
'''simple docstring'''






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		KandinskyVaaPriorPipeline
    lowerCAmelCase_				    =		['prompt']
    lowerCAmelCase_				    =		['prompt', 'negative_prompt']
    lowerCAmelCase_				    =		[
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    lowerCAmelCase_				    =		False



    @property
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return 3_2



    @property
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          return 3_2



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return self.time_input_dim



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          return self.time_input_dim * 4



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return 1_0_0



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Tuple								=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )
          return tokenizer



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          torch.manual_seed(0     )
          _lowerCamelCase				:   Optional[int]								=   CLIPTextConfig(
              bos_token_id=0,eos_token_id=2,hidden_size=self.text_embedder_hidden_size,projection_dim=self.text_embedder_hidden_size,intermediate_size=3_7,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,)
          return CLIPTextModelWithProjection(__A     )



    @property
    def   lowerCamelCase_  (	self      :       Any     ):
          torch.manual_seed(0     )

          _lowerCamelCase				:   List[str]								=   {
              "num_attention_heads": 2,
              "attention_head_dim": 1_2,
              "embedding_dim": self.text_embedder_hidden_size,
              "num_layers": 1,
          }

          _lowerCamelCase				:   Any								=   PriorTransformer(**__A     )
          # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
          _lowerCamelCase				:   Optional[Any]								=   nn.Parameter(torch.ones(model.clip_std.shape     )     )
          return model



    @property
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          torch.manual_seed(0     )
          _lowerCamelCase				:   Optional[int]								=   CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size,image_size=2_2_4,projection_dim=self.text_embedder_hidden_size,intermediate_size=3_7,num_attention_heads=4,num_channels=3,num_hidden_layers=5,patch_size=1_4,)

          _lowerCamelCase				:   Any								=   CLIPVisionModelWithProjection(__A     )
          return model



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Any								=   CLIPImageProcessor(
              crop_size=2_2_4,do_center_crop=__A,do_normalize=__A,do_resize=__A,image_mean=[0.48145466, 0.4578275, 0.40821073],image_std=[0.26862954, 0.26130258, 0.27577711],resample=3,size=2_2_4,)

          return image_processor



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Optional[Any]								=   self.dummy_prior
          _lowerCamelCase				:   Any								=   self.dummy_image_encoder
          _lowerCamelCase				:   Any								=   self.dummy_text_encoder
          _lowerCamelCase				:   List[str]								=   self.dummy_tokenizer
          _lowerCamelCase				:   List[str]								=   self.dummy_image_processor

          _lowerCamelCase				:   Optional[Any]								=   UnCLIPScheduler(
              variance_type="fixed_small_log",prediction_type="sample",num_train_timesteps=1_0_0_0,clip_sample=__A,clip_sample_range=10.0,)

          _lowerCamelCase				:   Optional[Any]								=   {
              "prior": prior,
              "image_encoder": image_encoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "scheduler": scheduler,
              "image_processor": image_processor,
          }

          return components



    def   lowerCamelCase_  (	self      :       int,__A      :       List[Any],__A      :       Tuple=0     ):
          if str(__A     ).startswith("mps"     ):
                _lowerCamelCase				:   Any								=   torch.manual_seed(__A     )
          else:
                _lowerCamelCase				:   Optional[int]								=   torch.Generator(device=__A     ).manual_seed(__A     )
          _lowerCamelCase				:   int								=   {
              "prompt": "horse",
              "generator": generator,
              "guidance_scale": 4.0,
              "num_inference_steps": 2,
              "output_type": "np",
          }
          return inputs



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Tuple								=   "cpu"

          _lowerCamelCase				:   Any								=   self.get_dummy_components()

          _lowerCamelCase				:   int								=   self.pipeline_class(**__A     )
          _lowerCamelCase				:   str								=   pipe.to(__A     )

          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   int								=   pipe(**self.get_dummy_inputs(__A     )     )
          _lowerCamelCase				:   Any								=   output.image_embeds

          _lowerCamelCase				:   Optional[int]								=   pipe(
              **self.get_dummy_inputs(__A     ),return_dict=__A,)[0]

          _lowerCamelCase				:   Any								=   image[0, -1_0:]
          _lowerCamelCase				:   List[str]								=   image_from_tuple[0, -1_0:]

          assert image.shape == (1, 3_2)

          _lowerCamelCase				:   str								=   np.array(
              [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2



    @skip_mps
    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   Tuple								=   torch_device == "cpu"
          _lowerCamelCase				:   Optional[int]								=   True
          _lowerCamelCase				:   Dict								=   False

          self._test_inference_batch_single_identical(
              test_max_difference=__A,relax_max_difference=__A,test_mean_pixel_difference=__A,)



