerCAmelCase  	=			[module_file]
       _lowerCAmelCase  	=			[]

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

              _lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						).parent
              _lowerCAmelCase  	=			[str(module_path / m						) for m in new_imports]
              _lowerCAmelCase  	=			[f for f in new_import_files if f not in all_relative_imports]
              _lowerCAmelCase  	=			[f"""{f}.py""" for f in new_import_files]

              _lowerCAmelCase  	=			len(SCREAMING_SNAKE_CASE						) == 0
              all_relative_imports.extend(SCREAMING_SNAKE_CASE						)

       return all_relative_imports






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict						):






       """simple docstring"""



       with open(SCREAMING_SNAKE_CASE     ,  'r'     ,  encoding='utf-8'						) as f:
              _lowerCAmelCase  	=			f.read()

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

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

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

       return get_relative_imports(SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Tuple						):






       """simple docstring"""



       _lowerCAmelCase  	=			module_path.replace(os.path.sep     ,  '.'						)
       _lowerCAmelCase  	=			importlib.import_module(SCREAMING_SNAKE_CASE						)

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]						):






       """simple docstring"""



       from ..pipelines import DiffusionPipeline

       _lowerCAmelCase  	=			dict(inspect.getmembers(SCREAMING_SNAKE_CASE     ,  inspect.isclass						)						)

       _lowerCAmelCase  	=			None
       for cls_name, cls in cls_members.items():
              if (
                  cls_name != DiffusionPipeline.__name__
                  and issubclass(cls     ,  SCREAMING_SNAKE_CASE						)
                  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  	=			cls

       return pipeline_class






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, os.PathLike]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[Union[str, os.PathLike]] = None     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  SCREAMING_SNAKE_CASE:					Optional[Dict[str, str]] = None     ,  SCREAMING_SNAKE_CASE:					Optional[Union[bool, str]] = None     ,  SCREAMING_SNAKE_CASE:					Optional[str] = None     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  ):






       """simple docstring"""



       _lowerCAmelCase  	=			str(SCREAMING_SNAKE_CASE						)

       _lowerCAmelCase  	=			os.path.join(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

       if os.path.isfile(SCREAMING_SNAKE_CASE						):
              _lowerCAmelCase  	=			module_file_or_url
              _lowerCAmelCase  	=			'local'
       elif pretrained_model_name_or_path.count('/'						) == 0:
              _lowerCAmelCase  	=			get_diffusers_versions()
              # cut ".dev0"
              _lowerCAmelCase  	=			'v' + '.'.join(__version__.split('.'						)[:3]						)

              # retrieve github version that matches
              if revision is None:
                     _lowerCAmelCase  	=			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  	=			f"""v{revision}"""
              elif revision == "main":
                     _lowerCAmelCase  	=			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  	=			COMMUNITY_PIPELINES_URL.format(revision=SCREAMING_SNAKE_CASE     ,  pipeline=SCREAMING_SNAKE_CASE						)
              try:
                     _lowerCAmelCase  	=			cached_download(
                         SCREAMING_SNAKE_CASE     ,  cache_dir=SCREAMING_SNAKE_CASE     ,  force_download=SCREAMING_SNAKE_CASE     ,  proxies=SCREAMING_SNAKE_CASE     ,  resume_download=SCREAMING_SNAKE_CASE     ,  local_files_only=SCREAMING_SNAKE_CASE     ,  use_auth_token=SCREAMING_SNAKE_CASE     ,  )
                     _lowerCAmelCase  	=			'git'
                     _lowerCAmelCase  	=			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  	=			hf_hub_download(
                         SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  cache_dir=SCREAMING_SNAKE_CASE     ,  force_download=SCREAMING_SNAKE_CASE     ,  proxies=SCREAMING_SNAKE_CASE     ,  resume_download=SCREAMING_SNAKE_CASE     ,  local_files_only=SCREAMING_SNAKE_CASE     ,  use_auth_token=SCREAMING_SNAKE_CASE     ,  )
                     _lowerCAmelCase  	=			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  	=			check_imports(SCREAMING_SNAKE_CASE						)

       # Now we move the module inside our cached dynamic modules.
       _lowerCAmelCase  	=			DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
       create_dynamic_module(SCREAMING_SNAKE_CASE						)
       _lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						) / 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(SCREAMING_SNAKE_CASE     ,  submodule_path / module_file						)
              for module_needed in modules_needed:
                     _lowerCAmelCase  	=			f"""{module_needed}.py"""
                     shutil.copy(os.path.join(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)     ,  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(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
                     _lowerCAmelCase  	=			use_auth_token
              elif use_auth_token is True:
                     _lowerCAmelCase  	=			HfFolder.get_token()
              else:
                     _lowerCAmelCase  	=			None

