
'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class     lowercase__						(	__a,       __a,       unittest.TestCase ):






   '''simple docstring'''







   _snake_case		 =				StableDiffusionXLImgaImgPipeline
   _snake_case		 =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   _snake_case		 =				PipelineTesterMixin.required_optional_params - {'''latents'''}
   _snake_case		 =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _snake_case		 =				IMAGE_TO_IMAGE_IMAGE_PARAMS
   _snake_case		 =				IMAGE_TO_IMAGE_IMAGE_PARAMS







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      torch.manual_seed(0 )
      UpperCamelCase	  =	UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)			, layers_per_block=2			, sample_size=3_2			, in_channels=4			, out_channels=4			, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			, attention_head_dim=(2, 4)			, use_linear_projection=lowerCAmelCase__			, addition_embed_type='''text_time'''			, addition_time_embed_dim=8			, transformer_layers_per_block=(1, 2)			, projection_class_embeddings_input_dim=8_0			, cross_attention_dim=6_4			, )
      UpperCamelCase	  =	EulerDiscreteScheduler(
          beta_start=0.00085			, beta_end=0.012			, steps_offset=1			, beta_schedule='''scaled_linear'''			, timestep_spacing='''leading'''			, )
      torch.manual_seed(0 )
      UpperCamelCase	  =	AutoencoderKL(
          block_out_channels=[3_2, 6_4]			, in_channels=3			, out_channels=3			, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			, latent_channels=4			, sample_size=1_2_8			, )
      torch.manual_seed(0 )
      UpperCamelCase	  =	CLIPTextConfig(
          bos_token_id=0			, eos_token_id=2			, hidden_size=3_2			, intermediate_size=3_7			, layer_norm_eps=1e-0_5			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1_0_0_0			, hidden_act='''gelu'''			, projection_dim=3_2			, )
      UpperCamelCase	  =	CLIPTextModel(lowerCAmelCase__ )
      UpperCamelCase	  =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			, local_files_only=lowerCAmelCase__ )

      UpperCamelCase	  =	CLIPTextModelWithProjection(lowerCAmelCase__ )
      UpperCamelCase	  =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			, local_files_only=lowerCAmelCase__ )

      UpperCamelCase	  =	{
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''text_encoder_2''': text_encoder_a,
          '''tokenizer_2''': tokenizer_a,
          # "safety_checker": None,
          # "feature_extractor": None,
      }
      return components







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

      '''simple docstring'''







      UpperCamelCase	  =	floats_tensor((1, 3, 3_2, 3_2)			, rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
      UpperCamelCase	  =	image / 2 + 0.5
      if str(lowerCAmelCase__ ).startswith('''mps''' ):
         UpperCamelCase	  =	torch.manual_seed(lowerCAmelCase__ )
      else:
         UpperCamelCase	  =	torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
      UpperCamelCase	  =	{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 5.0,
          '''output_type''': '''numpy''',
          '''strength''': 0.75,
      }
      return inputs







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
      UpperCamelCase	  =	self.get_dummy_components()
      UpperCamelCase	  =	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__ )
      UpperCamelCase	  =	sd_pipe.to(lowerCAmelCase__ )
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

      UpperCamelCase	  =	self.get_dummy_inputs(lowerCAmelCase__ )
      UpperCamelCase	  =	sd_pipe(**lowerCAmelCase__ ).images
      UpperCamelCase	  =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)

      UpperCamelCase	  =	np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )

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







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      super().test_inference_batch_single_identical(expected_max_diff=3e-3 )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      pass







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	self.get_dummy_components()
      UpperCamelCase	  =	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__ )
      UpperCamelCase	  =	sd_pipe.to(lowerCAmelCase__ )
      UpperCamelCase	  =	sd_pipe.to(lowerCAmelCase__ )
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

