






import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:List[str]		      =    LDMTextToImagePipeline
							UpperCamelCase_		:List[Any]		      =    TEXT_TO_IMAGE_PARAMS - {
							    'negative_prompt',
							    'negative_prompt_embeds',
							    'cross_attention_kwargs',
							    'prompt_embeds',
							}
							UpperCamelCase_		:Tuple		      =    PipelineTesterMixin.required_optional_params - {
							    'num_images_per_prompt',
							    'callback',
							    'callback_steps',
							}
							UpperCamelCase_		:Union[str, Any]		      =    TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCamelCase_		:int		      =    False




							def 						__snake_case					(   self     :   Tuple	):
											torch.manual_seed(0	)
											lowerCAmelCase__      			=						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 ,							)
											lowerCAmelCase__      			=						DDIMScheduler(
											    beta_start=0.00_085 ,							beta_end=0.012 ,							beta_schedule='''scaled_linear''' ,							clip_sample=SCREAMING_SNAKE_CASE_ ,							set_alpha_to_one=SCREAMING_SNAKE_CASE_ ,							)
											torch.manual_seed(0	)
											lowerCAmelCase__      			=						AutoencoderKL(
											    block_out_channels=(32, 64) ,							in_channels=3 ,							out_channels=3 ,							down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''') ,							up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''') ,							latent_channels=4 ,							)
											torch.manual_seed(0	)
											lowerCAmelCase__      			=						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 ,							)
											lowerCAmelCase__      			=						CLIPTextModel(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

											lowerCAmelCase__      			=						{
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vqvae''': vae,
											    '''bert''': text_encoder,
											    '''tokenizer''': tokenizer,
											}
											return components




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int=0	):
											if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
															lowerCAmelCase__      			=						torch.manual_seed(SCREAMING_SNAKE_CASE_	)
											else:
															lowerCAmelCase__      			=						torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{
											    '''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 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

											lowerCAmelCase__      			=						self.get_dummy_components()
											lowerCAmelCase__      			=						LDMTextToImagePipeline(**SCREAMING_SNAKE_CASE_	)
											pipe.to(SCREAMING_SNAKE_CASE_	)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						pipe(**SCREAMING_SNAKE_CASE_	).images
											lowerCAmelCase__      			=						image[0, -3:, -3:, -1]

											assert image.shape == (1, 16, 16, 3)
											lowerCAmelCase__      			=						np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



@slow
@require_torch_gpu
class  lowerCAmelCase_       (				unittest.TestCase					):




							def 						__snake_case					(   self     :   str	):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Dict=torch.floataa ,							SCREAMING_SNAKE_CASE_     :   int=0	):
											lowerCAmelCase__      			=						torch.manual_seed(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						np.random.RandomState(SCREAMING_SNAKE_CASE_	).standard_normal((1, 4, 32, 32)	)
											lowerCAmelCase__      			=						torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_ ,							dtype=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''	).to(SCREAMING_SNAKE_CASE_	)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						self.get_inputs(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						pipe(**SCREAMING_SNAKE_CASE_	).images
											lowerCAmelCase__      			=						image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 256, 256, 3)
											lowerCAmelCase__      			=						np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]	)
											lowerCAmelCase__      			=						np.abs(expected_slice - image_slice	).max()
											assert max_diff < 1e-3



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




							def 						__snake_case					(   self     :   Any	):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Tuple=torch.floataa ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=0	):
											lowerCAmelCase__      			=						torch.manual_seed(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						np.random.RandomState(SCREAMING_SNAKE_CASE_	).standard_normal((1, 4, 32, 32)	)
											lowerCAmelCase__      			=						torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_ ,							dtype=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 50,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''	).to(SCREAMING_SNAKE_CASE_	)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						self.get_inputs(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						pipe(**SCREAMING_SNAKE_CASE_	).images[0]

											lowerCAmelCase__      			=						load_numpy(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''	)
											lowerCAmelCase__      			=						np.abs(expected_image - image	).max()
											assert max_diff < 1e-3













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase			:		int   				=    {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GitForCausalLM",
			    "GitModel",
			    "GitPreTrainedModel",
			    "GitVisionModel",
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Optional[int]   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





