


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()





class   __a      (   unittest.TestCase				):
 __lowercase			:							Union[str, Any]       					=			StableDiffusionLDMaDPipeline
 __lowercase			:							Optional[Any]       					=			TEXT_TO_IMAGE_PARAMS
 __lowercase			:							int       					=			TEXT_TO_IMAGE_BATCH_PARAMS
 __lowercase			:							Optional[Any]       					=			TEXT_TO_IMAGE_IMAGE_PARAMS
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       str        =       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__:       Tuple        =       DDIMScheduler(
            beta_start=0.0_0_0_8_5    ,			beta_end=0.0_1_2    ,			beta_schedule='scaled_linear'    ,			clip_sample=lowerCAmelCase__    ,			set_alpha_to_one=lowerCAmelCase__    ,			)
        torch.manual_seed(0			)
        lowercase__:       str        =       AutoencoderKL(
            block_out_channels=[32, 64]    ,			in_channels=6    ,			out_channels=6    ,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,			latent_channels=4    ,			)
        torch.manual_seed(0			)
        lowercase__:       Any        =       CLIPTextConfig(
            bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=32    ,			intermediate_size=37    ,			layer_norm_eps=1E-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=1_000    ,			)
        lowercase__:       Dict        =       CLIPTextModel(lowerCAmelCase__			)
        lowercase__:       Any        =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

        lowercase__:       Any        =       {
            '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    ,			lowerCAmelCase__    ,			lowerCAmelCase__=0			) ->    Optional[Any]:




        '''simple docstring'''
        if str(lowerCAmelCase__			).startswith('mps'			):
               lowercase__:       str        =       torch.manual_seed(lowerCAmelCase__			)
        else:
               lowercase__:       Optional[Any]        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       {
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       'cpu'  # ensure determinism for the device-dependent torch.Generator

        lowercase__:       Optional[int]        =       self.get_dummy_components()
        lowercase__:       Tuple        =       StableDiffusionLDMaDPipeline(**lowerCAmelCase__			)
        lowercase__:       List[Any]        =       ldmad_pipe.to(lowerCAmelCase__			)
        ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       List[Any]        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       ldmad_pipe(**lowerCAmelCase__			)
        lowercase__      ,						lowercase__:       int        =       output.rgb, output.depth

        lowercase__:       List[str]        =       rgb[0, -3:, -3:, -1]
        lowercase__:       Optional[Any]        =       depth[0, -3:, -1]

        assert rgb.shape == (1, 64, 64, 3)
        assert depth.shape == (1, 64, 64)

        lowercase__:       Optional[int]        =       np.array(
            [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]			)
        lowercase__:       int        =       np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]			)

        assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb			).max() < 1E-2
        assert np.abs(image_slice_depth.flatten() - expected_slice_depth			).max() < 1E-2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       self.get_dummy_components()
        lowercase__:       Any        =       StableDiffusionLDMaDPipeline(**lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       ldmad_pipe.to(lowerCAmelCase__			)
        ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       List[str]        =       3 * [inputs['prompt']]

        # forward
        lowercase__:       List[Any]        =       ldmad_pipe(**lowerCAmelCase__			)
        lowercase__      ,						lowercase__:       Optional[int]        =       output.rgb, output.depth
        lowercase__:       Union[str, Any]        =       rgb_slice_a[0, -3:, -3:, -1]
        lowercase__:       List[str]        =       depth_slice_a[0, -3:, -1]

        lowercase__:       str        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       str        =       3 * [inputs.pop('prompt'			)]

        lowercase__:       Dict        =       ldmad_pipe.tokenizer(
            lowerCAmelCase__    ,			padding='max_length'    ,			max_length=ldmad_pipe.tokenizer.model_max_length    ,			truncation=lowerCAmelCase__    ,			return_tensors='pt'    ,			)
        lowercase__:       int        =       text_inputs['input_ids'].to(lowerCAmelCase__			)

        lowercase__:       Union[str, Any]        =       ldmad_pipe.text_encoder(lowerCAmelCase__			)[0]

        lowercase__:       Dict        =       prompt_embeds

        # forward
        lowercase__:       Dict        =       ldmad_pipe(**lowerCAmelCase__			)
        lowercase__      ,						lowercase__:       Any        =       output.rgb, output.depth
        lowercase__:       Optional[Any]        =       rgb_slice_a[0, -3:, -3:, -1]
        lowercase__:       Tuple        =       depth_slice_a[0, -3:, -1]

        assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()			).max() < 1E-4
        assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()			).max() < 1E-4
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Dict        =       'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase__:       str        =       self.get_dummy_components()
        lowercase__:       Any        =       PNDMScheduler(skip_prk_steps=lowerCAmelCase__			)
        lowercase__:       List[str]        =       StableDiffusionLDMaDPipeline(**lowerCAmelCase__			)
        lowercase__:       Any        =       ldmad_pipe.to(lowerCAmelCase__			)
        ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Tuple        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       List[str]        =       'french fries'
        lowercase__:       Tuple        =       ldmad_pipe(**lowerCAmelCase__    ,			negative_prompt=lowerCAmelCase__			)

