
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 						UpperCamelCase__    :
   def __init__(self					:   Tuple   ,							snake_case_					:   str = None   ,							snake_case_					:   uuid.UUID = None   ,							snake_case_					:   Optional[int]=None   ,							snake_case_					:   int=None  ):
    if not conversation_id:
     __a					:    Union[str, Any]													=    uuid.uuida()
    if past_user_inputs is None:
     __a					:    Any													=    []
    if generated_responses is None:
     __a					:    List[str]													=    []

    __a					:    uuid.UUID													=    conversation_id
    __a					:    List[str]													=    past_user_inputs
    __a					:    List[str]													=    generated_responses
    __a					:    Optional[str]													=    text
   def __eq__(self					:   int   ,							snake_case_					:   Union[str, Any]  ):
    if not isinstance(lowercase_   ,							lowercase_  ):
     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					:   int   ,							snake_case_					:   str   ,							snake_case_					:   bool = 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					:    List[str]													=    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					:    Any													=    text
   def  lowerCAmelCase     (self					:   Optional[Any]  ):
    if self.new_user_input:
     self.past_user_inputs.append(self.new_user_input  )
    __a					:    str													=    None
   def  lowerCAmelCase     (self					:   Tuple   ,							snake_case_					:   str  ):
    self.generated_responses.append(lowercase_  )
   def  lowerCAmelCase     (self					:   Tuple  ):
    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					:   List[Any]  ):
    __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(
    a_       ,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 						UpperCamelCase__    (			a_       ):
   def __init__(self					:   Dict   ,							*snake_case_					:   Optional[Any]   ,							**snake_case_					:   Union[str, Any]  ):
    super().__init__(*lowercase_   ,							**lowercase_  )
    if self.tokenizer.pad_token_id is None:
     __a					:    Optional[Any]													=    self.tokenizer.eos_token
   def  lowerCAmelCase     (self					:   str   ,							snake_case_					:   Dict=None   ,							snake_case_					:   Any=None   ,							snake_case_					:   Optional[int]=None   ,							**snake_case_					:   List[Any]  ):
    __a					:    str													=    {}
    __a					:    int													=    {}
    __a					:    List[str]													=    {}

    if min_length_for_response is not None:
     __a					:    Dict													=    min_length_for_response
    if minimum_tokens is not None:
     __a					:    Any													=    minimum_tokens

    if "max_length" in generate_kwargs:
     __a					:    Optional[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					:    Optional[Any]													=    clean_up_tokenization_spaces

    if generate_kwargs:
     forward_params.update(lowercase_  )
    return preprocess_params, forward_params, postprocess_params
   def __call__(self					:   Optional[int]   ,							snake_case_					:   Union[Conversation, List[Conversation]]   ,							snake_case_					:   Optional[Any]=0   ,							**snake_case_					:   Union[str, Any]  ):
    __a					:    int													=    super().__call__(lowercase_   ,							num_workers=lowercase_   ,							**lowercase_  )
    if isinstance(lowercase_   ,							lowercase_  ) and len(lowercase_  ) == 1:
     return outputs[0]
    return outputs
   def  lowerCAmelCase     (self					:   Tuple   ,							snake_case_					:   Conversation   ,							snake_case_					:   Any=3_2  ):
    if not isinstance(lowercase_   ,							lowercase_  ):
     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(lowercase_  )
    else:
     # If the tokenizer cannot handle conversations, we default to only the old version
     __a					:    Any													=    self._legacy_parse_and_tokenize(lowercase_  )

    if self.framework == "pt":
     __a					:    Tuple													=    torch.LongTensor([input_ids]  )
    elif self.framework == "tf":
     __a					:    Any													=    tf.constant([input_ids]  )
    return {"input_ids": input_ids, "conversation": conversation}
   def  lowerCAmelCase     (self					:   Optional[int]   ,							snake_case_					:   List[Any]   ,							snake_case_					:   List[str]=1_0   ,							**snake_case_					:   List[Any]  ):
    __a					:    Union[str, Any]													=    generate_kwargs.get('''max_length'''   ,							self.model.config.max_length  )

    __a					:    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})"  )
     __a					:    Optional[int]													=    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					:    str													=    model_inputs.pop('''conversation'''  )
    __a					:    Optional[int]													=    max_length
    __a					:    Tuple													=    self.model.generate(**lowercase_   ,							**lowercase_  )
    if self.model.config.is_encoder_decoder:
     __a					:    str													=    1
    else:
     __a					:    Any													=    n
    return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
   def  lowerCAmelCase     (self					:   Dict   ,							snake_case_					:   Union[str, Any]   ,							snake_case_					:   Optional[Any]=True  ):
    __a					:    int													=    model_outputs['''output_ids''']
    __a					:    str													=    self.tokenizer.decode(
        output_ids[0]   ,							skip_special_tokens=lowercase_   ,							clean_up_tokenization_spaces=lowercase_   ,							)
    __a					:    int													=    model_outputs['''conversation''']
    conversation.mark_processed()
    conversation.append_response(lowercase_  )
    return conversation







   def  lowerCAmelCase     (self					:   List[Any]   ,							snake_case_					:   Conversation  ):
    __a					:    List[str]													=    self.tokenizer.eos_token_id
    __a					:    Optional[int]													=    []
    for is_user, text in conversation.iter_texts():
     if eos_token_id is not None:
      input_ids.extend(self.tokenizer.encode(lowercase_   ,							add_special_tokens=lowercase_  ) + [eos_token_id]  )
     else:
      input_ids.extend(self.tokenizer.encode(lowercase_   ,							add_special_tokens=lowercase_  )  )

    if len(lowercase_  ) > self.tokenizer.model_max_length:
     __a					:    Optional[Any]													=    input_ids[-self.tokenizer.model_max_length :]
    return input_ids



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




