

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     ):
    __lowerCamelCase						:       Tuple														=     0
    __lowerCamelCase						:       Any														=     1



@add_end_docstrings(__snake_case     )
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       List[str]														=     """generated"""





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

         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       snake_case_				(      self						,   a=None						,   a=None						,   a=None						,   a=None						,   a=None						,   a=None						,   **a						,   ):
         lowercase__    :   Optional[Any]      =       {}
         if truncation is not None:
              lowercase__    :   List[Any]      =       truncation

         lowercase__    :   Optional[Any]      =       generate_kwargs

         lowercase__    :   str      =       {}
         if return_tensors is not None and return_type is None:
              lowercase__    :   List[Any]      =       ReturnType.TENSORS if return_tensors else ReturnType.TEXT
         if return_type is not None:
              lowercase__    :   Any      =       return_type

         if clean_up_tokenization_spaces is not None:
              lowercase__    :   Union[str, Any]      =       clean_up_tokenization_spaces

         if stop_sequence is not None:
              lowercase__    :   Union[str, Any]      =       self.tokenizer.encode(a						,   add_special_tokens=a)
              if len(a) > 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.')
              lowercase__    :   int      =       stop_sequence_ids[0]

         return preprocess_params, forward_params, postprocess_params





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





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

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





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





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

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





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



@add_end_docstrings(__snake_case     )
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Union[str, Any]														=     """summary"""





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





    def       snake_case_				(      self						,   a						,   a						,   a):
         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(__snake_case     )
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Any														=     """translation"""





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





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





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




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not all(x.isalpha() for x in string			):
          raise ValueError('String must only contain alphabetic characters.'			)

     lowercase__    :   Optional[int]      =       sorted(string.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == len(set(SCREAMING_SNAKE_CASE_			)			)


if __name__ == "__main__":
       snake_case_			  = input('''Enter a string ''').strip()

       snake_case_			  = is_isogram(input_str)
       print(F'''{input_str} is {'an' if isogram else 'not an'} isogram.''')

