


"""simple docstring"""
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 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=StableDiffusionLDMaDPipeline
		lowerCamelCase__				=TEXT_TO_IMAGE_PARAMS
		lowerCamelCase__				=TEXT_TO_IMAGE_BATCH_PARAMS
		lowerCamelCase__				=TEXT_TO_IMAGE_IMAGE_PARAMS



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						int						=				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				,       )
			__snake_case	:						Tuple						=				DDIMScheduler(
			    beta_start=0.0_0085				,       beta_end=0.012				,       beta_schedule='''scaled_linear'''				,       clip_sample=UpperCamelCase_				,       set_alpha_to_one=UpperCamelCase_				,       )
			torch.manual_seed(0					)
			__snake_case	:						Union[str, Any]						=				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					)
			__snake_case	:						Tuple						=				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=10_00				,       )
			__snake_case	:						Optional[int]						=				CLIPTextModel(UpperCamelCase_					)
			__snake_case	:						str						=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

			__snake_case	:						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				,       a_				,       a_=0					):





			'''simple docstring'''

			if str(UpperCamelCase_					).startswith('''mps'''					):
				__snake_case	:						Dict						=				torch.manual_seed(UpperCamelCase_					)
			else:
				__snake_case	:						List[str]						=				torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
			__snake_case	:						List[str]						=				{
			    '''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					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

			__snake_case	:						List[Any]						=				self.get_dummy_components()
			__snake_case	:						Any						=				StableDiffusionLDMaDPipeline(**UpperCamelCase_					)
			__snake_case	:						str						=				ldmad_pipe.to(UpperCamelCase_					)
			ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

			__snake_case	:						int						=				self.get_dummy_inputs(UpperCamelCase_					)
			__snake_case	:						str						=				ldmad_pipe(**UpperCamelCase_					)
			__snake_case	:						Union[str, Any]						=				output.rgb, output.depth

			__snake_case	:						List[Any]						=				rgb[0, -3:, -3:, -1]
			__snake_case	:						Optional[int]						=				depth[0, -3:, -1]

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

			__snake_case	:						Any						=				np.array(
			    [0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262]					)
			__snake_case	:						Optional[int]						=				np.array([103.4_6727, 85.81_2004, 87.84_9236]					)

			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					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				self.get_dummy_components()
			__snake_case	:						Optional[Any]						=				StableDiffusionLDMaDPipeline(**UpperCamelCase_					)
			__snake_case	:						Dict						=				ldmad_pipe.to(UpperCamelCase_					)
			ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

			__snake_case	:						str						=				self.get_dummy_inputs(UpperCamelCase_					)
			__snake_case	:						Optional[Any]						=				3 * [inputs['''prompt''']]

			# forward
			__snake_case	:						int						=				ldmad_pipe(**UpperCamelCase_					)
			__snake_case	:						Tuple						=				output.rgb, output.depth
			__snake_case	:						Union[str, Any]						=				rgb_slice_a[0, -3:, -3:, -1]
			__snake_case	:						Union[str, Any]						=				depth_slice_a[0, -3:, -1]

			__snake_case	:						Dict						=				self.get_dummy_inputs(UpperCamelCase_					)
			__snake_case	:						Optional[Any]						=				3 * [inputs.pop('''prompt'''					)]

			__snake_case	:						str						=				ldmad_pipe.tokenizer(
			    UpperCamelCase_				,       padding='''max_length'''				,       max_length=ldmad_pipe.tokenizer.model_max_length				,       truncation=UpperCamelCase_				,       return_tensors='''pt'''				,       )
			__snake_case	:						Optional[Any]						=				text_inputs['''input_ids'''].to(UpperCamelCase_					)

			__snake_case	:						str						=				ldmad_pipe.text_encoder(UpperCamelCase_					)[0]

			__snake_case	:						Optional[int]						=				prompt_embeds

			# forward
			__snake_case	:						Tuple						=				ldmad_pipe(**UpperCamelCase_					)
			__snake_case	:						int						=				output.rgb, output.depth
			__snake_case	:						List[Any]						=				rgb_slice_a[0, -3:, -3:, -1]
			__snake_case	:						List[Any]						=				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					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
			__snake_case	:						List[str]						=				self.get_dummy_components()
			__snake_case	:						Tuple						=				PNDMScheduler(skip_prk_steps=UpperCamelCase_					)
			__snake_case	:						List[str]						=				StableDiffusionLDMaDPipeline(**UpperCamelCase_					)
			__snake_case	:						int						=				ldmad_pipe.to(UpperCamelCase_					)
			ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

