




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


__lowerCAmelCase			=							False





class   __a      (   unittest.TestCase				):
 pass





@nightly
@require_torch_gpu
class   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       List[Any]        =       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,			torch_dtype=torch.floataa			)
        pipe.to(_UpperCAmelCase			)
        pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

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

        lowercase__:       Union[str, Any]        =       torch.manual_seed(0			)
        lowercase__:       Optional[int]        =       pipe.dual_guided(
            prompt='first prompt'    ,			image=_UpperCAmelCase    ,			text_to_image_strength=0.7_5    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='numpy'    ,			).images

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

        lowercase__:       Tuple        =       generator.manual_seed(0			)
        lowercase__:       Any        =       pipe.dual_guided(
            prompt='first prompt'    ,			image=_UpperCAmelCase    ,			text_to_image_strength=0.7_5    ,			generator=_UpperCAmelCase    ,			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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,			torch_dtype=torch.floataa			)
        pipe.to(_UpperCAmelCase			)
        pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

        lowercase__:       int        =       'cyberpunk 2077'
        lowercase__:       str        =       load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'			)
        lowercase__:       List[Any]        =       torch.manual_seed(0			)
        lowercase__:       Union[str, Any]        =       pipe.dual_guided(
            prompt=_UpperCAmelCase    ,			image=_UpperCAmelCase    ,			text_to_image_strength=0.7_5    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='numpy'    ,			).images

        lowercase__:       Optional[Any]        =       image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__:       Any        =       np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]			)

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

        lowercase__:       List[str]        =       'A painting of a squirrel eating a burger '
        lowercase__:       int        =       torch.manual_seed(0			)
        lowercase__:       List[str]        =       pipe.text_to_image(
            prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='numpy'			).images

        lowercase__:       str        =       image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__:       Optional[int]        =       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]			)

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

        lowercase__:       Union[str, Any]        =       pipe.image_variation(_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			output_type='numpy'			).images

        lowercase__:       Union[str, Any]        =       image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__:       Tuple        =       np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]			)

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






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class   __a      (   tf.keras.layers.Layer				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None			) ->    int:




        '''simple docstring'''
        super().__init__()
        lowercase__:       Union[str, Any]        =       pad_token_id
        lowercase__:       List[str]        =       max_length
        lowercase__:       int        =       vocab
        lowercase__:       List[Any]        =       merges
        lowercase__:       str        =       BytePairTokenizer(lowerCAmelCase__    ,			lowerCAmelCase__    ,			sequence_length=lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Tuple        =       [' '.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
        lowercase__:       List[Any]        =       tokenizer.get_vocab()
        return cls(lowerCAmelCase__    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       GPTaTokenizer.from_pretrained(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
        return cls.from_tokenizer(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        return cls(**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return {
            "vocab": self.vocab,
            "merges": self.merges,
            "max_length": self.max_length,
            "pad_token_id": self.pad_token_id,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.tf_tokenizer(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       tf.ones_like(lowerCAmelCase__			)

        if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__:       int        =       max_length if max_length is not None else self.max_length

               if max_length is not None:
                      lowercase__      ,						lowercase__:       List[Any]        =       pad_model_inputs(
                          lowerCAmelCase__    ,			max_seq_length=lowerCAmelCase__    ,			pad_value=self.pad_token_id			)

        return {"attention_mask": attention_mask, "input_ids": input_ids}


