


import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
      import tensorflow as tf







class        lowerCamelCase__	(							enum.Enum      ):






   """simple docstring"""






   _UpperCamelCase		:					Optional[Any]    =		0
   _UpperCamelCase		:					Tuple    =		1
   _UpperCamelCase		:					Dict    =		2







@add_end_docstrings(UpperCAmelCase      )
class        lowerCamelCase__	(							UpperCAmelCase      ):






   """simple docstring"""






   _UpperCamelCase		:					List[Any]    =		'\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '




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

      '''simple docstring'''


      super().__init__(*snake_case      ,						**snake_case     )
      self.check_model_type(
          TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING     )
      if "prefix" not in self._preprocess_params:
         # This is very specific. The logic is quite complex and needs to be done
         # as a "default".
         # It also defines both some preprocess_kwargs and generate_kwargs
         # which is why we cannot put them in their respective methods.
         UpperCamelCase__					=       None
         if self.model.config.prefix is not None:
            UpperCamelCase__					=       self.model.config.prefix
         if prefix is None and self.model.__class__.__name__ in [
             "XLNetLMHeadModel",
             "TransfoXLLMHeadModel",
             "TFXLNetLMHeadModel",
             "TFTransfoXLLMHeadModel",
         ]:
            # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
            UpperCamelCase__					=       self.XL_PREFIX
         if prefix is not None:
            # Recalculate some generate_kwargs linked to prefix.
            UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       self._sanitize_parameters(prefix=snake_case      ,						**self._forward_params     )
            UpperCamelCase__					=       {**self._preprocess_params, **preprocess_params}
            UpperCamelCase__					=       {**self._forward_params, **forward_params}




   def 				snake_case__				(							self      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						**snake_case      ,						):

      '''simple docstring'''


      UpperCamelCase__					=       {}
      if prefix is not None:
         UpperCamelCase__					=       prefix
      if prefix:
         UpperCamelCase__					=       self.tokenizer(
             snake_case      ,						padding=snake_case      ,						add_special_tokens=snake_case      ,						return_tensors=self.framework     )
         UpperCamelCase__					=       prefix_inputs["input_ids"].shape[-1]

      if handle_long_generation is not None:
         if handle_long_generation not in {"hole"}:
            raise ValueError(
                F'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
                " [None, 'hole']"     )
         UpperCamelCase__					=       handle_long_generation

      preprocess_params.update(snake_case     )
      UpperCamelCase__					=       generate_kwargs

      UpperCamelCase__					=       {}
      if return_full_text is not None and return_type is None:
         if return_text is not None:
            raise ValueError("`return_text` is mutually exclusive with `return_full_text`"     )
         if return_tensors is not None:
            raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`"     )
         UpperCamelCase__					=       ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
      if return_tensors is not None and return_type is None:
         if return_text is not None:
            raise ValueError("`return_text` is mutually exclusive with `return_tensors`"     )
         UpperCamelCase__					=       ReturnType.TENSORS
      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(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."     )
         UpperCamelCase__					=       stop_sequence_ids[0]

      return preprocess_params, forward_params, postprocess_params




   def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

      '''simple docstring'''


      if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
         kwargs.update({"add_space_before_punct_symbol": True}     )

      return super()._parse_and_tokenize(*snake_case      ,						**snake_case     )




   def __call__(							self      ,						snake_case      ,						**snake_case     ):

      '''simple docstring'''


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




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case=""      ,						snake_case=None      ,						**snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       self.tokenizer(
          prefix + prompt_text      ,						padding=snake_case      ,						add_special_tokens=snake_case      ,						return_tensors=self.framework     )
      UpperCamelCase__					=       prompt_text

      if handle_long_generation == "hole":
         UpperCamelCase__					=       inputs["input_ids"].shape[-1]
         if "max_new_tokens" in generate_kwargs:
            UpperCamelCase__					=       generate_kwargs["max_new_tokens"]
         else:
            UpperCamelCase__					=       generate_kwargs.get("max_length"      ,						self.model.config.max_length     ) - cur_len
            if new_tokens < 0:
               raise ValueError("We cannot infer how many new tokens are expected"     )
         if cur_len + new_tokens > self.tokenizer.model_max_length:
            UpperCamelCase__					=       self.tokenizer.model_max_length - new_tokens
            if keep_length <= 0:
               raise ValueError(
                   "We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
                   " models max length"     )