              _lowerCAmelCase  	=			model_info(SCREAMING_SNAKE_CASE     ,  revision=SCREAMING_SNAKE_CASE     ,  token=SCREAMING_SNAKE_CASE						).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  	=			submodule_path / commit_hash
              _lowerCAmelCase  	=			full_submodule + os.path.sep + commit_hash
              create_dynamic_module(SCREAMING_SNAKE_CASE						)

              if not (submodule_path / module_file).exists():
                     shutil.copy(SCREAMING_SNAKE_CASE     ,  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(
                                SCREAMING_SNAKE_CASE     ,  f"""{module_needed}.py"""     ,  cache_dir=SCREAMING_SNAKE_CASE     ,  force_download=SCREAMING_SNAKE_CASE     ,  resume_download=SCREAMING_SNAKE_CASE     ,  proxies=SCREAMING_SNAKE_CASE     ,  use_auth_token=SCREAMING_SNAKE_CASE     ,  revision=SCREAMING_SNAKE_CASE     ,  local_files_only=SCREAMING_SNAKE_CASE     ,  )
       return os.path.join(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, os.PathLike]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[str] = None     ,  SCREAMING_SNAKE_CASE:					Optional[Union[str, os.PathLike]] = None     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  SCREAMING_SNAKE_CASE:					Optional[Dict[str, str]] = None     ,  SCREAMING_SNAKE_CASE:					Optional[Union[bool, str]] = None     ,  SCREAMING_SNAKE_CASE:					Optional[str] = None     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  **SCREAMING_SNAKE_CASE:					Optional[Any]     ,  ):






       """simple docstring"""



       _lowerCAmelCase  	=			get_cached_module_file(
           SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  cache_dir=SCREAMING_SNAKE_CASE     ,  force_download=SCREAMING_SNAKE_CASE     ,  resume_download=SCREAMING_SNAKE_CASE     ,  proxies=SCREAMING_SNAKE_CASE     ,  use_auth_token=SCREAMING_SNAKE_CASE     ,  revision=SCREAMING_SNAKE_CASE     ,  local_files_only=SCREAMING_SNAKE_CASE     ,  )
       return get_class_in_module(SCREAMING_SNAKE_CASE     ,  final_module.replace('.py'     ,  ''						)						)








"""simple docstring"""



import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_snake_case          =						logging.get_logger(__name__)
class        _SCREAMING_SNAKE_CASE  (  enum.Enum							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Any							=					0
				SCREAMING_SNAKE_CASE_:							str							=					1






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					"generated"


				def __init__(					self	:			Optional[Any]				,     *UpperCAmelCase_	:			Dict				,     **UpperCAmelCase_	:			Union[str, Any]  )		->  Optional[Any]:







											"""simple docstring"""




											super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_  )

											self.check_model_type(
											    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
											    if self.framework == 'tf'
											    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING  )


				def 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			List[str]=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     UpperCAmelCase_	:			List[str]=None				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Optional[Any]=None				,     **UpperCAmelCase_	:			Union[str, Any]				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			{}
											if truncation is not None:
																		_lowerCAmelCase  	=			truncation

											_lowerCAmelCase  	=			generate_kwargs

											_lowerCAmelCase  	=			{}
											if return_tensors is not None and return_type is None:
																		_lowerCAmelCase  	=			ReturnType.TENSORS if return_tensors else ReturnType.TEXT
											if return_type is not None:
																		_lowerCAmelCase  	=			return_type

											if clean_up_tokenization_spaces is not None:
																		_lowerCAmelCase  	=			clean_up_tokenization_spaces

											if stop_sequence is not None:
																		_lowerCAmelCase  	=			self.tokenizer.encode(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_  )
																		if len(UpperCAmelCase_  ) > 1:
																									warnings.warn(
																									    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																									    ' the stop sequence will be used as the stop sequence string in the interim.'  )
																		_lowerCAmelCase  	=			stop_sequence_ids[0]

											return preprocess_params, forward_params, postprocess_params


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  Optional[Any]:







											"""simple docstring"""




											return True


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     *UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model.config.prefix if self.model.config.prefix is not None else ''
											if isinstance(args[0]				,     UpperCAmelCase_  ):
																		if self.tokenizer.pad_token_id is None:
																									raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'  )
																		_lowerCAmelCase  	=			([prefix + arg for arg in args[0]],)
																		_lowerCAmelCase  	=			True

											elif isinstance(args[0]				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			(prefix + args[0],)
																		_lowerCAmelCase  	=			False
											else:
																		raise ValueError(
																		    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""  )
											_lowerCAmelCase  	=			self.tokenizer(*UpperCAmelCase_				,     padding=UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     return_tensors=self.framework  )
											# This is produced by tokenizers but is an invalid generate kwargs
											if "token_type_ids" in inputs:
																		del inputs["token_type_ids"]
											return inputs


				def __call__(					self	:			Union[str, Any]				,     *UpperCAmelCase_	:			Optional[Any]				,     **UpperCAmelCase_	:			Tuple  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )
											if (
											    isinstance(args[0]				,     UpperCAmelCase_  )
											    and all(isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) for el in args[0]  )
											    and all(len(UpperCAmelCase_  ) == 1 for res in result  )
											):
																		return [res[0] for res in result]
											return result


