



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


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__=None,		lowerCamelCase__=None		):
        if not conversation_id:
            A			:	Optional[int]										=       uuid.uuida()
        if past_user_inputs is None:
            A			:	Tuple										=       []
        if generated_responses is None:
            A			:	Union[str, Any]										=       []

        A			:	uuid.UUID										=       conversation_id
        A			:	List[str]										=       past_user_inputs
        A			:	List[str]										=       generated_responses
        A			:	Optional[str]										=       text






    def __eq__(	self,		lowerCamelCase__		):
        if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
            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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = False		):
        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			:	Any										=       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			:	Dict										=       text






    def       _lowerCAmelCase (	self		):
        if self.new_user_input:
            self.past_user_inputs.append(self.new_user_input		)
        A			:	Dict										=       None






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        self.generated_responses.append(lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        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		):
        A			:	Optional[Any]										=       f'''Conversation id: {self.uuid} \n'''
        for is_user, text in self.iter_texts():
            A			:	Dict										=       """user""" if is_user else """bot"""
            output += f'''{name} >> {text} \n'''
        return output






@add_end_docstrings(
    SCREAMING_SNAKE_CASE__			,						r"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "			,						)
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		*lowerCamelCase__,		**lowerCamelCase__		):
        super().__init__(*lowerCamelCase__,		**lowerCamelCase__		)
        if self.tokenizer.pad_token_id is None:
            A			:	str										=       self.tokenizer.eos_token






    def       _lowerCAmelCase (	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A			:	Union[str, Any]										=       {}
        A			:	Union[str, Any]										=       {}
        A			:	Optional[int]										=       {}

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

        if "max_length" in generate_kwargs:
            A			:	Tuple										=       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			:	Any										=       clean_up_tokenization_spaces

        if generate_kwargs:
            forward_params.update(lowerCamelCase__		)
        return preprocess_params, forward_params, postprocess_params






    def __call__(	self,		lowerCamelCase__,		lowerCamelCase__=0,		**lowerCamelCase__		):
        A			:	Tuple										=       super().__call__(lowerCamelCase__,		num_workers=lowerCamelCase__,		**lowerCamelCase__		)
        if isinstance(lowerCamelCase__,		lowerCamelCase__		) and len(lowerCamelCase__		) == 1:
            return outputs[0]
        return outputs






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=32		):
        if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
            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			:	Tuple										=       self.tokenizer._build_conversation_input_ids(lowerCamelCase__		)
        else:
            # If the tokenizer cannot handle conversations, we default to only the old version
            A			:	Union[str, Any]										=       self._legacy_parse_and_tokenize(lowerCamelCase__		)

        if self.framework == "pt":
            A			:	int										=       torch.LongTensor([input_ids]		)
        elif self.framework == "tf":
            A			:	str										=       tf.constant([input_ids]		)
        return {"input_ids": input_ids, "conversation": conversation}






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=10,		**lowerCamelCase__		):
        A			:	Optional[int]										=       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			:	List[str]										=       model_inputs["""input_ids"""][:, -trim:]
            if "attention_mask" in model_inputs:
                A			:	Optional[int]										=       model_inputs["""attention_mask"""][:, -trim:]
        A			:	Tuple										=       model_inputs.pop("""conversation"""		)
        A			:	Tuple										=       max_length
        A			:	Any										=       self.model.generate(**lowerCamelCase__,		**lowerCamelCase__		)
        if self.model.config.is_encoder_decoder:
            A			:	Optional[Any]										=       1
        else:
            A			:	Any										=       n
        return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=True		):
        A			:	Optional[int]										=       model_outputs["""output_ids"""]
        A			:	str										=       self.tokenizer.decode(
            output_ids[0],		skip_special_tokens=lowerCamelCase__,		clean_up_tokenization_spaces=lowerCamelCase__,		)
        A			:	int										=       model_outputs["""conversation"""]
        conversation.mark_processed()
        conversation.append_response(lowerCamelCase__		)
        return conversation






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       self.tokenizer.eos_token_id
        A			:	Tuple										=       []
        for is_user, text in conversation.iter_texts():
            if eos_token_id is not None:
                input_ids.extend(self.tokenizer.encode(lowerCamelCase__,		add_special_tokens=lowerCamelCase__		) + [eos_token_id]		)
            else:
                input_ids.extend(self.tokenizer.encode(lowerCamelCase__,		add_special_tokens=lowerCamelCase__		)		)

        if len(lowerCamelCase__		) > self.tokenizer.model_max_length:
            A			:	int										=       input_ids[-self.tokenizer.model_max_length :]
        return input_ids







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:Dict					=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:int					=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE_:Tuple					=						{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""facebook/blenderbot_small-90M""": 512}




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Optional[int]										=       set()
    A			:	Any										=       word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A			:	List[Any]										=       char

