


import uuid
from typing import Any, Dict, List, Optional, Union

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

if is_torch_available():
   import torch


UpperCAmelCase_      : Tuple  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       str = None     ,							SCREAMING_SNAKE_CASE__				:       uuid.UUID = None     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       int=None     )		->			List[Any]:
             if not conversation_id:
                    a_					:		Any							=  uuid.uuida()
             if past_user_inputs is None:
                    a_					:		int							=  []
             if generated_responses is None:
                    a_					:		int							=  []

             a_					:		uuid.UUID							=  conversation_id
             a_					:		List[str]							=  past_user_inputs
             a_					:		List[str]							=  generated_responses
             a_					:		Optional[str]							=  text
      def __eq__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			Tuple:
             if not isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    return False
             if self.uuid == other.uuid:
                    return True
             return (
                 self.new_user_input == other.new_user_input
                 and self.past_user_inputs == other.past_user_inputs
                 and self.generated_responses == other.generated_responses
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       bool = False     )		->			Optional[Any]:
             if self.new_user_input:
                    if overwrite:
                           logger.warning(
                               F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
                               F"""with: \"{text}\"."""     )
                           a_					:		Optional[int]							=  text
                    else:
                           logger.warning(
                               F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
                               F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""     )
             else:
                    a_					:		Optional[Any]							=  text
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			List[str]:
             if self.new_user_input:
                    self.past_user_inputs.append(self.new_user_input     )
             a_					:		List[Any]							=  None
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			Dict:
             self.generated_responses.append(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Optional[Any]:
             for user_input, generated_response in zip(self.past_user_inputs     ,							self.generated_responses     ):
                    yield True, user_input
                    yield False, generated_response
             if self.new_user_input:
                    yield True, self.new_user_input

      def __repr__(       self				:       Tuple     )		->			Dict:
             a_					:		str							=  F"""Conversation id: {self.uuid} \n"""
             for is_user, text in self.iter_texts():
                    a_					:		List[str]							=  'user' if is_user else 'bot'
                    output += F"""{name} >> {text} \n"""
             return output
@add_end_docstrings(
    lowercase__						,    r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''						,    )
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       List[Any]     ,							*SCREAMING_SNAKE_CASE__				:       Any     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Optional[Any]:
             super().__init__(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
             if self.tokenizer.pad_token_id is None:
                    a_					:		Dict							=  self.tokenizer.eos_token
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       str=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							**SCREAMING_SNAKE_CASE__				:       str     )		->			Union[str, Any]:
             a_					:		Optional[int]							=  {}
             a_					:		Optional[Any]							=  {}
             a_					:		Dict							=  {}

             if min_length_for_response is not None:
                    a_					:		Optional[int]							=  min_length_for_response
             if minimum_tokens is not None:
                    a_					:		Union[str, Any]							=  minimum_tokens

             if "max_length" in generate_kwargs:
                    a_					:		Any							=  generate_kwargs['max_length']
                    # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
             if clean_up_tokenization_spaces is not None:
                    a_					:		List[Any]							=  clean_up_tokenization_spaces

             if generate_kwargs:
                    forward_params.update(SCREAMING_SNAKE_CASE__     )
             return preprocess_params, forward_params, postprocess_params
      def __call__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Union[Conversation, List[Conversation]]     ,							SCREAMING_SNAKE_CASE__				:       Any=0     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     )		->			List[str]:
             a_					:		Dict							=  super().__call__(SCREAMING_SNAKE_CASE__     ,							num_workers=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) and len(SCREAMING_SNAKE_CASE__     ) == 1:
                    return outputs[0]
             return outputs
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Conversation     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_2     )		->			Dict[str, Any]:
             if not isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    raise ValueError('ConversationalPipeline, expects Conversation as inputs'     )
             if conversation.new_user_input is None:
                    raise ValueError(
                        F"""Conversation with UUID {type(conversation.uuid     )} does not contain new user input to process. """
                        'Add user inputs with the conversation\'s `add_user_input` method'     )
             if hasattr(self.tokenizer     ,							'_build_conversation_input_ids'     ):
                    a_					:		str							=  self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE__     )
             else:
                    # If the tokenizer cannot handle conversations, we default to only the old version
                    a_					:		Any							=  self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE__     )

