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__											=					logging.get_logger(__name__)





class      A							(				enum.Enum     ):
   __UpperCAmelCase : Dict      =			0
   __UpperCAmelCase : List[Any]      =			1





@add_end_docstrings(UpperCAmelCase_     )
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : int      =			'generated'


   def __init__(self     :						str       ,  *__UpperCAmelCase     :						Any       ,  **__UpperCAmelCase     :						Tuple		)						->				List[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 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						str=None       ,  __UpperCAmelCase     :						List[Any]=None       ,  __UpperCAmelCase     :						str=None       ,  __UpperCAmelCase     :						Optional[Any]=None       ,  __UpperCAmelCase     :						Optional[Any]=None       ,  __UpperCAmelCase     :						Dict=None       ,  **__UpperCAmelCase     :						Union[str, Any]       ,  )						->				Dict:
         """simple docstring"""



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

         UpperCAmelCase__								=  generate_kwargs

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

         if clean_up_tokenization_spaces is not None:
               UpperCAmelCase__								=  clean_up_tokenization_spaces

         if stop_sequence is not None:
               UpperCAmelCase__								=  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."		)
               UpperCAmelCase__								=  stop_sequence_ids[0]

         return preprocess_params, forward_params, postprocess_params


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



         return True


   def 			lowercase_      (self     :						Optional[int]       ,  *__UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[int]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  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"		)
               UpperCAmelCase__								=  ([prefix + arg for arg in args[0]],)
               UpperCAmelCase__								=  True

         elif isinstance(args[0]       ,  __UpperCAmelCase		):
               UpperCAmelCase__								=  (prefix + args[0],)
               UpperCAmelCase__								=  False
         else:
               raise ValueError(
                   f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""		)
         UpperCAmelCase__								=  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     :						Dict       ,  *__UpperCAmelCase     :						Optional[Any]       ,  **__UpperCAmelCase     :						Optional[Any]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  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 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE       ,  **__UpperCAmelCase     :						Dict		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self._parse_and_tokenize(__UpperCAmelCase       ,  truncation=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         return inputs


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Dict       ,  **__UpperCAmelCase     :						Optional[Any]		)						->				Any:
         """simple docstring"""



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

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





   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]=ReturnType.TEXT       ,  __UpperCAmelCase     :						Dict=False		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  []
         for output_ids in model_outputs["output_ids"][0]:
               if return_type == ReturnType.TENSORS:
                     UpperCAmelCase__								=  {f"""{self.return_name}_token_ids""": output_ids}
               elif return_type == ReturnType.TEXT:
                     UpperCAmelCase__								=  {
                         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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Union[str, Any]      =			'summary'


   def __call__(self     :						Dict       ,  *__UpperCAmelCase     :						Union[str, Any]       ,  **__UpperCAmelCase     :						int		)						->				Any:
         """simple docstring"""



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





   def 			lowercase_      (self     :						Union[str, Any]       ,  __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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			'translation'


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				Union[str, 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 			lowercase_      (self     :						int       ,  *__UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Any=TruncationStrategy.DO_NOT_TRUNCATE       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						Optional[int]=None		)						->				List[Any]:
         """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 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Dict=None       ,  __UpperCAmelCase     :						List[Any]=None       ,  **__UpperCAmelCase     :						List[str]		)						->				Dict:
         """simple docstring"""



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





   def __call__(self     :						Optional[int]       ,  *__UpperCAmelCase     :						Union[str, Any]       ,  **__UpperCAmelCase     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



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




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      UpperCamelCase__											=					np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

      # Create two fuzzy sets by defining any membership function
      # (trapmf(), gbellmf(), gaussmf(), etc).
      UpperCamelCase__											=					[0, 2_5, 5_0]
      UpperCamelCase__											=					[2_5, 5_0, 7_5]
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)

      # Compute the different operations using inbuilt functions.
      UpperCamelCase__											=					np.ones(7_5)
      UpperCamelCase__											=					np.zeros((7_5,))
      # 1. Union = max(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_or(X, young, X, middle_aged)[1]
      # 2. Intersection = min(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, middle_aged)[1]
      # 3. Complement (A) = (1- min(µA(x))
      UpperCamelCase__											=					fuzz.fuzzy_not(young)
      # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
      # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
      UpperCamelCase__											=					young + middle_aged - (young * middle_aged)
      # 6. Algebraic Product = (µA(x) * µB(x))
      UpperCamelCase__											=					young * middle_aged
      # 7. Bounded Sum = min[1,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
      # 8. Bounded difference = min[0,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

      # max-min composition
      # max-product composition

      # Plot each set A, set B and each operation result using plot() and subplot().
      from matplotlib import pyplot as plt

      plt.figure()

      plt.subplot(4, 3, 1)
      plt.plot(X, young)
      plt.title('Young')
      plt.grid(True)

      plt.subplot(4, 3, 2)
      plt.plot(X, middle_aged)
      plt.title('Middle aged')
      plt.grid(True)

      plt.subplot(4, 3, 3)
      plt.plot(X, union)
      plt.title('union')
      plt.grid(True)

      plt.subplot(4, 3, 4)
      plt.plot(X, intersection)
      plt.title('intersection')
      plt.grid(True)

      plt.subplot(4, 3, 5)
      plt.plot(X, complement_a)
      plt.title('complement_a')
      plt.grid(True)

      plt.subplot(4, 3, 6)
      plt.plot(X, difference)
      plt.title('difference a/b')
      plt.grid(True)

      plt.subplot(4, 3, 7)
      plt.plot(X, alg_sum)
      plt.title('alg_sum')
      plt.grid(True)

      plt.subplot(4, 3, 8)
      plt.plot(X, alg_product)
      plt.title('alg_product')
      plt.grid(True)

      plt.subplot(4, 3, 9)
      plt.plot(X, bdd_sum)
      plt.title('bdd_sum')
      plt.grid(True)

      plt.subplot(4, 3, 1_0)
      plt.plot(X, bdd_difference)
      plt.title('bdd_difference')
      plt.grid(True)

      plt.subplots_adjust(hspace=0.5)
      plt.show()