        lowercase__      ,						lowercase__:       Union[str, Any]        =       output.rgb, output.depth
        lowercase__:       str        =       rgb[0, -3:, -3:, -1]
        lowercase__:       Union[str, Any]        =       depth[0, -3:, -1]

        assert rgb.shape == (1, 64, 64, 3)
        assert depth.shape == (1, 64, 64)

        lowercase__:       Union[str, Any]        =       np.array(
            [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]			)
        lowercase__:       List[Any]        =       np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]			)
        assert np.abs(rgb_slice.flatten() - expected_slice_rgb			).max() < 1E-2
        assert np.abs(depth_slice.flatten() - expected_slice_depth			).max() < 1E-2





@slow
@require_torch_gpu
class   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__="cpu"    ,			lowerCAmelCase__=torch.floataa    ,			lowerCAmelCase__=0			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[str]        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
        lowercase__:       int        =       np.random.RandomState(lowerCAmelCase__			).standard_normal((1, 4, 64, 64)			)
        lowercase__:       List[str]        =       torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__    ,			dtype=lowerCAmelCase__			)
        lowercase__:       Optional[Any]        =       {
            'prompt': 'a photograph of an astronaut riding a horse',
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'			)
        lowercase__:       str        =       ldmad_pipe.to(lowerCAmelCase__			)
        ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Dict        =       self.get_inputs(lowerCAmelCase__			)
        lowercase__:       Tuple        =       ldmad_pipe(**lowerCAmelCase__			)
        lowercase__      ,						lowercase__:       List[str]        =       output.rgb, output.depth
        lowercase__:       Optional[Any]        =       rgb[0, -3:, -3:, -1].flatten()
        lowercase__:       List[str]        =       rgb[0, -3:, -1].flatten()

        assert rgb.shape == (1, 512, 512, 3)
        assert depth.shape == (1, 512, 512)

        lowercase__:       Any        =       np.array(
            [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]			)
        lowercase__:       Optional[int]        =       np.array(
            [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]			)
        assert np.abs(rgb_slice - expected_slice_rgb			).max() < 3E-3
        assert np.abs(depth_slice - expected_slice_depth			).max() < 3E-3





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




        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__="cpu"    ,			lowerCAmelCase__=torch.floataa    ,			lowerCAmelCase__=0			) ->    Any:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       np.random.RandomState(lowerCAmelCase__			).standard_normal((1, 4, 64, 64)			)
        lowercase__:       Any        =       torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__    ,			dtype=lowerCAmelCase__			)
        lowercase__:       List[str]        =       {
            'prompt': 'a photograph of an astronaut riding a horse',
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 50,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       int        =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'			).to(lowerCAmelCase__			)
        ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       List[Any]        =       self.get_inputs(lowerCAmelCase__			)
        lowercase__:       Dict        =       ldmad_pipe(**lowerCAmelCase__			)
        lowercase__      ,						lowercase__:       Tuple        =       output.rgb, output.depth

        lowercase__:       Optional[int]        =       0.4_9_5_5_8_6
        lowercase__:       List[Any]        =       0.3_3_7_9_5_5_1_5
        lowercase__:       Dict        =       1_1_2.4_8_5_1_8
        lowercase__:       str        =       9_8.4_8_9_7_4_6
        assert np.abs(expected_rgb_mean - rgb.mean()			) < 1E-3
        assert np.abs(expected_rgb_std - rgb.std()			) < 1E-3
        assert np.abs(expected_depth_mean - depth.mean()			) < 1E-3
        assert np.abs(expected_depth_std - depth.std()			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'			).to(lowerCAmelCase__			)
        ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Any        =       self.get_inputs(lowerCAmelCase__			)
        lowercase__:       Tuple        =       ldmad_pipe(**lowerCAmelCase__			)
        lowercase__      ,						lowercase__:       Optional[Any]        =       output.rgb, output.depth

        lowercase__:       Tuple        =       0.4_1_9_4_1_2_7
        lowercase__:       Any        =       0.3_5_3_7_5_5_8_6
        lowercase__:       str        =       0.5_6_3_8_5_0_2
        lowercase__:       Any        =       0.3_4_6_8_6_1_0_3

        assert rgb.shape == (1, 512, 512, 3)
        assert depth.shape == (1, 512, 512, 1)
        assert np.abs(expected_rgb_mean - rgb.mean()			) < 1E-3
        assert np.abs(expected_rgb_std - rgb.std()			) < 1E-3
        assert np.abs(expected_depth_mean - depth.mean()			) < 1E-3
        assert np.abs(expected_depth_std - depth.std()			) < 1E-3






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}