             if self.framework == "pt":
                    a_					:		Any							=  torch.LongTensor([input_ids]     )
             elif self.framework == "tf":
                    a_					:		Dict							=  tf.constant([input_ids]     )
             return {"input_ids": input_ids, "conversation": conversation}
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_0     ,							**SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		Union[str, Any]							=  generate_kwargs.get('max_length'     ,							self.model.config.max_length     )

             a_					:		int							=  model_inputs['input_ids'].shape[1]
             if max_length - minimum_tokens < n:
                    logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""     )
                    a_					:		Any							=  max_length - minimum_tokens
                    a_					:		Tuple							=  model_inputs['input_ids'][:, -trim:]
                    if "attention_mask" in model_inputs:
                           a_					:		Tuple							=  model_inputs['attention_mask'][:, -trim:]
             a_					:		Dict							=  model_inputs.pop('conversation'     )
             a_					:		List[Any]							=  max_length
             a_					:		Optional[int]							=  self.model.generate(**SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
             if self.model.config.is_encoder_decoder:
                    a_					:		Optional[int]							=  1
             else:
                    a_					:		List[str]							=  n
             return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     )		->			Optional[Any]:
             a_					:		Optional[int]							=  model_outputs['output_ids']
             a_					:		List[Any]							=  self.tokenizer.decode(
                 output_ids[0]     ,							skip_special_tokens=SCREAMING_SNAKE_CASE__     ,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     ,							)
             a_					:		Optional[int]							=  model_outputs['conversation']
             conversation.mark_processed()
             conversation.append_response(SCREAMING_SNAKE_CASE__     )
             return conversation

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Conversation     )		->			Dict:
             a_					:		Optional[int]							=  self.tokenizer.eos_token_id
             a_					:		List[Any]							=  []
             for is_user, text in conversation.iter_texts():
                    if eos_token_id is not None:
                           input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ) + [eos_token_id]     )
                    else:
                           input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )     )

             if len(SCREAMING_SNAKE_CASE__     ) > self.tokenizer.model_max_length:
                    a_					:		str							=  input_ids[-self.tokenizer.model_max_length :]
             return input_ids







'''simple docstring'''




import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCamelCase__       :	List[Any]    = {
    '''E''': 1_2.7_0,
    '''T''': 9.0_6,
    '''A''': 8.1_7,
    '''O''': 7.5_1,
    '''I''': 6.9_7,
    '''N''': 6.7_5,
    '''S''': 6.3_3,
    '''H''': 6.0_9,
    '''R''': 5.9_9,
    '''D''': 4.2_5,
    '''L''': 4.0_3,
    '''C''': 2.7_8,
    '''U''': 2.7_6,
    '''M''': 2.4_1,
    '''W''': 2.3_6,
    '''F''': 2.2_3,
    '''G''': 2.0_2,
    '''Y''': 1.9_7,
    '''P''': 1.9_3,
    '''B''': 1.2_9,
    '''V''': 0.9_8,
    '''K''': 0.7_7,
    '''J''': 0.1_5,
    '''X''': 0.1_5,
    '''Q''': 0.1_0,
    '''Z''': 0.0_7,
}
UpperCamelCase__       :	Optional[Any]    = '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
UpperCamelCase__       :	Dict    = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		int           =		{letter: 0 for letter in string.ascii_uppercase}
		for letter in message.upper():
				if letter in LETTERS:
						letter_count[letter] += 1

		return letter_count


def 				lowerCAmelCase_   (	_lowerCamelCase:   tuple					):
		return x[0]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		get_letter_count(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		dict[int, list[str]]           =		{
		    freq: [] for letter, freq in letter_to_freq.items()
		}
		for letter in LETTERS:
				freq_to_letter[letter_to_freq[letter]].append(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		dict[int, str]           =		{}

		for freq in freq_to_letter:
				freq_to_letter[freq].sort(key=ETAOIN.find       ,      reverse=_lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		"""""".join(freq_to_letter[freq]					)

		__SCREAMING_SNAKE_CASE       :		str           =		list(freq_to_letter_str.items()					)
		freq_pairs.sort(key=_lowerCamelCase       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		list[str]           =		[freq_pair[1] for freq_pair in freq_pairs]

		return "".join(_lowerCamelCase					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_frequency_order(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0
		for common_letter in ETAOIN[:6]:
				if common_letter in freq_order[:6]:
						match_score += 1

		for uncommon_letter in ETAOIN[-6:]:
				if uncommon_letter in freq_order[-6:]:
						match_score += 1

		return match_score


if __name__ == "__main__":
						import doctest

						doctest.testmod()