
"""simple docstring"""

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


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

UpperCamelCase      :							Dict          =					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase      :							Union[str, Any]          =					{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

UpperCamelCase      :							str          =					{
    "microsoft/deberta-base": 5_1_2,
    "microsoft/deberta-large": 5_1_2,
    "microsoft/deberta-xlarge": 5_1_2,
    "microsoft/deberta-base-mnli": 5_1_2,
    "microsoft/deberta-large-mnli": 5_1_2,
    "microsoft/deberta-xlarge-mnli": 5_1_2,
}

UpperCamelCase      :							Any          =					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		VOCAB_FILES_NAMES
     lowercase		  =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		  =		["input_ids", "attention_mask", "token_type_ids"]
     lowercase		  =		DebertaTokenizer





     def __init__(      self      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase="replace"      ,	__UpperCAmelCase="[CLS]"      ,	__UpperCAmelCase="[SEP]"      ,	__UpperCAmelCase="[SEP]"      ,	__UpperCAmelCase="[CLS]"      ,	__UpperCAmelCase="[UNK]"      ,	__UpperCAmelCase="[PAD]"      ,	__UpperCAmelCase="[MASK]"      ,	__UpperCAmelCase=False      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase      ,	__UpperCAmelCase      ,	tokenizer_file=__UpperCAmelCase      ,	errors=__UpperCAmelCase      ,	bos_token=__UpperCAmelCase      ,	eos_token=__UpperCAmelCase      ,	unk_token=__UpperCAmelCase      ,	sep_token=__UpperCAmelCase      ,	cls_token=__UpperCAmelCase      ,	pad_token=__UpperCAmelCase      ,	mask_token=__UpperCAmelCase      ,	add_prefix_space=__UpperCAmelCase      ,	**__UpperCAmelCase      ,	)
      __UpperCamelCase							    =			kwargs.pop('add_bos_token'      ,	__UpperCAmelCase )

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

      __UpperCamelCase							    =			add_prefix_space





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			AddedToken(__UpperCAmelCase      ,	lstrip=__UpperCAmelCase      ,	rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ) else value
      __UpperCamelCase							    =			value





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      __UpperCamelCase							    =			[self.cls_token_id]
      __UpperCamelCase							    =			[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      __UpperCamelCase							    =			[self.sep_token_id]
      __UpperCamelCase							    =			[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 ) * [0] + len(token_ids_a + sep ) * [1]





     def 		UpperCAmelCase   (      self      ,	*__UpperCAmelCase      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			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 		UpperCAmelCase   (      self      ,	*__UpperCAmelCase      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      __UpperCamelCase							    =			self._tokenizer.model.save(__UpperCAmelCase      ,	name=__UpperCAmelCase )
      return tuple(__UpperCAmelCase )







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			[]
      for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(__UpperCAmelCase      ,	add_special_tokens=__UpperCAmelCase ) + [self.eos_token_id] )

      if len(__UpperCAmelCase ) > self.model_max_length:
       __UpperCamelCase							    =			input_ids[-self.model_max_length :]
      return input_ids





"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,	)



