

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

_UpperCAmelCase		:   str										=       logging.get_logger(__name__)







class  lowercase				(       enum.Enum			):
				__SCREAMING_SNAKE_CASE					: Any       			=			0
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			1







@add_end_docstrings(lowercase_			)
class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: str       			=			'''generated'''




				def __init__(					self      ,   *snake_case      ,   **snake_case       ):
											super().__init__(*snake_case      ,   **snake_case       )

											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     a							(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   **snake_case      ,   ):
											snake_case_							=    {}
											if truncation is not None:
																		snake_case_							=    truncation

											snake_case_							=    generate_kwargs

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

											if clean_up_tokenization_spaces is not None:
																		snake_case_							=    clean_up_tokenization_spaces

											if stop_sequence is not None:
																		snake_case_							=    self.tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
																		if len(snake_case       ) > 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.'       )
																		snake_case_							=    stop_sequence_ids[0]

											return preprocess_params, forward_params, postprocess_params




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											return True




				def     a							(					self      ,   *snake_case      ,   snake_case       ):
											snake_case_							=    self.model.config.prefix if self.model.config.prefix is not None else ''
											if isinstance(args[0]      ,   snake_case       ):
																		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'       )
																		snake_case_							=    ([prefix + arg for arg in args[0]],)
																		snake_case_							=    True

											elif isinstance(args[0]      ,   snake_case       ):
																		snake_case_							=    (prefix + args[0],)
																		snake_case_							=    False
											else:
																		raise ValueError(
																		    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''       )
											snake_case_							=    self.tokenizer(*snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   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      ,   *snake_case      ,   **snake_case       ):
											snake_case_							=    super().__call__(*snake_case      ,   **snake_case       )
											if (
											    isinstance(args[0]      ,   snake_case       )
											    and all(isinstance(snake_case      ,   snake_case       ) for el in args[0]       )
											    and all(len(snake_case       ) == 1 for res in result       )
											):
																		return [res[0] for res in result]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case=TruncationStrategy.DO_NOT_TRUNCATE      ,   **snake_case       ):
											snake_case_							=    self._parse_and_tokenize(snake_case      ,   truncation=snake_case      ,   **snake_case       )
											return inputs




				def     a							(					self      ,   snake_case      ,   **snake_case       ):
											if self.framework == "pt":
																		snake_case_				,    snake_case_							=    model_inputs['input_ids'].shape
											elif self.framework == "tf":
																		snake_case_				,    snake_case_							=    tf.shape(model_inputs['input_ids']       ).numpy()

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




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







@add_end_docstrings(lowercase_			)
class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			'''summary'''




				def __call__(					self      ,   *snake_case      ,   **snake_case       ):
											return super().__call__(*snake_case      ,   **snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											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(lowercase_			)
class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			'''translation'''




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											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     a							(					self      ,   *snake_case      ,   snake_case=TruncationStrategy.DO_NOT_TRUNCATE      ,   snake_case=None      ,   snake_case=None       ):
											if getattr(self.tokenizer      ,   '_build_translation_inputs'      ,   snake_case       ):
																		return self.tokenizer._build_translation_inputs(
																		    *snake_case      ,   return_tensors=self.framework      ,   truncation=snake_case      ,   src_lang=snake_case      ,   tgt_lang=snake_case       )
											else:
																		return super()._parse_and_tokenize(*snake_case      ,   truncation=snake_case       )




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




				def __call__(					self      ,   *snake_case      ,   **snake_case       ):
											return super().__call__(*snake_case      ,   **snake_case       )







from PIL import Image





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    (259 * (level + 255)) / (255 * (259 - level))

							def contrast(UpperCamelCase__      ) -> int:
														return int(128 + factor * (c - 128)      )

							return img.point(UpperCamelCase__      )


if __name__ == "__main__":
					# Load image
					with Image.open("""image_data/lena.jpg""") as img:
										# Change contrast to 170
										_UpperCAmelCase		:   Tuple										=       change_contrast(img, 170)
										cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")




