
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							=			logging.get_logger(__name__)





class 							UpperCamelCase__						:






       """simple docstring"""


       def __init__(				self      ,			_A = None      ,			_A = None      ,			_A=None      ,			_A=None     )							->							str:
          if not conversation_id:
             SCREAMING_SNAKE_CASE_     =	uuid.uuida()
          if past_user_inputs is None:
             SCREAMING_SNAKE_CASE_     =	[]
          if generated_responses is None:
             SCREAMING_SNAKE_CASE_     =	[]

          SCREAMING_SNAKE_CASE_     =	conversation_id
          SCREAMING_SNAKE_CASE_     =	past_user_inputs
          SCREAMING_SNAKE_CASE_     =	generated_responses
          SCREAMING_SNAKE_CASE_     =	text



       def __eq__(				self      ,			_A     )							->							Dict:
          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      ,			_A      ,			_A = 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}\".'''     )
                SCREAMING_SNAKE_CASE_     =	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:
             SCREAMING_SNAKE_CASE_     =	text



       def   _UpperCamelCase     (				self     )							->							List[Any]:
          if self.new_user_input:
             self.past_user_inputs.append(self.new_user_input     )
          SCREAMING_SNAKE_CASE_     =	None



       def   _UpperCamelCase     (				self      ,			_A     )							->							Tuple:
          self.generated_responses.append(__UpperCAmelCase     )



       def   _UpperCamelCase     (				self     )							->							List[str]:
          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:
          SCREAMING_SNAKE_CASE_     =	F'''Conversation id: {self.uuid} \n'''
          for is_user, text in self.iter_texts():
             SCREAMING_SNAKE_CASE_     =	"""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 							UpperCamelCase__						(     SCREAMING_SNAKE_CASE_     ):






       """simple docstring"""


       def __init__(				self      ,			*_A      ,			**_A     )							->							Tuple:
          super().__init__(*__UpperCAmelCase      ,			**__UpperCAmelCase     )
          if self.tokenizer.pad_token_id is None:
             SCREAMING_SNAKE_CASE_     =	self.tokenizer.eos_token



       def   _UpperCamelCase     (				self      ,			_A=None      ,			_A=None      ,			_A=None      ,			**_A     )							->							Optional[int]:
          SCREAMING_SNAKE_CASE_     =	{}
          SCREAMING_SNAKE_CASE_     =	{}
          SCREAMING_SNAKE_CASE_     =	{}

          if min_length_for_response is not None:
             SCREAMING_SNAKE_CASE_     =	min_length_for_response
          if minimum_tokens is not None:
             SCREAMING_SNAKE_CASE_     =	minimum_tokens

          if "max_length" in generate_kwargs:
             SCREAMING_SNAKE_CASE_     =	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:
             SCREAMING_SNAKE_CASE_     =	clean_up_tokenization_spaces

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



       def __call__(				self      ,			_A      ,			_A=0      ,			**_A     )							->							List[str]:
          SCREAMING_SNAKE_CASE_     =	super().__call__(__UpperCAmelCase      ,			num_workers=__UpperCAmelCase      ,			**__UpperCAmelCase     )
          if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase     ) and len(__UpperCAmelCase     ) == 1:
             return outputs[0]
          return outputs



       def   _UpperCamelCase     (				self      ,			_A      ,			_A=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'''     ):
             SCREAMING_SNAKE_CASE_     =	self.tokenizer._build_conversation_input_ids(__UpperCAmelCase     )
          else:
             # If the tokenizer cannot handle conversations, we default to only the old version
             SCREAMING_SNAKE_CASE_     =	self._legacy_parse_and_tokenize(__UpperCAmelCase     )

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



       def   _UpperCamelCase     (				self      ,			_A      ,			_A=10      ,			**_A     )							->							Dict:
          SCREAMING_SNAKE_CASE_     =	generate_kwargs.get('''max_length'''      ,			self.model.config.max_length     )

          SCREAMING_SNAKE_CASE_     =	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})'''     )
             SCREAMING_SNAKE_CASE_     =	max_length - minimum_tokens
             SCREAMING_SNAKE_CASE_     =	model_inputs["""input_ids"""][:, -trim:]
             if "attention_mask" in model_inputs:
                SCREAMING_SNAKE_CASE_     =	model_inputs["""attention_mask"""][:, -trim:]
          SCREAMING_SNAKE_CASE_     =	model_inputs.pop('''conversation'''     )
          SCREAMING_SNAKE_CASE_     =	max_length
          SCREAMING_SNAKE_CASE_     =	self.model.generate(**__UpperCAmelCase      ,			**__UpperCAmelCase     )
          if self.model.config.is_encoder_decoder:
             SCREAMING_SNAKE_CASE_     =	1
          else:
             SCREAMING_SNAKE_CASE_     =	n
          return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



