






"""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


__lowerCamelCase							=  False



class       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[str]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()

					@property
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											return 1_2

					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											return 1_2

					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Tuple:
											return 3_2

					@property
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Tuple:
											torch.manual_seed(0					)
											__magic_name__:	Union[str, Any]							=       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   lowerCamelCase__   (			self  :      Any					)			->							str:
											__magic_name__:	Dict							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)
											return tokenizer

					@property
					def   lowerCamelCase__   (			self  :      Any					)			->							List[Any]:
											torch.manual_seed(0					)
											__magic_name__:	List[Any]							=       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(A__					)

					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[int]:
											torch.manual_seed(0					)

											__magic_name__:	Any							=       1_2
											__magic_name__:	Dict							=       1_2

											__magic_name__:	int							=       {
											    """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""",
											}

											__magic_name__:	Optional[int]							=       TransformeraDModel(**A__					)
											return model

					def   lowerCamelCase__   (			self  :      int					)			->							Tuple:
											__magic_name__:	Any							=       """cpu"""

											__magic_name__:	List[Any]							=       self.dummy_vqvae
											__magic_name__:	List[str]							=       self.dummy_text_encoder
											__magic_name__:	List[str]							=       self.dummy_tokenizer
											__magic_name__:	Optional[Any]							=       self.dummy_transformer
											__magic_name__:	str							=       VQDiffusionScheduler(self.num_embed					)
											__magic_name__:	Dict							=       LearnedClassifierFreeSamplingEmbeddings(learnable=A__					)

											__magic_name__:	Tuple							=       VQDiffusionPipeline(
											    vqvae=A__     ,		text_encoder=A__     ,		tokenizer=A__     ,		transformer=A__     ,		scheduler=A__     ,		learned_classifier_free_sampling_embeddings=A__     ,		)
											__magic_name__:	Tuple							=       pipe.to(A__					)
											pipe.set_progress_bar_config(disable=A__					)

											__magic_name__:	Dict							=       """teddy bear playing in the pool"""

											__magic_name__:	Optional[int]							=       torch.Generator(device=A__					).manual_seed(0					)
											__magic_name__:	Any							=       pipe([prompt]     ,		generator=A__     ,		num_inference_steps=2     ,		output_type="""np"""					)
											__magic_name__:	Optional[int]							=       output.images

											__magic_name__:	Optional[Any]							=       torch.Generator(device=A__					).manual_seed(0					)
											__magic_name__:	str							=       pipe(
											    [prompt]     ,		generator=A__     ,		output_type="""np"""     ,		return_dict=A__     ,		num_inference_steps=2					)[0]

											__magic_name__:	Optional[int]							=       image[0, -3:, -3:, -1]
											__magic_name__:	str							=       image_from_tuple[0, -3:, -3:, -1]

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

											__magic_name__:	List[Any]							=       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   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Any:
											__magic_name__:	Union[str, Any]							=       """cpu"""

											__magic_name__:	Optional[int]							=       self.dummy_vqvae
											__magic_name__:	Dict							=       self.dummy_text_encoder
											__magic_name__:	Optional[Any]							=       self.dummy_tokenizer
											__magic_name__:	str							=       self.dummy_transformer
											__magic_name__:	List[Any]							=       VQDiffusionScheduler(self.num_embed					)
											__magic_name__:	str							=       LearnedClassifierFreeSamplingEmbeddings(
											    learnable=A__     ,		hidden_size=self.text_embedder_hidden_size     ,		length=tokenizer.model_max_length					)

											__magic_name__:	List[str]							=       VQDiffusionPipeline(
											    vqvae=A__     ,		text_encoder=A__     ,		tokenizer=A__     ,		transformer=A__     ,		scheduler=A__     ,		learned_classifier_free_sampling_embeddings=A__     ,		)
											__magic_name__:	Union[str, Any]							=       pipe.to(A__					)
											pipe.set_progress_bar_config(disable=A__					)

											__magic_name__:	List[str]							=       """teddy bear playing in the pool"""

											__magic_name__:	Union[str, Any]							=       torch.Generator(device=A__					).manual_seed(0					)
											__magic_name__:	Dict							=       pipe([prompt]     ,		generator=A__     ,		num_inference_steps=2     ,		output_type="""np"""					)
											__magic_name__:	Optional[int]							=       output.images

											__magic_name__:	Tuple							=       torch.Generator(device=A__					).manual_seed(0					)
											__magic_name__:	List[str]							=       pipe(
											    [prompt]     ,		generator=A__     ,		output_type="""np"""     ,		return_dict=A__     ,		num_inference_steps=2					)[0]

											__magic_name__:	Optional[Any]							=       image[0, -3:, -3:, -1]
											__magic_name__:	Union[str, Any]							=       image_from_tuple[0, -3:, -3:, -1]

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

											__magic_name__:	List[Any]							=       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   lowerCamelCase__   (			self  :      int					)			->							Tuple:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def   lowerCamelCase__   (			self  :      str					)			->							Optional[Any]:
											__magic_name__:	int							=       load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""					)

											__magic_name__:	Union[str, Any]							=       VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""					)
											__magic_name__:	Optional[int]							=       pipeline.to(A__					)
											pipeline.set_progress_bar_config(disable=A__					)

											# requires GPU generator for gumbel softmax
											# don't use GPU generator in tests though
											__magic_name__:	List[str]							=       torch.Generator(device=A__					).manual_seed(0					)
											__magic_name__:	Optional[Any]							=       pipeline(
											    """teddy bear playing in the pool"""     ,		num_images_per_prompt=1     ,		generator=A__     ,		output_type="""np"""     ,		)

											__magic_name__:	Optional[Any]							=       output.images[0]

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






A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