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Dict=TruncationStrategy.DO_NOT_TRUNCATE				,     **UpperCAmelCase_	:			Tuple  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self._parse_and_tokenize(UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     **UpperCAmelCase_  )
											return inputs


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			List[Any]				,     **UpperCAmelCase_	:			List[str]  )		->  Any:







											"""simple docstring"""




											if self.framework == "pt":
																		_lowerCAmelCase	,		_lowerCAmelCase  	=			model_inputs['input_ids'].shape
											elif self.framework == "tf":
																		_lowerCAmelCase	,		_lowerCAmelCase  	=			tf.shape(model_inputs['input_ids']  ).numpy()

											_lowerCAmelCase  	=			generate_kwargs.get('min_length'				,     self.model.config.min_length  )
											_lowerCAmelCase  	=			generate_kwargs.get('max_length'				,     self.model.config.max_length  )
											self.check_inputs(UpperCAmelCase_				,     generate_kwargs['min_length']				,     generate_kwargs['max_length']  )
											_lowerCAmelCase  	=			self.model.generate(**UpperCAmelCase_				,     **UpperCAmelCase_  )
											_lowerCAmelCase  	=			output_ids.shape[0]
											if self.framework == "pt":
																		_lowerCAmelCase  	=			output_ids.reshape(UpperCAmelCase_				,     out_b // in_b				,     *output_ids.shape[1:]  )
											elif self.framework == "tf":
																		_lowerCAmelCase  	=			tf.reshape(UpperCAmelCase_				,     (in_b, out_b // in_b, *output_ids.shape[1:])  )
											return {"output_ids": output_ids}







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Union[str, Any]=ReturnType.TEXT				,     UpperCAmelCase_	:			Optional[Any]=False  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			[]
											for output_ids in model_outputs["output_ids"][0]:
																		if return_type == ReturnType.TENSORS:
																									_lowerCAmelCase  	=			{F"""{self.return_name}_token_ids""": output_ids}
																		elif return_type == ReturnType.TEXT:
																									_lowerCAmelCase  	=			{
																									    F"""{self.return_name}_text""": self.tokenizer.decode(
																									        UpperCAmelCase_				,     skip_special_tokens=UpperCAmelCase_				,     clean_up_tokenization_spaces=UpperCAmelCase_				,     )
																									}
																		records.append(UpperCAmelCase_  )
											return records






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					"summary"


				def __call__(					self	:			Optional[int]				,     *UpperCAmelCase_	:			Optional[Any]				,     **UpperCAmelCase_	:			Dict  )		->  int:







											"""simple docstring"""




											return super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  bool:







											"""simple docstring"""




											if max_length < min_length:
																		logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""  )

											if input_length < max_length:
																		logger.warning(
																		    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
																		    'a summarization task, where outputs shorter than the input are typically wanted, you might '
																		    F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""  )






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					"translation"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  Any:







											"""simple docstring"""




											if input_length > 0.9 * max_length:
																		logger.warning(
																		    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
																		    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'  )
											return True


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     *UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Dict=None  )		->  Optional[int]:







											"""simple docstring"""




											if getattr(self.tokenizer				,     '_build_translation_inputs'				,     UpperCAmelCase_  ):
																		return self.tokenizer._build_translation_inputs(
																		    *UpperCAmelCase_				,     return_tensors=self.framework				,     truncation=UpperCAmelCase_				,     src_lang=UpperCAmelCase_				,     tgt_lang=UpperCAmelCase_  )
											else:
																		return super()._parse_and_tokenize(*UpperCAmelCase_				,     truncation=UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     **UpperCAmelCase_	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			super()._sanitize_parameters(**UpperCAmelCase_  )
											if src_lang is not None:
																		_lowerCAmelCase  	=			src_lang
											if tgt_lang is not None:
																		_lowerCAmelCase  	=			tgt_lang
											if src_lang is None and tgt_lang is None:
																		# Backward compatibility, direct arguments use is preferred.
																		_lowerCAmelCase  	=			kwargs.get('task'				,     self.task  )
																		_lowerCAmelCase  	=			task.split('_'  )
																		if task and len(UpperCAmelCase_  ) == 4:
																									# translation, XX, to YY
																									_lowerCAmelCase  	=			items[1]
																									_lowerCAmelCase  	=			items[3]
											return preprocess_params, forward_params, postprocess_params







				def __call__(					self	:			Optional[int]				,     *UpperCAmelCase_	:			int				,     **UpperCAmelCase_	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											return super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )






