


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       __magic_name__	:


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[int]:





      '''simple docstring'''






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

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

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

      torch.manual_seed(0			)
      UpperCamelCase__					:    List[Any]          =   IFWatermarker()

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


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      str:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    str          =   TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''			)

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

      torch.manual_seed(0			)
      UpperCamelCase__					:    Optional[int]          =   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__					:    Optional[Any]          =   DDPMScheduler(
          num_train_timesteps=1000							,	beta_schedule='''squaredcos_cap_v2'''							,	beta_start=0.0001							,	beta_end=0.02							,	thresholding=lowerCamelCase__							,	dynamic_thresholding_ratio=0.95							,	sample_max_value=1.0							,	prediction_type='''epsilon'''							,	variance_type='''learned_range'''							,	)

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

      torch.manual_seed(0			)
      UpperCamelCase__					:    List[str]          =   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							:     Optional[Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.get_dummy_components()
      UpperCamelCase__					:    List[str]          =   self.pipeline_class(**lowerCamelCase__			)
      pipe.to(lowerCamelCase__			)
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)

      UpperCamelCase__					:    Any          =   self.get_dummy_inputs(lowerCamelCase__			)

      UpperCamelCase__					:    List[Any]          =   inputs['''prompt''']
      UpperCamelCase__					:    List[Any]          =   inputs['''generator''']
      UpperCamelCase__					:    str          =   inputs['''num_inference_steps''']
      UpperCamelCase__					:    Union[str, Any]          =   inputs['''output_type''']

      if "image" in inputs:
       UpperCamelCase__					:    Optional[Any]          =   inputs['''image''']
      else:
       UpperCamelCase__					:    int          =   None

      if "mask_image" in inputs:
       UpperCamelCase__					:    int          =   inputs['''mask_image''']
      else:
       UpperCamelCase__					:    Dict          =   None

      if "original_image" in inputs:
       UpperCamelCase__					:    int          =   inputs['''original_image''']
      else:
       UpperCamelCase__					:    Any          =   None

      UpperCamelCase__       ,				UpperCamelCase__					:    Optional[Any]          =   pipe.encode_prompt(lowerCamelCase__			)

      # inputs with prompt converted to embeddings
      UpperCamelCase__					:    Optional[int]          =   {
          '''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__					:    Tuple          =   image

      if mask_image is not None:
       UpperCamelCase__					:    Optional[Any]          =   mask_image

      if original_image is not None:
       UpperCamelCase__					:    Dict          =   original_image

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

      UpperCamelCase__					:    Optional[int]          =   pipe(**lowerCamelCase__			)[0]

      with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(lowerCamelCase__			)
       UpperCamelCase__					:    Optional[Any]          =   self.pipeline_class.from_pretrained(lowerCamelCase__			)
       pipe_loaded.to(lowerCamelCase__			)
       pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__			)

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

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

      UpperCamelCase__					:    Optional[Any]          =   self.get_dummy_inputs(lowerCamelCase__			)

      UpperCamelCase__					:    Optional[Any]          =   inputs['''generator''']
      UpperCamelCase__					:    int          =   inputs['''num_inference_steps''']
      UpperCamelCase__					:    Optional[int]          =   inputs['''output_type''']

      # inputs with prompt converted to embeddings
      UpperCamelCase__					:    List[str]          =   {
          '''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__					:    Any          =   image

      if mask_image is not None:
       UpperCamelCase__					:    List[str]          =   mask_image

      if original_image is not None:
       UpperCamelCase__					:    str          =   original_image

      UpperCamelCase__					:    str          =   pipe_loaded(**lowerCamelCase__			)[0]

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


     def      UpperCAmelCase__	(      self							:     str			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.get_dummy_components()
      UpperCamelCase__					:    List[str]          =   self.pipeline_class(**lowerCamelCase__			)
      pipe.to(lowerCamelCase__			)
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)

      UpperCamelCase__					:    Dict          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   pipe(**lowerCamelCase__			)[0]

      with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(lowerCamelCase__			)
       UpperCamelCase__					:    Union[str, Any]          =   self.pipeline_class.from_pretrained(lowerCamelCase__			)
       pipe_loaded.to(lowerCamelCase__			)
       pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__			)

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

      UpperCamelCase__					:    Tuple          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    Union[str, Any]          =   pipe_loaded(**lowerCamelCase__			)[0]

      UpperCamelCase__					:    List[Any]          =   np.abs(to_np(lowerCamelCase__			) - to_np(lowerCamelCase__			)			).max()
      self.assertLess(lowerCamelCase__							,	1E-4			)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


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

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [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      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    List[str]          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


