

"""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

A_							:			Optional[Any]											= logging.get_logger(__name__)


A_							:			List[str]											= {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A_							:			Union[str, Any]											= {
    "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"
    },
}

A_							:			Tuple											= {"facebook/blenderbot-3B": 128}





class    lowerCamelCase						(A__			):
   lowerCamelCase__   :		Tuple							=							VOCAB_FILES_NAMES
   lowerCamelCase__   :		List[str]							=							PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__   :		int							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__   :		Optional[Any]							=							['input_ids', 'attention_mask']
   lowerCamelCase__   :		str							=							BlenderbotTokenizer

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

      SCREAMING_SNAKE_CASE__								=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get("""add_prefix_space"""    ,				__UpperCAmelCase       ) != add_prefix_space:
         SCREAMING_SNAKE_CASE__								=      getattr(__UpperCAmelCase    ,				pre_tok_state.pop("""type"""       )       )
         SCREAMING_SNAKE_CASE__								=      add_prefix_space
         SCREAMING_SNAKE_CASE__								=      pre_tok_class(**__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      add_prefix_space

      SCREAMING_SNAKE_CASE__								=      """post_processor"""
      SCREAMING_SNAKE_CASE__								=      getattr(self.backend_tokenizer    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )
      if tokenizer_component_instance:
         SCREAMING_SNAKE_CASE__								=      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:
            SCREAMING_SNAKE_CASE__								=      tuple(state["""sep"""]       )
         if "cls" in state:
            SCREAMING_SNAKE_CASE__								=      tuple(state["""cls"""]       )

         SCREAMING_SNAKE_CASE__								=      False

         if state.get("""add_prefix_space"""    ,				__UpperCAmelCase       ) != add_prefix_space:
            SCREAMING_SNAKE_CASE__								=      add_prefix_space
            SCREAMING_SNAKE_CASE__								=      True

         if state.get("""trim_offsets"""    ,				__UpperCAmelCase       ) != trim_offsets:
            SCREAMING_SNAKE_CASE__								=      trim_offsets
            SCREAMING_SNAKE_CASE__								=      True

         if changes_to_apply:
            SCREAMING_SNAKE_CASE__								=      getattr(__UpperCAmelCase    ,				state.pop("""type"""       )       )
            SCREAMING_SNAKE_CASE__								=      component_class(**__UpperCAmelCase       )
            setattr(self.backend_tokenizer    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

   @property
   # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
   def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
      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 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    List[Any]:
      SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else value
      SCREAMING_SNAKE_CASE__								=      value

   def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				*__UpperCAmelCase				:						Tuple    ,				**__UpperCAmelCase				:						Tuple       )   ->    BatchEncoding:
      SCREAMING_SNAKE_CASE__								=      kwargs.get("""is_split_into_words"""    ,				__UpperCAmelCase       )
      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(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[Any]       )   ->    BatchEncoding:
      SCREAMING_SNAKE_CASE__								=      kwargs.get("""is_split_into_words"""    ,				__UpperCAmelCase       )

      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(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
      SCREAMING_SNAKE_CASE__								=      self._tokenizer.model.save(__UpperCAmelCase    ,				name=__UpperCAmelCase       )
      return tuple(__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
      SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
      SCREAMING_SNAKE_CASE__								=      [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 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    str:
      return token_ids_a + [self.eos_token_id]





   def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						"Conversation"       )   ->    List[int]:
      SCREAMING_SNAKE_CASE__								=      []
      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(__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      """  """.join(__UpperCAmelCase       )
      SCREAMING_SNAKE_CASE__								=      self.encode(__UpperCAmelCase       )
      if len(__UpperCAmelCase       ) > self.model_max_length:
         SCREAMING_SNAKE_CASE__								=      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





"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
						import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ort.SessionOptions()
						SCREAMING_SNAKE_CASE__								=      False
						return options





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""       )

						# using the PNDM scheduler by default
						SCREAMING_SNAKE_CASE__								=      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""    ,				revision="""onnx"""    ,				safety_checker=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      """A red cat sitting on a park bench"""

						SCREAMING_SNAKE_CASE__								=      np.random.RandomState(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(
						    prompt=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				strength=0.75    ,				guidance_scale=7.5    ,				num_inference_steps=1_5    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 1e-2


