
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_UpperCAmelCase      :				Tuple          = False


class        lowercase      (	unittest.TestCase     ):
		pass






@nightly
@require_torch_gpu
class        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
				# remove text_unet
				pipe.remove_unused_weights()
				pipe.to(A_ )
				pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  'A painting of a squirrel eating a burger '
				UpperCamelCase						=  torch.manual_seed(0 )
				UpperCamelCase						=  pipe(
				    prompt=A_       ,   generator=A_       ,   guidance_scale=7.5       ,   num_inference_steps=2       ,   output_type='numpy' ).images

				with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(A_ )
						UpperCamelCase						=  VersatileDiffusionTextToImagePipeline.from_pretrained(A_ )
				pipe.to(A_ )
				pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  generator.manual_seed(0 )
				UpperCamelCase						=  pipe(
				    prompt=A_       ,   generator=A_       ,   guidance_scale=7.5       ,   num_inference_steps=2       ,   output_type='numpy' ).images

				assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  VersatileDiffusionTextToImagePipeline.from_pretrained(
				    'shi-labs/versatile-diffusion'       ,   torch_dtype=torch.floataa )
				pipe.to(A_ )
				pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  'A painting of a squirrel eating a burger '
				UpperCamelCase						=  torch.manual_seed(0 )
				UpperCamelCase						=  pipe(
				    prompt=A_       ,   generator=A_       ,   guidance_scale=7.5       ,   num_inference_steps=50       ,   output_type='numpy' ).images

				UpperCamelCase						=  image[0, 253:256, 253:256, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase						=  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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


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      :				List[str]          = None


_UpperCAmelCase      :				Any          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_UpperCAmelCase      :				List[str]          = {
    "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[int]          = {
    "camembert-base": 512,
}

_UpperCAmelCase      :				Union[str, Any]          = "▁"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : str			  =       VOCAB_FILES_NAMES
		__lowercase : Optional[Any]			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[str]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : List[str]			  =       ["input_ids", "attention_mask"]
		__lowercase : Tuple			  =       CamembertTokenizer



		def __init__(				self       ,   A_=None       ,   A_=None       ,   A_="<s>"       ,   A_="</s>"       ,   A_="</s>"       ,   A_="<s>"       ,   A_="<unk>"       ,   A_="<pad>"       ,   A_="<mask>"       ,   A_=["<s>NOTUSED", "</s>NOTUSED"]       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else mask_token

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

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



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""

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



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""
				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 + sep + token_ids_a + sep ) * [0]



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = 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(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

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

				return (out_vocab_file,)
