
'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


A							     = False



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



  '''simple docstring'''






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

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Dict:
        snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"     )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCAmelCase_     )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

        snake_case_    							=				load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"     )

        snake_case_    							=				torch.manual_seed(0     )
        snake_case_    							=				pipe(
            prompt="first prompt"						,			image=lowerCAmelCase_						,			text_to_image_strength=0.75						,			generator=lowerCAmelCase_						,			guidance_scale=7.5						,			num_inference_steps=2						,			output_type="numpy"						,			).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowerCAmelCase_     )
              snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCAmelCase_     )

        pipe.to(lowerCAmelCase_     )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

        snake_case_    							=				generator.manual_seed(0     )
        snake_case_    							=				pipe(
            prompt="first prompt"						,			image=lowerCAmelCase_						,			text_to_image_strength=0.75						,			generator=lowerCAmelCase_						,			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 		_lowerCamelCase						(self	:			str     ) ->							Optional[int]:
        snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"     )
        pipe.remove_unused_weights()
        pipe.to(lowerCAmelCase_     )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

        snake_case_    							=				"cyberpunk 2077"
        snake_case_    							=				load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"     )
        snake_case_    							=				torch.manual_seed(0     )
        snake_case_    							=				pipe(
            prompt=lowerCAmelCase_						,			image=lowerCAmelCase_						,			text_to_image_strength=0.75						,			generator=lowerCAmelCase_						,			guidance_scale=7.5						,			num_inference_steps=50						,			output_type="numpy"						,			).images

        snake_case_    							=				image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case_    							=				np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]     )

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







"""simple docstring"""

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

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


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		_lowerCamelCase	:      Union[str, Any]   =							None


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

_lowerCamelCase	:      Tuple   =							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase	:      Union[str, Any]   =							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase	:      Dict   =							{
    'facebook/xglm-564M': 2048,
}

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Optional[int]								= VOCAB_FILES_NAMES
				__lowerCAmelCase     :     Tuple								= PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase     :     List[Any]								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase     :     Union[str, Any]								= ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase     :     List[str]								= XGLMTokenizer







				def __init__(				self :       str   ,  _lowerCamelCase :       int=None   ,  _lowerCamelCase :       Any=None   ,  _lowerCamelCase :       List[str]="<s>"   ,  _lowerCamelCase :       int="</s>"   ,  _lowerCamelCase :       Any="</s>"   ,  _lowerCamelCase :       Optional[int]="<s>"   ,  _lowerCamelCase :       List[Any]="<unk>"   ,  _lowerCamelCase :       Optional[int]="<pad>"   ,  **_lowerCamelCase :       List[Any]   ,  ):



					"""simple docstring"""

					A_	:					Tuple						 =		7
					A_	:					Dict						 =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

					A_	:					str						 =		kwargs.get('''additional_special_tokens'''   ,  []							)
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    _lowerCamelCase   ,  tokenizer_file=_lowerCamelCase   ,  bos_token=_lowerCamelCase   ,  eos_token=_lowerCamelCase   ,  sep_token=_lowerCamelCase   ,  cls_token=_lowerCamelCase   ,  unk_token=_lowerCamelCase   ,  pad_token=_lowerCamelCase   ,  **_lowerCamelCase   ,  )

					A_	:					Tuple						 =		vocab_file
					A_	:					str						 =		False if not self.vocab_file else True







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
					A_	:					List[Any]						 =		[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					A_	:					Dict						 =		[self.sep_token_id]

					if token_ids_a is None:
						return len(sep + token_ids_a							) * [0]
					return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[str] = None							):



					"""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
					A_	:					Tuple						 =		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,)





