






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

A        =			logging.get_logger(__name__)


class 							__a							(  enum.Enum						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						0
		UpperCAmelCase__      :				List[Any]				    =						1






@add_end_docstrings(__A						)
class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Any				    =						"""generated"""


		def __init__(						self ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
						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   __snake_case			(						self ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {}
						if truncation is not None:
										SCREAMING_SNAKE_CASE_   :	Any													=   truncation

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   generate_kwargs

						SCREAMING_SNAKE_CASE_   :	Dict													=   {}
						if return_tensors is not None and return_type is None:
										SCREAMING_SNAKE_CASE_   :	Any													=   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
						if return_type is not None:
										SCREAMING_SNAKE_CASE_   :	str													=   return_type

						if clean_up_tokenization_spaces is not None:
										SCREAMING_SNAKE_CASE_   :	List[str]													=   clean_up_tokenization_spaces

						if stop_sequence is not None:
										SCREAMING_SNAKE_CASE_   :	str													=   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.'       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   stop_sequence_ids[0]

						return preprocess_params, forward_params, postprocess_params


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						return True


		def   __snake_case			(						self ,    *UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   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'       )
										SCREAMING_SNAKE_CASE_   :	int													=   ([prefix + arg for arg in args[0]],)
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   True

						elif isinstance(args[0] ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   (prefix + args[0],)
										SCREAMING_SNAKE_CASE_   :	Any													=   False
						else:
										raise ValueError(
										    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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 ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=TruncationStrategy.DO_NOT_TRUNCATE ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self._parse_and_tokenize(UpperCamelCase__ ,    truncation=UpperCamelCase__ ,    **UpperCamelCase__       )
						return inputs


		def   __snake_case			(						self ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
						if self.framework == "pt":
										SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   model_inputs['input_ids'].shape
						elif self.framework == "tf":
										SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   tf.shape(model_inputs['input_ids']       ).numpy()

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






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=ReturnType.TEXT ,    UpperCamelCase__=False       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   []
						for output_ids in model_outputs["output_ids"][0]:
										if return_type == ReturnType.TENSORS:
														SCREAMING_SNAKE_CASE_   :	str													=   {F'''{self.return_name}_token_ids''': output_ids}
										elif return_type == ReturnType.TEXT:
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {
														    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(__A						)
class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Any				    =						"""summary"""


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






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						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(__A						)
class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Tuple				    =						"""translation"""


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






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







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()