    @skip_mps
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   torch_device == "cpu"
          _lowerCamelCase				:   Any								=   False

          self._test_attention_slicing_forward_pass(
              test_max_difference=__A,test_mean_pixel_difference=__A,)

'''simple docstring'''






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)


UpperCAmelCase_       :   Any   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   List[str]   =       {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

UpperCAmelCase_       :   List[Any]   =       {'facebook/blenderbot-3B': 128}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		['input_ids', 'attention_mask']
    lowerCAmelCase_				    =		BlenderbotTokenizer



    def __init__(	self      :       Dict,__A      :       Optional[Any]=None,__A      :       List[str]=None,__A      :       Optional[Any]=None,__A      :       List[Any]="replace",__A      :       List[Any]="<s>",__A      :       str="</s>",__A      :       List[str]="</s>",__A      :       List[Any]="<s>",__A      :       Union[str, Any]="<unk>",__A      :       Optional[Any]="<pad>",__A      :       Dict="<mask>",__A      :       Any=False,__A      :       Tuple=True,**__A      :       Dict,):
          super().__init__(
              __A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)

          _lowerCamelCase				:   Tuple								=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
          if pre_tok_state.get("add_prefix_space",__A     ) != add_prefix_space:
                _lowerCamelCase				:   Optional[Any]								=   getattr(__A,pre_tok_state.pop("type"     )     )
                _lowerCamelCase				:   List[Any]								=   add_prefix_space
                _lowerCamelCase				:   Optional[Any]								=   pre_tok_class(**__A     )

          _lowerCamelCase				:   Any								=   add_prefix_space

          _lowerCamelCase				:   Any								=   "post_processor"
          _lowerCamelCase				:   Optional[Any]								=   getattr(self.backend_tokenizer,__A,__A     )
          if tokenizer_component_instance:
                _lowerCamelCase				:   Tuple								=   json.loads(tokenizer_component_instance.__getstate__()     )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                      _lowerCamelCase				:   List[Any]								=   tuple(state["sep"]     )
                if "cls" in state:
                      _lowerCamelCase				:   Optional[int]								=   tuple(state["cls"]     )

                _lowerCamelCase				:   List[str]								=   False

                if state.get("add_prefix_space",__A     ) != add_prefix_space:
                      _lowerCamelCase				:   List[str]								=   add_prefix_space
                      _lowerCamelCase				:   int								=   True

                if state.get("trim_offsets",__A     ) != trim_offsets:
                      _lowerCamelCase				:   List[str]								=   trim_offsets
                      _lowerCamelCase				:   Dict								=   True

                if changes_to_apply:
                      _lowerCamelCase				:   Tuple								=   getattr(__A,state.pop("type"     )     )
                      _lowerCamelCase				:   Union[str, Any]								=   component_class(**__A     )
                      setattr(self.backend_tokenizer,__A,__A     )



    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          if self._mask_token is None:
                if self.verbose:
                      logger.error("Using mask_token, but it is not set yet."     )
                return None
          return str(self._mask_token     )



    @mask_token.setter
    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       List[Any]     ):
          _lowerCamelCase				:   Any								=   AddedToken(__A,lstrip=__A,rstrip=__A     ) if isinstance(__A,__A     ) else value
          _lowerCamelCase				:   List[str]								=   value



    def   lowerCamelCase_  (	self      :       Optional[int],*__A      :       Optional[Any],**__A      :       List[str]     ):
          _lowerCamelCase				:   int								=   kwargs.get("is_split_into_words",__A     )
          assert self.add_prefix_space or not is_split_into_words, (
              f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       str,*__A      :       Union[str, Any],**__A      :       List[Any]     ):
          _lowerCamelCase				:   Optional[int]								=   kwargs.get("is_split_into_words",__A     )

          assert self.add_prefix_space or not is_split_into_words, (
              f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       str,__A      :       str,__A      :       Optional[str] = None     ):
          _lowerCamelCase				:   Optional[int]								=   self._tokenizer.model.save(__A,name=__A     )
          return tuple(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   int								=   [self.sep_token_id]
          _lowerCamelCase				:   str								=   [self.cls_token_id]

          if token_ids_a is None:
                return len(cls + token_ids_a + sep     ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]



    def   lowerCamelCase_  (	self      :       Dict,__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          return token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[str],__A      :       "Conversation"     ):
          _lowerCamelCase				:   Dict								=   []
          for is_user, text in conversation.iter_texts():
                if is_user:
                      # We need to space prefix as it's being done within blenderbot
                      inputs.append(" " + text     )
                else:
                      # Generated responses should contain them already.
                      inputs.append(__A     )

          _lowerCamelCase				:   List[Any]								=   "  ".join(__A     )
          _lowerCamelCase				:   List[str]								=   self.encode(__A     )
          if len(__A     ) > self.model_max_length:
                _lowerCamelCase				:   Tuple								=   input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'     )
          return input_ids