
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    DDIMPipeline
   __UpperCamelCase		:    Optional[int] 				=    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   __UpperCamelCase		:    Tuple 				=    PipelineTesterMixin.required_optional_params - {
       '''num_images_per_prompt''',
       '''latents''',
       '''callback''',
       '''callback_steps''',
   }
   __UpperCamelCase		:    Tuple 				=    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   __UpperCamelCase		:    Union[str, Any] 				=    False


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      torch.manual_seed(0     )
      _A:  Optional[Any]					=       UNetaDModel(
          block_out_channels=(3_2, 6_4)			,			layers_per_block=2			,			sample_size=3_2			,			in_channels=3			,			out_channels=3			,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')			,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')			,			)
      _A:  Union[str, Any]					=       DDIMScheduler()
      _A:  Optional[int]					=       {'''unet''': unet, '''scheduler''': scheduler}
      return components


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							int=0     ):

      """simple docstring"""
      if str(lowerCAmelCase_     ).startswith('''mps'''     ):
         _A:  Optional[int]					=       torch.manual_seed(lowerCAmelCase_     )
      else:
         _A:  Tuple					=       torch.Generator(device=lowerCAmelCase_     ).manual_seed(lowerCAmelCase_     )
      _A:  List[Any]					=       {
          '''batch_size''': 1,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }
      return inputs


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       '''cpu'''

      _A:  List[Any]					=       self.get_dummy_components()
      _A:  Any					=       self.pipeline_class(**lowerCAmelCase_     )
      pipe.to(lowerCAmelCase_     )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

      _A:  List[Any]					=       self.get_dummy_inputs(lowerCAmelCase_     )
      _A:  int					=       pipe(**lowerCAmelCase_     ).images
      _A:  Dict					=       image[0, -3:, -3:, -1]

      self.assertEqual(image.shape			,			(1, 3_2, 3_2, 3)     )
      _A:  Tuple					=       np.array(
          [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]     )
      _A:  Union[str, Any]					=       np.abs(image_slice.flatten() - expected_slice     ).max()
      self.assertLessEqual(lowerCAmelCase_			,			1e-3     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      super().test_save_load_local(expected_max_difference=3e-3     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      super().test_save_load_optional_components(expected_max_difference=3e-3     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






@slow
@require_torch_gpu
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  List[Any]					=       '''google/ddpm-cifar10-32'''

      _A:  List[str]					=       UNetaDModel.from_pretrained(lowerCAmelCase_     )
      _A:  List[str]					=       DDIMScheduler()

      _A:  Any					=       DDIMPipeline(unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_     )
      ddim.to(lowerCAmelCase_     )
      ddim.set_progress_bar_config(disable=lowerCAmelCase_     )

      _A:  Any					=       torch.manual_seed(0     )
      _A:  int					=       ddim(generator=lowerCAmelCase_			,			eta=0.0			,			output_type='''numpy'''     ).images

      _A:  Tuple					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      _A:  str					=       np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]     )

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


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  Optional[Any]					=       '''google/ddpm-ema-bedroom-256'''

      _A:  Optional[Any]					=       UNetaDModel.from_pretrained(lowerCAmelCase_     )
      _A:  Optional[Any]					=       DDIMScheduler.from_pretrained(lowerCAmelCase_     )

      _A:  Optional[Any]					=       DDIMPipeline(unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_     )
      ddpm.to(lowerCAmelCase_     )
      ddpm.set_progress_bar_config(disable=lowerCAmelCase_     )

      _A:  str					=       torch.manual_seed(0     )
      _A:  Optional[Any]					=       ddpm(generator=lowerCAmelCase_			,			output_type='''numpy'''     ).images

      _A:  Optional[Any]					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_5_6, 2_5_6, 3)
      _A:  Union[str, Any]					=       np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]     )

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





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


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

UpperCAmelCase__		:     Dict     			=      {'vocab_file': 'vocab.txt'}

UpperCAmelCase__		:     List[Any]     			=      {
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


UpperCAmelCase__		:     Dict     			=      {
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Any 				=    VOCAB_FILES_NAMES
   __UpperCamelCase		:    Tuple 				=    PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase		:    str 				=    PRETRAINED_INIT_CONFIGURATION
   __UpperCamelCase		:    int 				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase		:    int 				=    ConvBertTokenizer


   def __init__(					self     :							Tuple			,			lowerCAmelCase_     :							int=None			,			lowerCAmelCase_     :							List[Any]=None			,			lowerCAmelCase_     :							Optional[Any]=True			,			lowerCAmelCase_     :							List[Any]="[UNK]"			,			lowerCAmelCase_     :							Optional[Any]="[SEP]"			,			lowerCAmelCase_     :							Optional[Any]="[PAD]"			,			lowerCAmelCase_     :							List[Any]="[CLS]"			,			lowerCAmelCase_     :							Optional[int]="[MASK]"			,			lowerCAmelCase_     :							Any=True			,			lowerCAmelCase_     :							List[str]=None			,			**lowerCAmelCase_     :							List[Any]			,			):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_			,			tokenizer_file=lowerCAmelCase_			,			do_lower_case=lowerCAmelCase_			,			unk_token=lowerCAmelCase_			,			sep_token=lowerCAmelCase_			,			pad_token=lowerCAmelCase_			,			cls_token=lowerCAmelCase_			,			mask_token=lowerCAmelCase_			,			tokenize_chinese_chars=lowerCAmelCase_			,			strip_accents=lowerCAmelCase_			,			**lowerCAmelCase_			,			)

      _A:  List[Any]					=       json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
      if (
          normalizer_state.get('''lowercase'''			,			lowerCAmelCase_     ) != do_lower_case
          or normalizer_state.get('''strip_accents'''			,			lowerCAmelCase_     ) != strip_accents
          or normalizer_state.get('''handle_chinese_chars'''			,			lowerCAmelCase_     ) != tokenize_chinese_chars
      ):
         _A:  List[str]					=       getattr(lowerCAmelCase_			,			normalizer_state.pop('''type'''     )     )
         _A:  List[Any]					=       do_lower_case
         _A:  Optional[Any]					=       strip_accents
         _A:  Union[str, Any]					=       tokenize_chinese_chars
         _A:  Optional[int]					=       normalizer_class(**lowerCAmelCase_     )

      _A:  Optional[Any]					=       do_lower_case


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]=None     ):

      """simple docstring"""
      _A:  Dict					=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

      return output


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							List[int]			,			lowerCAmelCase_     :							Optional[List[int]] = None     ):

      """simple docstring"""
      _A:  Any					=       [self.sep_token_id]
      _A:  int					=       [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  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[str] = None     ):

      """simple docstring"""
      _A:  str					=       self._tokenizer.model.save(lowerCAmelCase_			,			name=lowerCAmelCase_     )
      return tuple(lowerCAmelCase_     )



