
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





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







						'''simple docstring'''



						UpperCamelCase      :  Union[str, Any]				   =					StableDiffusionSAGPipeline
						UpperCamelCase      :  Tuple				   =					TEXT_TO_IMAGE_PARAMS
						UpperCamelCase      :  Union[str, Any]				   =					TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase      :  Optional[Any]				   =					TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase      :  Optional[Any]				   =					TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase      :  Optional[int]				   =					False





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[Any]:
										torch.manual_seed(0     )
										lowercase         =      UNetaDConditionModel(
										    block_out_channels=(32, 64)		,    layers_per_block=2		,    sample_size=32		,    in_channels=4		,    out_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,    cross_attention_dim=32		,    )
										lowercase         =      DDIMScheduler(
										    beta_start=0.0_00_85		,    beta_end=0.0_12		,    beta_schedule="scaled_linear"		,    clip_sample=lowerCAmelCase__		,    set_alpha_to_one=lowerCAmelCase__		,    )
										torch.manual_seed(0     )
										lowercase         =      AutoencoderKL(
										    block_out_channels=[32, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,    latent_channels=4		,    )
										torch.manual_seed(0     )
										lowercase         =      CLIPTextConfig(
										    bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1E-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=1000		,    )
										lowercase         =      CLIPTextModel(lowerCAmelCase__     )
										lowercase         =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

										lowercase         =      {
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:Tuple=0     )  ->str:
										if str(lowerCAmelCase__     ).startswith("mps"     ):
														lowercase         =      torch.manual_seed(lowerCAmelCase__     )
										else:
														lowercase         =      torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )
										lowercase         =      {
										    "prompt": ".",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 1.0,
										    "sag_scale": 1.0,
										    "output_type": "numpy",
										}
										return inputs





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[int]:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3     )





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







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[str]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Optional[Any]:
										lowercase         =      StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"     )
										lowercase         =      sag_pipe.to(lowerCAmelCase__     )
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

										lowercase         =      "."
										lowercase         =      torch.manual_seed(0     )
										lowercase         =      sag_pipe(
										    [prompt]		,    generator=lowerCAmelCase__		,    guidance_scale=7.5		,    sag_scale=1.0		,    num_inference_steps=20		,    output_type="np"     )

										lowercase         =      output.images

										lowercase         =      image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										lowercase         =      np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]     )

										assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-2





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Union[str, Any]:
										lowercase         =      StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"     )
										lowercase         =      sag_pipe.to(lowerCAmelCase__     )
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

										lowercase         =      "."
										lowercase         =      torch.manual_seed(0     )
										lowercase         =      sag_pipe(
										    [prompt]		,    generator=lowerCAmelCase__		,    guidance_scale=7.5		,    sag_scale=1.0		,    num_inference_steps=20		,    output_type="np"     )

										lowercase         =      output.images

										lowercase         =      image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										lowercase         =      np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]     )

										assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-2





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Tuple:
										lowercase         =      StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"     )
										lowercase         =      sag_pipe.to(lowerCAmelCase__     )
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

										lowercase         =      "."
										lowercase         =      torch.manual_seed(0     )
										lowercase         =      sag_pipe(
										    [prompt]		,    width=768		,    height=512		,    generator=lowerCAmelCase__		,    guidance_scale=7.5		,    sag_scale=1.0		,    num_inference_steps=20		,    output_type="np"		,    )

										lowercase         =      output.images

										assert image.shape == (1, 512, 768, 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,)
