




'''simple docstring'''





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     a__					(      enum.Enum   ):
						_a							:					Optional[Any]      =					0
						_a							:					Tuple      =					1
						_a							:					Tuple      =					2






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






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




											"""simple docstring"""



											super().__init__(*__UpperCamelCase	,    **__UpperCamelCase    )
											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.
																__lowerCAmelCase            = None
																if self.model.config.prefix is not None:
																					__lowerCAmelCase            = 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.
																					__lowerCAmelCase            = self.XL_PREFIX
																if prefix is not None:
																					# Recalculate some generate_kwargs linked to prefix.
																					__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = self._sanitize_parameters(prefix=__UpperCamelCase	,    **self._forward_params    )
																					__lowerCAmelCase            = {**self._preprocess_params, **preprocess_params}
																					__lowerCAmelCase            = {**self._forward_params, **forward_params}






						def        __SCREAMING_SNAKE_CASE(     self	,    _A=None	,    _A=None	,    _A=None	,    _A=None	,    _A=None	,    _A=None	,    _A=None	,    _A=None	,    **_A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = {}
											if prefix is not None:
																__lowerCAmelCase            = prefix
											if prefix:
																__lowerCAmelCase            = self.tokenizer(
																    __UpperCamelCase	,    padding=__UpperCamelCase	,    add_special_tokens=__UpperCamelCase	,    return_tensors=self.framework    )
																__lowerCAmelCase            = 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\']"    )
																__lowerCAmelCase            = handle_long_generation

											preprocess_params.update(__UpperCamelCase    )
											__lowerCAmelCase            = generate_kwargs

											__lowerCAmelCase            = {}
											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`"    )
																__lowerCAmelCase            = 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`"    )
																__lowerCAmelCase            = ReturnType.TENSORS
											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        __SCREAMING_SNAKE_CASE(     self	,    *_A	,    **_A    ):




											"""simple docstring"""



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

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






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




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A=""	,    _A=None	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.tokenizer(
											    prefix + prompt_text	,    padding=__UpperCamelCase	,    add_special_tokens=__UpperCamelCase	,    return_tensors=self.framework    )
											__lowerCAmelCase            = prompt_text

											if handle_long_generation == "hole":
																__lowerCAmelCase            = inputs["input_ids"].shape[-1]
																if "max_new_tokens" in generate_kwargs:
																					__lowerCAmelCase            = generate_kwargs["max_new_tokens"]
																else:
																					__lowerCAmelCase            = 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:
																					__lowerCAmelCase            = 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"    )

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

											return inputs






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = model_inputs["input_ids"]
											__lowerCAmelCase            = model_inputs.get("attention_mask"	,    __UpperCamelCase    )
											# Allow empty prompts
											if input_ids.shape[1] == 0:
																__lowerCAmelCase            = None
																__lowerCAmelCase            = None
																__lowerCAmelCase            = 1
											else:
																__lowerCAmelCase            = input_ids.shape[0]
											__lowerCAmelCase            = 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.
											__lowerCAmelCase            = generate_kwargs.pop("prefix_length"	,    0    )
											if prefix_length > 0:
																__lowerCAmelCase            = "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:
																					__lowerCAmelCase            = generate_kwargs.get("max_length"    ) or self.model.config.max_length
																					generate_kwargs["max_length"] += prefix_length
																__lowerCAmelCase            = "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
											__lowerCAmelCase            = self.model.generate(input_ids=__UpperCamelCase	,    attention_mask=__UpperCamelCase	,    **__UpperCamelCase    )
											__lowerCAmelCase            = generated_sequence.shape[0]
											if self.framework == "pt":
																__lowerCAmelCase            = generated_sequence.reshape(__UpperCamelCase	,    out_b // in_b	,    *generated_sequence.shape[1:]    )
											elif self.framework == "tf":
																__lowerCAmelCase            = tf.reshape(__UpperCamelCase	,    (in_b, out_b // in_b, *generated_sequence.shape[1:])    )
											return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A=ReturnType.FULL_TEXT	,    _A=True    ):




											"""simple docstring"""



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

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

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

																					__lowerCAmelCase            = {"generated_text": all_text}
																records.append(__UpperCamelCase    )

											return records










from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class     a__					:






						def __init__(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = data
											__lowerCAmelCase            = None






class     a__					:






						def __init__(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = None
											__lowerCAmelCase            = None






						def __iter__(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.head
											while self.head:
																yield node.data
																__lowerCAmelCase            = node.next
																if node == self.head:
																					break






						def __len__(     self    ):




											"""simple docstring"""



											return sum(1 for _ in self    )






						def __repr__(     self    ):




											"""simple docstring"""



											return "->".join(str(_A    ) for item in iter(self    )    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											self.insert_nth(len(self    )	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											self.insert_nth(0	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											if index < 0 or index > len(self    ):
																raise IndexError("list index out of range."    )
											__lowerCAmelCase            = Node(_A    )
											if self.head is None:
																__lowerCAmelCase            = new_node  # first node points itself
																__lowerCAmelCase            = __lowerCAmelCase            = new_node
											elif index == 0:  # insert at head
																__lowerCAmelCase            = self.head
																__lowerCAmelCase            = __lowerCAmelCase            = new_node
											else:
																__lowerCAmelCase            = self.head
																for _ in range(index - 1    ):
																					__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = new_node
																if index == len(self    ) - 1:  # insert at tail
																					__lowerCAmelCase            = new_node






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.delete_nth(0    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.delete_nth(len(self    ) - 1    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A = 0    ):




											"""simple docstring"""



											if not 0 <= index < len(self    ):
																raise IndexError("list index out of range."    )
											__lowerCAmelCase            = self.head
											if self.head == self.tail:  # just one node
																__lowerCAmelCase            = __lowerCAmelCase            = None
											elif index == 0:  # delete head node
																__lowerCAmelCase            = self.tail.next.next
																__lowerCAmelCase            = self.head.next
											else:
																__lowerCAmelCase            = self.head
																for _ in range(index - 1    ):
																					__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = temp.next.next
																if index == len(self    ) - 1:  # delete at tail
																					__lowerCAmelCase            = temp
											return delete_node.data






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return len(self    ) == 0







def 			_a   (		):
					__lowerCAmelCase            = CircularLinkedList()
					assert len(SCREAMING_SNAKE_CASE_  ) == 0
					assert circular_linked_list.is_empty() is True
					assert str(SCREAMING_SNAKE_CASE_  ) == ""

					try:
										circular_linked_list.delete_front()
										raise AssertionError  # This should not happen
					except IndexError:
										assert True  # This should happen

					try:
										circular_linked_list.delete_tail()
										raise AssertionError  # This should not happen
					except IndexError:
										assert True  # This should happen

					try:
										circular_linked_list.delete_nth(-1  )
										raise AssertionError
					except IndexError:
										assert True

					try:
										circular_linked_list.delete_nth(0  )
										raise AssertionError
					except IndexError:
										assert True

					assert circular_linked_list.is_empty() is True
					for i in range(5  ):
										assert len(SCREAMING_SNAKE_CASE_  ) == i
										circular_linked_list.insert_nth(SCREAMING_SNAKE_CASE_   ,   i + 1  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   6  )  )

					circular_linked_list.insert_tail(6  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   7  )  )
					circular_linked_list.insert_head(0  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(0   ,   7  )  )

					assert circular_linked_list.delete_front() == 0
					assert circular_linked_list.delete_tail() == 6
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   6  )  )
					assert circular_linked_list.delete_nth(2  ) == 3

					circular_linked_list.insert_nth(2   ,   3  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   6  )  )

					assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
					import doctest

					doctest.testmod()




