

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


A__						=							False

class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						return 12







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return 12







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						return 32







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    VQModel(
						    block_out_channels=[32, 64]  , 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 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
						return tokenizer







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    CLIPTextConfig(
						    bos_token_id=0  , eos_token_id=2  , hidden_size=self.text_embedder_hidden_size  , intermediate_size=37  , layer_norm_eps=1E-05  , num_attention_heads=4  , num_hidden_layers=5  , pad_token_id=1  , vocab_size=10_00  , )
						return CLIPTextModel(__UpperCamelCase      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						torch.manual_seed(0      )

						__magic_name__							=    12
						__magic_name__							=    12

						__magic_name__							=    {
						    'attention_bias': True,
						    'cross_attention_dim': 32,
						    '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': 32,
						    'sample_size': width,
						    'activation_fn': 'geglu-approximate',
						}

						__magic_name__							=    TransformeraDModel(**__UpperCamelCase      )
						return model







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.dummy_vqvae
						__magic_name__							=    self.dummy_text_encoder
						__magic_name__							=    self.dummy_tokenizer
						__magic_name__							=    self.dummy_transformer
						__magic_name__							=    VQDiffusionScheduler(self.num_embed      )
						__magic_name__							=    LearnedClassifierFreeSamplingEmbeddings(learnable=__UpperCamelCase      )

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

						__magic_name__							=    'teddy bear playing in the pool'

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

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

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

						assert image.shape == (1, 24, 24, 3)

						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

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

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

						__magic_name__							=    'teddy bear playing in the pool'

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

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

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

						assert image.shape == (1, 24, 24, 3)

						__magic_name__							=    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 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'      )

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

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

						__magic_name__							=    output.images[0]

						assert image.shape == (2_56, 2_56, 3)
						assert np.abs(expected_image - image      ).max() < 2.0







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




