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


__lowercase											=      logging.get_logger(__name__)


class 			lowerCamelCase_  :






    '''simple docstring'''






    def __init__(			self				,				__lowercase = None				,				__lowercase = None				,				__lowercase=None				,				__lowercase=None) ->			Tuple:




        if not conversation_id:
            __UpperCamelCase  :Union[str, Any]    						=	uuid.uuida()
        if past_user_inputs is None:
            __UpperCamelCase  :List[str]    						=	[]
        if generated_responses is None:
            __UpperCamelCase  :str    						=	[]

        __UpperCamelCase  :int    						=	conversation_id
        __UpperCamelCase  :List[Any]    						=	past_user_inputs
        __UpperCamelCase  :Optional[int]    						=	generated_responses
        __UpperCamelCase  :str    						=	text







    def __eq__(			self				,				__lowercase) ->			Union[str, Any]:




        if not isinstance(_UpperCAmelCase				,				_UpperCAmelCase):
            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     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = False) ->			int:




        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}\".""")
                __UpperCamelCase  :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:
            __UpperCamelCase  :List[str]    						=	text







    def     UpperCamelCase__     (			self) ->			int:




        if self.new_user_input:
            self.past_user_inputs.append(self.new_user_input)
        __UpperCamelCase  :Optional[Any]    						=	None







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Dict:
        self.generated_responses.append(_UpperCAmelCase)







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


@add_end_docstrings(
    UpperCAmelCase_		,			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 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				*__lowercase				,				**__lowercase) ->			Optional[int]:
        super().__init__(*_UpperCAmelCase				,				**_UpperCAmelCase)
        if self.tokenizer.pad_token_id is None:
            __UpperCamelCase  :Tuple    						=	self.tokenizer.eos_token







    def     UpperCamelCase__     (			self				,				__lowercase=None				,				__lowercase=None				,				__lowercase=None				,				**__lowercase) ->			int:
        __UpperCamelCase  :List[str]    						=	{}
        __UpperCamelCase  :Any    						=	{}
        __UpperCamelCase  :Tuple    						=	{}

        if min_length_for_response is not None:
            __UpperCamelCase  :Union[str, Any]    						=	min_length_for_response
        if minimum_tokens is not None:
            __UpperCamelCase  :List[str]    						=	minimum_tokens

        if "max_length" in generate_kwargs:
            __UpperCamelCase  :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:
            __UpperCamelCase  :List[str]    						=	clean_up_tokenization_spaces

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







    def __call__(			self				,				__lowercase				,				__lowercase=0				,				**__lowercase) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	super().__call__(_UpperCAmelCase				,				num_workers=_UpperCAmelCase				,				**_UpperCAmelCase)
        if isinstance(_UpperCAmelCase				,				_UpperCAmelCase) and len(_UpperCAmelCase) == 1:
            return outputs[0]
        return outputs







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=32) ->			Dict[str, Any]:




        if not isinstance(_UpperCAmelCase				,				_UpperCAmelCase):
            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'''):
            __UpperCamelCase  :Tuple    						=	self.tokenizer._build_conversation_input_ids(_UpperCAmelCase)
        else:
            # If the tokenizer cannot handle conversations, we default to only the old version
            __UpperCamelCase  :int    						=	self._legacy_parse_and_tokenize(_UpperCAmelCase)

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







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=10				,				**__lowercase) ->			Tuple:
        __UpperCamelCase  :Optional[int]    						=	generate_kwargs.get('''max_length'''				,				self.model.config.max_length)

        __UpperCamelCase  :Optional[Any]    						=	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})""")
            __UpperCamelCase  :Union[str, Any]    						=	max_length - minimum_tokens
            __UpperCamelCase  :Optional[Any]    						=	model_inputs['''input_ids'''][:, -trim:]
            if "attention_mask" in model_inputs:
                __UpperCamelCase  :Tuple    						=	model_inputs['''attention_mask'''][:, -trim:]
        __UpperCamelCase  :Union[str, Any]    						=	model_inputs.pop('''conversation''')
        __UpperCamelCase  :int    						=	max_length
        __UpperCamelCase  :Optional[int]    						=	self.model.generate(**_UpperCAmelCase				,				**_UpperCAmelCase)
        if self.model.config.is_encoder_decoder:
            __UpperCamelCase  :Any    						=	1
        else:
            __UpperCamelCase  :Union[str, Any]    						=	n
        return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=True) ->			List[str]:
        __UpperCamelCase  :List[Any]    						=	model_outputs['''output_ids''']
        __UpperCamelCase  :int    						=	self.tokenizer.decode(
            output_ids[0]				,				skip_special_tokens=_UpperCAmelCase				,				clean_up_tokenization_spaces=_UpperCAmelCase				,				)
        __UpperCamelCase  :Tuple    						=	model_outputs['''conversation''']
        conversation.mark_processed()
        conversation.append_response(_UpperCAmelCase)
        return conversation







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Dict:
        __UpperCamelCase  :str    						=	self.tokenizer.eos_token_id
        __UpperCamelCase  :Optional[Any]    						=	[]
        for is_user, text in conversation.iter_texts():
            if eos_token_id is not None:
                input_ids.extend(self.tokenizer.encode(_UpperCAmelCase				,				add_special_tokens=_UpperCAmelCase) + [eos_token_id])
            else:
                input_ids.extend(self.tokenizer.encode(_UpperCAmelCase				,				add_special_tokens=_UpperCAmelCase))

        if len(_UpperCAmelCase) > self.tokenizer.model_max_length:
            __UpperCamelCase  :Any    						=	input_ids[-self.tokenizer.model_max_length :]
        return input_ids
import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





