
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


A     :	Union[str, Any]    = logging.get_logger(__name__)


def     __lowerCAmelCase			(			a__						,    a__  )       ->  Optional[int]:





				try:
								with open(a__						,    '''rb'''  ) as flax_state_f:
												__a										=    from_bytes(a__						,    flax_state_f.read()  )
				except UnpicklingError as e:
								try:
												with open(a__  ) 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(a__						,    a__  )






def     __lowerCAmelCase			(			a__						,    a__  )       ->  Optional[Any]:





				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
				__a										=    flatten_dict(jax.tree_util.tree_map(lambda a__  : x.dtype == jnp.bfloataa						,    a__  )  ).values()
				if any(a__  ):
								# 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.'''  )
								__a										=    jax.tree_util.tree_map(
								    lambda a__  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params						,    a__  )

				__a										=    """"""

				__a										=    flatten_dict(a__						,    sep='''.'''  )
				__a										=    pt_model.state_dict()

				# keep track of unexpected & missing keys
				__a										=    []
				__a										=    set(pt_model_dict.keys()  )

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								__a										=    flax_key_tuple.split('''.'''  )

								if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
												__a										=    flax_key_tuple_array[:-1] + ["""weight"""]
												__a										=    jnp.transpose(a__						,    (3, 2, 0, 1)  )
								elif flax_key_tuple_array[-1] == "kernel":
												__a										=    flax_key_tuple_array[:-1] + ["""weight"""]
												__a										=    flax_tensor.T
								elif flax_key_tuple_array[-1] == "scale":
												__a										=    flax_key_tuple_array[:-1] + ["""weight"""]

								if "time_embedding" not in flax_key_tuple_array:
												for i, flax_key_tuple_string in enumerate(a__  ):
																__a										=    (
																    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'''  )
																)

								__a										=    """.""".join(a__  )

								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
																__a										=    np.asarray(a__  ) if not isinstance(a__						,    np.ndarray  ) else flax_tensor
																__a										=    torch.from_numpy(a__  )
																# remove from missing keys
																missing_keys.remove(a__  )
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(a__  )

				pt_model.load_state_dict(a__  )

				# re-transform missing_keys to list
				__a										=    list(a__  )

				if len(a__  ) > 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(a__  ) > 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




'''simple docstring'''





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


_lowerCAmelCase      						=	logging.get_logger(__name__)






class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None      )    ->		str:
      if not conversation_id:
          lowerCAmelCase__ :  List[str]							      =					uuid.uuida()
      if past_user_inputs is None:
          lowerCAmelCase__ :  List[Any]							      =					[]
      if generated_responses is None:
          lowerCAmelCase__ :  str							      =					[]

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




  def __eq__(    self  ,__UpperCAmelCase      )    ->		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  ,__UpperCAmelCase  ,__UpperCAmelCase = 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}\"."""      )
              lowerCAmelCase__ :  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:
          lowerCAmelCase__ :  Optional[Any]							      =					text




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      if self.new_user_input:
          self.past_user_inputs.append(self.new_user_input      )
      lowerCAmelCase__ :  Union[str, Any]							      =					None




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		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:
      lowerCAmelCase__ :  Tuple							      =					F"""Conversation id: {self.uuid} \n"""
      for is_user, text in self.iter_texts():
          lowerCAmelCase__ :  Any							      =					"""user""" if is_user else """bot"""
          output += F"""{name} >> {text} \n"""
      return output







@add_end_docstrings(
    SCREAMING_SNAKE_CASE_						, 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_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[Any]							      =					{}
      lowerCAmelCase__ :  Optional[int]							      =					{}
      lowerCAmelCase__ :  List[str]							      =					{}

      if min_length_for_response is not None:
          lowerCAmelCase__ :  Any							      =					min_length_for_response
      if minimum_tokens is not None:
          lowerCAmelCase__ :  Optional[int]							      =					minimum_tokens

      if "max_length" in generate_kwargs:
          lowerCAmelCase__ :  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:
          lowerCAmelCase__ :  int							      =					clean_up_tokenization_spaces

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




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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=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"""      ):
          lowerCAmelCase__ :  str							      =					self.tokenizer._build_conversation_input_ids(__UpperCAmelCase      )
      else:
          # If the tokenizer cannot handle conversations, we default to only the old version
          lowerCAmelCase__ :  List[Any]							      =					self._legacy_parse_and_tokenize(__UpperCAmelCase      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=10  ,**__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Optional[Any]							      =					generate_kwargs.get("""max_length"""  ,self.model.config.max_length      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=True      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					model_outputs["""output_ids"""]
      lowerCAmelCase__ :  Tuple							      =					self.tokenizer.decode(
          output_ids[0]  ,skip_special_tokens=__UpperCAmelCase  ,clean_up_tokenization_spaces=__UpperCAmelCase  ,)
      lowerCAmelCase__ :  Union[str, Any]							      =					model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__UpperCAmelCase      )
      return conversation






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Dict							      =					self.tokenizer.eos_token_id
      lowerCAmelCase__ :  int							      =					[]
      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:
          lowerCAmelCase__ :  Optional[Any]							      =					input_ids[-self.tokenizer.model_max_length :]
      return input_ids