    A			:	Optional[int]										=       set(_lowerCAmelCase		)
    return pairs






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Union[str, Any] =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			Any =				PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Optional[Any] =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			int =				["input_ids", "attention_mask"]






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__="__start__",		lowerCamelCase__="__end__",		lowerCamelCase__="__unk__",		lowerCamelCase__="__null__",		**lowerCamelCase__,		):
        super().__init__(unk_token=lowerCamelCase__,		bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		**lowerCamelCase__		)

        with open(lowerCamelCase__,		encoding="""utf-8"""		) as vocab_handle:
            A			:	Tuple										=       json.load(lowerCamelCase__		)
        A			:	Optional[Any]										=       {v: k for k, v in self.encoder.items()}
        with open(lowerCamelCase__,		encoding="""utf-8"""		) as merges_handle:
            A			:	str										=       merges_handle.read().split("""\n"""		)[1:-1]
        A			:	List[str]										=       [tuple(merge.split()		) for merge in merges]
        A			:	int										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[int]										=       {}






    @property
    def       _lowerCAmelCase (	self		):
        return len(self.encoder		)






    def       _lowerCAmelCase (	self		):
        return dict(self.encoder,		**self.added_tokens_encoder		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if token in self.cache:
            return self.cache[token]
        A			:	Optional[int]										=       re.sub("""([.,!?()])""",		R""" \1""",		lowerCamelCase__		)
        A			:	List[Any]										=       re.sub("""(')""",		R""" \1 """,		lowerCamelCase__		)
        A			:	int										=       re.sub(R"""\s{2,}""",		""" """,		lowerCamelCase__		)
        if "\n" in token:
            A			:	Dict										=       token.replace("""\n""",		""" __newln__"""		)

        A			:	Tuple										=       token.split(""" """		)
        A			:	Union[str, Any]										=       []
        for token in tokens:
            if not len(lowerCamelCase__		):
                continue

            A			:	Optional[int]										=       token.lower()
            A			:	Optional[Any]										=       tuple(lowerCamelCase__		)
            A			:	int										=       tuple(list(word[:-1]		) + [word[-1] + """</w>"""]		)
            A			:	Tuple										=       get_pairs(lowerCamelCase__		)

            if not pairs:
                words.append(lowerCamelCase__		)
                continue

            while True:
                A			:	Any										=       min(lowerCamelCase__,		key=lambda lowerCamelCase__		: self.bpe_ranks.get(lowerCamelCase__,		float("""inf"""		)		)		)
                if bigram not in self.bpe_ranks:
                    break
                A					,							A			:	Any										=       bigram
                A			:	Optional[Any]										=       []
                A			:	Any										=       0

                while i < len(lowerCamelCase__		):
                    try:
                        A			:	List[str]										=       word.index(lowerCamelCase__,		lowerCamelCase__		)
                        new_word.extend(word[i:j]		)
                        A			:	Dict										=       j
                    except ValueError:
                        new_word.extend(word[i:]		)
                        break

                    if word[i] == first and i < len(lowerCamelCase__		) - 1 and word[i + 1] == second:
                        new_word.append(first + second		)
                        i += 2
                    else:
                        new_word.append(word[i]		)
                        i += 1
                A			:	Tuple										=       tuple(lowerCamelCase__		)
                A			:	int										=       new_word
                if len(lowerCamelCase__		) == 1:
                    break
                else:
                    A			:	Dict										=       get_pairs(lowerCamelCase__		)
            A			:	Any										=       """@@ """.join(lowerCamelCase__		)
            A			:	Dict										=       word[:-4]

            A			:	Union[str, Any]										=       word
            words.append(lowerCamelCase__		)
        return " ".join(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	int										=       []

        A			:	Optional[Any]										=       re.findall(R"""\S+\n?""",		lowerCamelCase__		)

        for token in words:
            split_tokens.extend(list(self.bpe(lowerCamelCase__		).split(""" """		)		)		)
        return split_tokens






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	List[Any]										=       token.lower()
        return self.encoder.get(lowerCamelCase__,		self.encoder.get(self.unk_token		)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.decoder.get(lowerCamelCase__,		self.unk_token		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Dict										=       """ """.join(lowerCamelCase__		).replace("""@@ """,		""""""		).strip()
        return out_string






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A			:	str										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
        A			:	int										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]		)

        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as f:
            f.write(json.dumps(self.encoder,		indent=2,		sort_keys=lowerCamelCase__,		ensure_ascii=lowerCamelCase__		) + """\n"""		)

        A			:	str										=       0
        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as writer:
            writer.write("""#version: 0.2\n"""		)
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),		key=lambda lowerCamelCase__		: kv[1]		):
                if index != token_index:
                    logger.warning(
                        f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                        """ Please check that the tokenizer is not corrupted!"""		)
                    A			:	List[Any]										=       token_index
                writer.write(""" """.join(lowerCamelCase__		) + """\n"""		)
                index += 1

        return vocab_file, merge_file


