





'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__lowerCamelCase						: Any		=   logging.get_logger(__name__)

__lowerCamelCase						: int		=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						: Optional[int]		=   {
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase						: List[str]		=   {
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


__lowerCamelCase						: int		=   {
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				PRETRAINED_INIT_CONFIGURATION
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]
  lowerCAmelCase_					=				DistilBertTokenizer


  def __init__(				self    :					List[Any]							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					str=True							,	UpperCamelCase__    :					Optional[int]="[UNK]"							,	UpperCamelCase__    :					Tuple="[SEP]"							,	UpperCamelCase__    :					List[str]="[PAD]"							,	UpperCamelCase__    :					Optional[int]="[CLS]"							,	UpperCamelCase__    :					Tuple="[MASK]"							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					List[str]=None							,	**UpperCamelCase__    :					Optional[Any]							,	)     ->						Dict:
    super().__init__(
        UpperCamelCase__							,	tokenizer_file=UpperCamelCase__							,	do_lower_case=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	tokenize_chinese_chars=UpperCamelCase__							,	strip_accents=UpperCamelCase__							,	**UpperCamelCase__							,	)

    _UpperCamelCase       			=json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
    if (
        normalizer_state.get('''lowercase'''							,	UpperCamelCase__  ) != do_lower_case
        or normalizer_state.get('''strip_accents'''							,	UpperCamelCase__  ) != strip_accents
        or normalizer_state.get('''handle_chinese_chars'''							,	UpperCamelCase__  ) != tokenize_chinese_chars
    ):
      _UpperCamelCase       			=getattr(UpperCamelCase__							,	normalizer_state.pop('''type'''  )  )
      _UpperCamelCase       			=do_lower_case
      _UpperCamelCase       			=strip_accents
      _UpperCamelCase       			=tokenize_chinese_chars
      _UpperCamelCase       			=normalizer_class(**UpperCamelCase__  )

    _UpperCamelCase       			=do_lower_case


  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Dict=None  )     ->						Optional[Any]:
    _UpperCamelCase       			=[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   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _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    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    _UpperCamelCase       			=self._tokenizer.model.save(UpperCamelCase__							,	name=UpperCamelCase__  )
    return tuple(UpperCamelCase__  )













'''simple docstring'''






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class 		UpperCAmelCase   :


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Union[str, Any]:
    torch.manual_seed(0  )
    _UpperCamelCase       			=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=UNetaDConditionModel(
        sample_size=32							,	layers_per_block=1							,	block_out_channels=[32, 64]							,	down_block_types=[
            '''ResnetDownsampleBlock2D''',
            '''SimpleCrossAttnDownBlock2D''',
        ]							,	mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,	up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,	in_channels=3							,	out_channels=6							,	cross_attention_dim=32							,	encoder_hid_dim=32							,	attention_head_dim=8							,	addition_embed_type='''text'''							,	addition_embed_type_num_heads=2							,	cross_attention_norm='''group_norm'''							,	resnet_time_scale_shift='''scale_shift'''							,	act_fn='''gelu'''							,	)
    unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    torch.manual_seed(0  )
    _UpperCamelCase       			=DDPMScheduler(
        num_train_timesteps=1000							,	beta_schedule='''squaredcos_cap_v2'''							,	beta_start=0.0001							,	beta_end=0.02							,	thresholding=UpperCamelCase__							,	dynamic_thresholding_ratio=0.95							,	sample_max_value=1.0							,	prediction_type='''epsilon'''							,	variance_type='''learned_range'''							,	)

    torch.manual_seed(0  )
    _UpperCamelCase       			=IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[int]:
    torch.manual_seed(0  )
    _UpperCamelCase       			=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=UNetaDConditionModel(
        sample_size=32							,	layers_per_block=[1, 2]							,	block_out_channels=[32, 64]							,	down_block_types=[
            '''ResnetDownsampleBlock2D''',
            '''SimpleCrossAttnDownBlock2D''',
        ]							,	mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,	up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,	in_channels=6							,	out_channels=6							,	cross_attention_dim=32							,	encoder_hid_dim=32							,	attention_head_dim=8							,	addition_embed_type='''text'''							,	addition_embed_type_num_heads=2							,	cross_attention_norm='''group_norm'''							,	resnet_time_scale_shift='''scale_shift'''							,	act_fn='''gelu'''							,	class_embed_type='''timestep'''							,	mid_block_scale_factor=1.414							,	time_embedding_act_fn='''gelu'''							,	time_embedding_dim=32							,	)
    unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    torch.manual_seed(0  )
    _UpperCamelCase       			=DDPMScheduler(
        num_train_timesteps=1000							,	beta_schedule='''squaredcos_cap_v2'''							,	beta_start=0.0001							,	beta_end=0.02							,	thresholding=UpperCamelCase__							,	dynamic_thresholding_ratio=0.95							,	sample_max_value=1.0							,	prediction_type='''epsilon'''							,	variance_type='''learned_range'''							,	)