      # forward without prompt embeds
      UpperCamelCase	  =	self.get_dummy_inputs(lowerCAmelCase__ )
      UpperCamelCase	  =	3 * ['''this is a negative prompt''']
      UpperCamelCase	  =	negative_prompt
      UpperCamelCase	  =	3 * [inputs['''prompt''']]

      UpperCamelCase	  =	sd_pipe(**lowerCAmelCase__ )
      UpperCamelCase	  =	output.images[0, -3:, -3:, -1]

      # forward with prompt embeds
      UpperCamelCase	  =	self.get_dummy_inputs(lowerCAmelCase__ )
      UpperCamelCase	  =	3 * ['''this is a negative prompt''']
      UpperCamelCase	  =	3 * [inputs.pop('''prompt''' )]

      (
          (
          UpperCamelCase	
      )  ,			(
          UpperCamelCase	
      )  ,			(
          UpperCamelCase	
      )  ,			(
          UpperCamelCase	
      )  ,				
      )	  =	sd_pipe.encode_prompt(lowerCAmelCase__			, negative_prompt=lowerCAmelCase__ )

      UpperCamelCase	  =	sd_pipe(
          **lowerCAmelCase__			, prompt_embeds=lowerCAmelCase__			, negative_prompt_embeds=lowerCAmelCase__			, pooled_prompt_embeds=lowerCAmelCase__			, negative_pooled_prompt_embeds=lowerCAmelCase__			, )
      UpperCamelCase	  =	output.images[0, -3:, -3:, -1]

      # make sure that it's equal
      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4




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






   '''simple docstring'''







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







   def    UpperCAmelCase					(      self			, lowerCamelCase__			, lowerCamelCase__="cpu"			, lowerCamelCase__=torch.floataa			, lowerCamelCase__=0 ):

      '''simple docstring'''







      UpperCamelCase	  =	torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
      UpperCamelCase	  =	np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 6_4, 6_4) )
      UpperCamelCase	  =	torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__			, dtype=lowerCAmelCase__ )
      UpperCamelCase	  =	{
          '''prompt''': '''a photograph of an astronaut riding a horse''',
          '''latents''': latents,
          '''generator''': generator,
          '''num_inference_steps''': 3,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
      pipe.to(lowerCAmelCase__ )
      pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

      UpperCamelCase	  =	self.get_inputs(lowerCAmelCase__ )
      UpperCamelCase	  =	pipe(**lowerCAmelCase__ ).images
      UpperCamelCase	  =	image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      UpperCamelCase	  =	np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506] )
      assert np.abs(image_slice - expected_slice ).max() < 7e-3







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_barthez import BarthezTokenizer
else:
						_snake_case      :							Any     	=    None

_snake_case      :							Union[str, Any]     	=    logging.get_logger(__name__)

_snake_case      :							Optional[Any]     	=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case      :							int     	=    {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case      :							str     	=    {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

_snake_case      :							Optional[int]     	=    "▁"





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Dict				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					BarthezTokenizer





						def __init__(			self							:str		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:Union[str, Any]="<s>"		,    lowerCAmelCase__							:Optional[Any]="</s>"		,    lowerCAmelCase__							:str="</s>"		,    lowerCAmelCase__							:List[str]="<s>"		,    lowerCAmelCase__							:Dict="<unk>"		,    lowerCAmelCase__							:int="<pad>"		,    lowerCAmelCase__							:Optional[Any]="<mask>"		,    **lowerCAmelCase__							:Optional[int]		,    )  ->int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase         =      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__		,    unk_token=lowerCAmelCase__		,    sep_token=lowerCAmelCase__		,    cls_token=lowerCAmelCase__		,    pad_token=lowerCAmelCase__		,    mask_token=lowerCAmelCase__		,    **lowerCAmelCase__		,    )

										lowercase         =      vocab_file
										lowercase         =      False if not self.vocab_file else True





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:

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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [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]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Tuple[str]:
										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
										lowercase         =      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,)