       def   _UpperCamelCase     (				self      ,			_A      ,			_A=True     )							->							List[str]:
          SCREAMING_SNAKE_CASE_     =	model_outputs["""output_ids"""]
          SCREAMING_SNAKE_CASE_     =	self.tokenizer.decode(
              output_ids[0]      ,			skip_special_tokens=__UpperCAmelCase      ,			clean_up_tokenization_spaces=__UpperCAmelCase      ,			)
          SCREAMING_SNAKE_CASE_     =	model_outputs["""conversation"""]
          conversation.mark_processed()
          conversation.append_response(__UpperCAmelCase     )
          return conversation




       def   _UpperCamelCase     (				self      ,			_A     )							->							Dict:
          SCREAMING_SNAKE_CASE_     =	self.tokenizer.eos_token_id
          SCREAMING_SNAKE_CASE_     =	[]
          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:
             SCREAMING_SNAKE_CASE_     =	input_ids[-self.tokenizer.model_max_length :]
          return input_ids







'''simple docstring'''





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        with open(UpperCamelCase							,    """rb"""			) as flax_state_f:
            lowerCAmelCase__ :  Union[str, Any]							      =					from_bytes(UpperCamelCase							,    flax_state_f.read()			)
    except UnpicklingError as e:
        try:
            with open(UpperCamelCase			) as f:
                if f.read().startswith("""version"""			):
                    raise OSError(
                        """You seem to have cloned a repository without having git-lfs installed. Please"""
                        """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                        """ folder you cloned."""			)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """			)

    return load_flax_weights_in_pytorch_model(UpperCamelCase							,    UpperCamelCase			)




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
            """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
            """ instructions."""			)
        raise

    # check if we have bf16 weights
    lowerCAmelCase__ :  str							      =					flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase			: x.dtype == jnp.bfloataa							,    UpperCamelCase			)			).values()
    if any(UpperCamelCase			):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
            """before loading those in PyTorch model."""			)
        lowerCAmelCase__ :  Dict							      =					jax.tree_util.tree_map(
            lambda UpperCamelCase			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params							,    UpperCamelCase			)

    lowerCAmelCase__ :  Any							      =					""""""

    lowerCAmelCase__ :  Any							      =					flatten_dict(UpperCamelCase							,    sep="""."""			)
    lowerCAmelCase__ :  Optional[int]							      =					pt_model.state_dict()

    # keep track of unexpected & missing keys
    lowerCAmelCase__ :  Optional[Any]							      =					[]
    lowerCAmelCase__ :  int							      =					set(pt_model_dict.keys()			)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        lowerCAmelCase__ :  Union[str, Any]							      =					flax_key_tuple.split("""."""			)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            lowerCAmelCase__ :  Optional[int]							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					jnp.transpose(UpperCamelCase							,    (3, 2, 0, 1)			)
        elif flax_key_tuple_array[-1] == "kernel":
            lowerCAmelCase__ :  str							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            lowerCAmelCase__ :  int							      =					flax_key_tuple_array[:-1] + ["""weight"""]

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(UpperCamelCase			):
                lowerCAmelCase__ :  List[str]							      =					(
                    flax_key_tuple_string.replace("""_0"""							,    """.0"""			)
                    .replace("""_1"""							,    """.1"""			)
                    .replace("""_2"""							,    """.2"""			)
                    .replace("""_3"""							,    """.3"""			)
                    .replace("""_4"""							,    """.4"""			)
                    .replace("""_5"""							,    """.5"""			)
                    .replace("""_6"""							,    """.6"""			)
                    .replace("""_7"""							,    """.7"""			)
                    .replace("""_8"""							,    """.8"""			)
                    .replace("""_9"""							,    """.9"""			)
                )

        lowerCAmelCase__ :  Union[str, Any]							      =					""".""".join(UpperCamelCase			)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)
            else:
                # add weight to pytorch dict
                lowerCAmelCase__ :  int							      =					np.asarray(UpperCamelCase			) if not isinstance(UpperCamelCase							,    np.ndarray			) else flax_tensor
                lowerCAmelCase__ :  int							      =					torch.from_numpy(UpperCamelCase			)
                # remove from missing keys
                missing_keys.remove(UpperCamelCase			)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(UpperCamelCase			)

    pt_model.load_state_dict(UpperCamelCase			)

    # re-transform missing_keys to list
    lowerCAmelCase__ :  Optional[int]							      =					list(UpperCamelCase			)

    if len(UpperCamelCase			) > 0:
        logger.warning(
            """Some weights of the Flax model were not used when initializing the PyTorch model"""
            f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
            f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
            """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
            f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
            """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
            """ FlaxBertForSequenceClassification model)."""			)
    if len(UpperCamelCase			) > 0:
        logger.warning(
            f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
            f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
            """ use it for predictions and inference."""			)

    return pt_model


