

import gc
import tempfile
import unittest

import numpy as np
import torch

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


a_    :						int    		=      False




class      UpperCamelCase (     unittest.TestCase   ):
    pass




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

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'							,    torch_dtype=torch.floataa    )
     pipe.to(snake_case__    )
     pipe.set_progress_bar_config(disable=snake_case__    )

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

     SCREAMING_SNAKE_CASE								=			torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			pipe.dual_guided(
         prompt='first prompt'							,    image=snake_case__							,    text_to_image_strength=0.75							,    generator=snake_case__							,    guidance_scale=7.5							,    num_inference_steps=2							,    output_type='numpy'							,    ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
      pipe.save_pretrained(snake_case__    )
      SCREAMING_SNAKE_CASE								=			VersatileDiffusionPipeline.from_pretrained(snake_case__							,    torch_dtype=torch.floataa    )
     pipe.to(snake_case__    )
     pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			generator.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			pipe.dual_guided(
         prompt='first prompt'							,    image=snake_case__							,    text_to_image_strength=0.75							,    generator=snake_case__							,    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 :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'							,    torch_dtype=torch.floataa    )
     pipe.to(snake_case__    )
     pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			'cyberpunk 2077'
     SCREAMING_SNAKE_CASE								=			load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
     SCREAMING_SNAKE_CASE								=			torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			pipe.dual_guided(
         prompt=snake_case__							,    image=snake_case__							,    text_to_image_strength=0.75							,    generator=snake_case__							,    guidance_scale=7.5							,    num_inference_steps=5_0							,    output_type='numpy'							,    ).images

     SCREAMING_SNAKE_CASE								=			image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE								=			np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]    )

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

     SCREAMING_SNAKE_CASE								=			'A painting of a squirrel eating a burger '
     SCREAMING_SNAKE_CASE								=			torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			pipe.text_to_image(
         prompt=snake_case__							,    generator=snake_case__							,    guidance_scale=7.5							,    num_inference_steps=5_0							,    output_type='numpy'    ).images

     SCREAMING_SNAKE_CASE								=			image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE								=			np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]    )

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

     SCREAMING_SNAKE_CASE								=			pipe.image_variation(snake_case__							,    generator=snake_case__							,    output_type='numpy'    ).images

     SCREAMING_SNAKE_CASE								=			image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE								=			np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]    )

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




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


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

a_    :						Optional[Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

a_    :						Union[str, Any]    		=      {"allegro/herbert-base-cased": 514}
a_    :						List[Any]    		=      {}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =HerbertTokenizer

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

     """simple docstring"""






     super().__init__(
         snake_case__							,    snake_case__							,    tokenizer_file=snake_case__							,    cls_token=snake_case__							,    unk_token=snake_case__							,    pad_token=snake_case__							,    mask_token=snake_case__							,    sep_token=snake_case__							,    **snake_case__							,    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






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

     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None							,    snake_case__ :      bool = False    ):

     """simple docstring"""






     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=snake_case__							,    token_ids_a=snake_case__							,    already_has_special_tokens=snake_case__    )

     if token_ids_a is None:
      return [1] + ([0] * len(snake_case__    )) + [1]
     return [1] + ([0] * len(snake_case__    )) + [1] + ([0] * len(snake_case__    )) + [1]

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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    ) * [0] + len(token_ids_a + sep    ) * [1]




    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

