




'''simple docstring'''


import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowercase  =			False
class 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  Tuple      ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




	@property
	def   lowercase__   (       self     :  Tuple      ):
							return 1_2




	@property
	def   lowercase__   (       self     :  Optional[Any]      ):
							return 1_2




	@property
	def   lowercase__   (       self     :  int      ):
							return 3_2




	@property
	def   lowercase__   (       self     :  Tuple      ):
							torch.manual_seed(0      )
							__snake_case						 =   VQModel(
							    block_out_channels=[3_2, 6_4]       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,		latent_channels=3       ,		num_vq_embeddings=self.num_embed       ,		vq_embed_dim=3       ,		)
							return model




	@property
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
							return tokenizer




	@property
	def   lowercase__   (       self     :  str      ):
							torch.manual_seed(0      )
							__snake_case						 =   CLIPTextConfig(
							    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=self.text_embedder_hidden_size       ,		intermediate_size=3_7       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=1_0_0_0       ,		)
							return CLIPTextModel(__lowerCAmelCase      )




	@property
	def   lowercase__   (       self     :  Tuple      ):
							torch.manual_seed(0      )

							__snake_case						 =   1_2
							__snake_case						 =   1_2

							__snake_case						 =   {
							    'attention_bias': True,
							    'cross_attention_dim': 3_2,
							    'attention_head_dim': height * width,
							    'num_attention_heads': 1,
							    'num_vector_embeds': self.num_embed,
							    'num_embeds_ada_norm': self.num_embeds_ada_norm,
							    'norm_num_groups': 3_2,
							    'sample_size': width,
							    'activation_fn': 'geglu-approximate',
							}

							__snake_case						 =   TransformeraDModel(**__lowerCAmelCase      )
							return model




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   'cpu'

							__snake_case						 =   self.dummy_vqvae
							__snake_case						 =   self.dummy_text_encoder
							__snake_case						 =   self.dummy_tokenizer
							__snake_case						 =   self.dummy_transformer
							__snake_case						 =   VQDiffusionScheduler(self.num_embed      )
							__snake_case						 =   LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCAmelCase      )

							__snake_case						 =   VQDiffusionPipeline(
							    vqvae=__lowerCAmelCase       ,		text_encoder=__lowerCAmelCase       ,		tokenizer=__lowerCAmelCase       ,		transformer=__lowerCAmelCase       ,		scheduler=__lowerCAmelCase       ,		learned_classifier_free_sampling_embeddings=__lowerCAmelCase       ,		)
							__snake_case						 =   pipe.to(__lowerCAmelCase      )
							pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

							__snake_case						 =   'teddy bear playing in the pool'

							__snake_case						 =   torch.Generator(device=__lowerCAmelCase      ).manual_seed(0      )
							__snake_case						 =   pipe([prompt]       ,		generator=__lowerCAmelCase       ,		num_inference_steps=2       ,		output_type='np'      )
							__snake_case						 =   output.images

							__snake_case						 =   torch.Generator(device=__lowerCAmelCase      ).manual_seed(0      )
							__snake_case						 =   pipe(
							    [prompt]       ,		generator=__lowerCAmelCase       ,		output_type='np'       ,		return_dict=__lowerCAmelCase       ,		num_inference_steps=2      )[0]

							__snake_case						 =   image[0, -3:, -3:, -1]
							__snake_case						 =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 2_4, 2_4, 3)

							__snake_case						 =   np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   'cpu'

							__snake_case						 =   self.dummy_vqvae
							__snake_case						 =   self.dummy_text_encoder
							__snake_case						 =   self.dummy_tokenizer
							__snake_case						 =   self.dummy_transformer
							__snake_case						 =   VQDiffusionScheduler(self.num_embed      )
							__snake_case						 =   LearnedClassifierFreeSamplingEmbeddings(
							    learnable=__lowerCAmelCase       ,		hidden_size=self.text_embedder_hidden_size       ,		length=tokenizer.model_max_length      )

							__snake_case						 =   VQDiffusionPipeline(
							    vqvae=__lowerCAmelCase       ,		text_encoder=__lowerCAmelCase       ,		tokenizer=__lowerCAmelCase       ,		transformer=__lowerCAmelCase       ,		scheduler=__lowerCAmelCase       ,		learned_classifier_free_sampling_embeddings=__lowerCAmelCase       ,		)
							__snake_case						 =   pipe.to(__lowerCAmelCase      )
							pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

							__snake_case						 =   'teddy bear playing in the pool'

							__snake_case						 =   torch.Generator(device=__lowerCAmelCase      ).manual_seed(0      )
							__snake_case						 =   pipe([prompt]       ,		generator=__lowerCAmelCase       ,		num_inference_steps=2       ,		output_type='np'      )
							__snake_case						 =   output.images

							__snake_case						 =   torch.Generator(device=__lowerCAmelCase      ).manual_seed(0      )
							__snake_case						 =   pipe(
							    [prompt]       ,		generator=__lowerCAmelCase       ,		output_type='np'       ,		return_dict=__lowerCAmelCase       ,		num_inference_steps=2      )[0]

							__snake_case						 =   image[0, -3:, -3:, -1]
							__snake_case						 =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 2_4, 2_4, 3)

							__snake_case						 =   np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2.0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2





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




	def   lowercase__   (       self     :  Any      ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'      )

							__snake_case						 =   VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'      )
							__snake_case						 =   pipeline.to(__lowerCAmelCase      )
							pipeline.set_progress_bar_config(disable=__lowerCAmelCase      )

							# requires GPU generator for gumbel softmax
							# don't use GPU generator in tests though
							__snake_case						 =   torch.Generator(device=__lowerCAmelCase      ).manual_seed(0      )
							__snake_case						 =   pipeline(
							    'teddy bear playing in the pool'       ,		num_images_per_prompt=1       ,		generator=__lowerCAmelCase       ,		output_type='np'       ,		)

							__snake_case						 =   output.images[0]

							assert image.shape == (2_5_6, 2_5_6, 3)
							assert np.abs(expected_image - image      ).max() < 2.0







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase  =			{
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GroupViTModel""",
	    """GroupViTPreTrainedModel""",
	    """GroupViTTextModel""",
	    """GroupViTVisionModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGroupViTModel""",
	    """TFGroupViTPreTrainedModel""",
	    """TFGroupViTTextModel""",
	    """TFGroupViTVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_groupvit import (
	    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    GroupViTConfig,
	    GroupViTOnnxConfig,
	    GroupViTTextConfig,
	    GroupViTVisionConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_groupvit import (
		    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GroupViTModel,
		    GroupViTPreTrainedModel,
		    GroupViTTextModel,
		    GroupViTVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_groupvit import (
		    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGroupViTModel,
		    TFGroupViTPreTrainedModel,
		    TFGroupViTTextModel,
		    TFGroupViTVisionModel,
		)

else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