    torch.manual_seed(0  )
    _UpperCamelCase       			=DDPMScheduler(
        num_train_timesteps=1000							,	beta_schedule='''squaredcos_cap_v2'''							,	beta_start=0.0001							,	beta_end=0.02							,	)

    torch.manual_seed(0  )
    _UpperCamelCase       			=IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "image_noising_scheduler": image_noising_scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }


  def   UpperCamelCase__      (				self    :					Any  )     ->						Any:
    _UpperCamelCase       			=self.get_dummy_components()
    _UpperCamelCase       			=self.pipeline_class(**UpperCamelCase__  )
    pipe.to(UpperCamelCase__  )
    pipe.set_progress_bar_config(disable=UpperCamelCase__  )

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )

    _UpperCamelCase       			=inputs['''prompt''']
    _UpperCamelCase       			=inputs['''generator''']
    _UpperCamelCase       			=inputs['''num_inference_steps''']
    _UpperCamelCase       			=inputs['''output_type''']

    if "image" in inputs:
      _UpperCamelCase       			=inputs['''image''']
    else:
      _UpperCamelCase       			=None

    if "mask_image" in inputs:
      _UpperCamelCase       			=inputs['''mask_image''']
    else:
      _UpperCamelCase       			=None

    if "original_image" in inputs:
      _UpperCamelCase       			=inputs['''original_image''']
    else:
      _UpperCamelCase       			=None

    _UpperCamelCase			,     _UpperCamelCase       			=pipe.encode_prompt(UpperCamelCase__  )

    # inputs with prompt converted to embeddings
    _UpperCamelCase       			={
        '''prompt_embeds''': prompt_embeds,
        '''negative_prompt_embeds''': negative_prompt_embeds,
        '''generator''': generator,
        '''num_inference_steps''': num_inference_steps,
        '''output_type''': output_type,
    }

    if image is not None:
      _UpperCamelCase       			=image

    if mask_image is not None:
      _UpperCamelCase       			=mask_image

    if original_image is not None:
      _UpperCamelCase       			=original_image

    # set all optional components to None
    for optional_component in pipe._optional_components:
      setattr(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )

    _UpperCamelCase       			=pipe(**UpperCamelCase__  )[0]

    with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(UpperCamelCase__  )
      _UpperCamelCase       			=self.pipeline_class.from_pretrained(UpperCamelCase__  )
      pipe_loaded.to(UpperCamelCase__  )
      pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__  )

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    for optional_component in pipe._optional_components:
      self.assertTrue(
          getattr(UpperCamelCase__							,	UpperCamelCase__  ) is None							,	F'''`{optional_component}` did not stay set to None after loading.'''							,	)

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )

    _UpperCamelCase       			=inputs['''generator''']
    _UpperCamelCase       			=inputs['''num_inference_steps''']
    _UpperCamelCase       			=inputs['''output_type''']

    # inputs with prompt converted to embeddings
    _UpperCamelCase       			={
        '''prompt_embeds''': prompt_embeds,
        '''negative_prompt_embeds''': negative_prompt_embeds,
        '''generator''': generator,
        '''num_inference_steps''': num_inference_steps,
        '''output_type''': output_type,
    }

    if image is not None:
      _UpperCamelCase       			=image

    if mask_image is not None:
      _UpperCamelCase       			=mask_image

    if original_image is not None:
      _UpperCamelCase       			=original_image

    _UpperCamelCase       			=pipe_loaded(**UpperCamelCase__  )[0]

    _UpperCamelCase       			=np.abs(to_np(UpperCamelCase__  ) - to_np(UpperCamelCase__  )  ).max()
    self.assertLess(UpperCamelCase__							,	1E-4  )




  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase       			=self.get_dummy_components()
    _UpperCamelCase       			=self.pipeline_class(**UpperCamelCase__  )
    pipe.to(UpperCamelCase__  )
    pipe.set_progress_bar_config(disable=UpperCamelCase__  )

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )
    _UpperCamelCase       			=pipe(**UpperCamelCase__  )[0]

    with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(UpperCamelCase__  )
      _UpperCamelCase       			=self.pipeline_class.from_pretrained(UpperCamelCase__  )
      pipe_loaded.to(UpperCamelCase__  )
      pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__  )

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )
    _UpperCamelCase       			=pipe_loaded(**UpperCamelCase__  )[0]

    _UpperCamelCase       			=np.abs(to_np(UpperCamelCase__  ) - to_np(UpperCamelCase__  )  ).max()
    self.assertLess(UpperCamelCase__							,	1E-4  )