			__snake_case	:						List[Any]						=				self.get_dummy_inputs(UpperCamelCase_					)
			__snake_case	:						Optional[int]						=				'''french fries'''
			__snake_case	:						List[Any]						=				ldmad_pipe(**UpperCamelCase_				,       negative_prompt=UpperCamelCase_					)

			__snake_case	:						List[Any]						=				output.rgb, output.depth
			__snake_case	:						Tuple						=				rgb[0, -3:, -3:, -1]
			__snake_case	:						Any						=				depth[0, -3:, -1]

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

			__snake_case	:						Union[str, Any]						=				np.array(
			    [0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217]					)
			__snake_case	:						List[str]						=				np.array([107.8_4738, 84.6_2802, 89.96_2135]					)
			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 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_="cpu"				,       a_=torch.floataa				,       a_=0					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
			__snake_case	:						int						=				np.random.RandomState(UpperCamelCase_					).standard_normal((1, 4, 64, 64)					)
			__snake_case	:						Any						=				torch.from_numpy(UpperCamelCase_					).to(device=UpperCamelCase_				,       dtype=UpperCamelCase_					)
			__snake_case	:						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					):





			'''simple docstring'''

			__snake_case	:						Dict						=				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''					)
			__snake_case	:						int						=				ldmad_pipe.to(UpperCamelCase_					)
			ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

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

			assert rgb.shape == (1, 5_12, 5_12, 3)
			assert depth.shape == (1, 5_12, 5_12)

			__snake_case	:						List[str]						=				np.array(
			    [0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706]					)
			__snake_case	:						Optional[int]						=				np.array(
			    [0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706]					)
			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 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_="cpu"				,       a_=torch.floataa				,       a_=0					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
			__snake_case	:						List[str]						=				np.random.RandomState(UpperCamelCase_					).standard_normal((1, 4, 64, 64)					)
			__snake_case	:						Any						=				torch.from_numpy(UpperCamelCase_					).to(device=UpperCamelCase_				,       dtype=UpperCamelCase_					)
			__snake_case	:						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					):





			'''simple docstring'''

			__snake_case	:						str						=				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''					).to(UpperCamelCase_					)
			ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

			__snake_case	:						Any						=				self.get_inputs(UpperCamelCase_					)
			__snake_case	:						Optional[int]						=				ldmad_pipe(**UpperCamelCase_					)
			__snake_case	:						Union[str, Any]						=				output.rgb, output.depth

			__snake_case	:						Union[str, Any]						=				0.49_5586
			__snake_case	:						List[str]						=				0.3379_5515
			__snake_case	:						Tuple						=				112.4_8518
			__snake_case	:						int						=				98.48_9746
			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					):





			'''simple docstring'''

			__snake_case	:						str						=				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''					).to(UpperCamelCase_					)
			ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

			__snake_case	:						Dict						=				self.get_inputs(UpperCamelCase_					)
			__snake_case	:						Any						=				ldmad_pipe(**UpperCamelCase_					)
			__snake_case	:						Dict						=				output.rgb, output.depth

			__snake_case	:						int						=				0.419_4127
			__snake_case	:						Optional[int]						=				0.3537_5586
			__snake_case	:						int						=				0.563_8502
			__snake_case	:						Optional[int]						=				0.3468_6103

			assert rgb.shape == (1, 5_12, 5_12, 3)
			assert depth.shape == (1, 5_12, 5_12, 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









'''simple docstring'''
from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__snake_case							      =     {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__snake_case							      =     {
    '''facebook/blenderbot_small-90M''': 512,
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				VOCAB_FILES_NAMES
		_a     =				PRETRAINED_VOCAB_FILES_MAP
		_a     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     =				BlenderbotSmallTokenizer




		def __init__(		self    ,   UpperCamelCase_=None    ,   UpperCamelCase_=None    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_=False    ,   UpperCamelCase_=True    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=UpperCamelCase_    ,   merges=UpperCamelCase_    ,   add_prefix_space=UpperCamelCase_    ,   trim_offsets=UpperCamelCase_    ,   )    ,   bos_token=UpperCamelCase_    ,   eos_token=UpperCamelCase_    ,   unk_token=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Union[str, Any]												= add_prefix_space




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= [self.sep_token_id]
							UpperCamelCase__   :Any												= [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]