            UpperCamelCase__					=       inputs["input_ids"][:, -keep_length:]
            if "attention_mask" in inputs:
               UpperCamelCase__					=       inputs["attention_mask"][:, -keep_length:]

      return inputs




   def 				snake_case__				(							self      ,						snake_case      ,						**snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       model_inputs["input_ids"]
      UpperCamelCase__					=       model_inputs.get("attention_mask"      ,						snake_case     )
      # Allow empty prompts
      if input_ids.shape[1] == 0:
         UpperCamelCase__					=       None
         UpperCamelCase__					=       None
         UpperCamelCase__					=       1
      else:
         UpperCamelCase__					=       input_ids.shape[0]
      UpperCamelCase__					=       model_inputs.pop("prompt_text"     )

      # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
      # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
      UpperCamelCase__					=       generate_kwargs.pop("prefix_length"      ,						0     )
      if prefix_length > 0:
         UpperCamelCase__					=       "max_new_tokens" in generate_kwargs or (
             "generation_config" in generate_kwargs
             and generate_kwargs["generation_config"].max_new_tokens is not None
         )
         if not has_max_new_tokens:
            UpperCamelCase__					=       generate_kwargs.get("max_length"     ) or self.model.config.max_length
            generate_kwargs["max_length"] += prefix_length
         UpperCamelCase__					=       "min_new_tokens" in generate_kwargs or (
             "generation_config" in generate_kwargs
             and generate_kwargs["generation_config"].min_new_tokens is not None
         )
         if not has_min_new_tokens and "min_length" in generate_kwargs:
            generate_kwargs["min_length"] += prefix_length

        # BS x SL
      UpperCamelCase__					=       self.model.generate(input_ids=snake_case      ,						attention_mask=snake_case      ,						**snake_case     )
      UpperCamelCase__					=       generated_sequence.shape[0]
      if self.framework == "pt":
         UpperCamelCase__					=       generated_sequence.reshape(snake_case      ,						out_b // in_b      ,						*generated_sequence.shape[1:]     )
      elif self.framework == "tf":
         UpperCamelCase__					=       tf.reshape(snake_case      ,						(in_b, out_b // in_b, *generated_sequence.shape[1:])     )
      return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}







   def 				snake_case__				(							self      ,						snake_case      ,						snake_case=ReturnType.FULL_TEXT      ,						snake_case=True     ):

      '''simple docstring'''


      UpperCamelCase__					=       model_outputs["generated_sequence"][0]
      UpperCamelCase__					=       model_outputs["input_ids"]
      UpperCamelCase__					=       model_outputs["prompt_text"]
      UpperCamelCase__					=       generated_sequence.numpy().tolist()
      UpperCamelCase__					=       []
      for sequence in generated_sequence:
         if return_type == ReturnType.TENSORS:
            UpperCamelCase__					=       {"generated_token_ids": sequence}
         elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
            # Decode text
            UpperCamelCase__					=       self.tokenizer.decode(
                snake_case      ,						skip_special_tokens=snake_case      ,						clean_up_tokenization_spaces=snake_case      ,						)

            # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
            if input_ids is None:
               UpperCamelCase__					=       0
            else:
               UpperCamelCase__					=       len(
                   self.tokenizer.decode(
                       input_ids[0]      ,						skip_special_tokens=snake_case      ,						clean_up_tokenization_spaces=snake_case      ,						)     )

            if return_type == ReturnType.FULL_TEXT:
               UpperCamelCase__					=       prompt_text + text[prompt_length:]
            else:
               UpperCamelCase__					=       text[prompt_length:]

            UpperCamelCase__					=       {"generated_text": all_text}
         records.append(snake_case     )

      return records






from typing import Any







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data
						UpperCamelCase__					=       None







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.head
						while temp is not None:
									print(temp.data      ,						end=" "     )
									UpperCamelCase__					=       temp.next
						print()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       Node(snake_case     )
						UpperCamelCase__					=       self.head
						UpperCamelCase__					=       new_node







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if node_data_a == node_data_a:
									return
						else:
									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									if node_a is None or node_a is None:
												return

									UpperCamelCase__,      UpperCamelCase__					=       node_a.data, node_a.data


if __name__ == "__main__":
						__UpperCamelCase    		=       LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print('After swapping')
						ll.print_list()


