
"""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


_A     :			str  						=			False

class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   @property
   def      __magic_name__     (     self							):
          return 12
   @property
   def      __magic_name__     (     self							):
          return 12
   @property
   def      __magic_name__     (     self							):
          return 32
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				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      __magic_name__     (     self							):
          lowercase       :							List[str]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
          return tokenizer
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							List[str]									=				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=1_000      ,       )
          return CLIPTextModel(_a							)
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)

          lowercase       :							Optional[Any]									=				12
          lowercase       :							Optional[int]									=				12

          lowercase       :							str									=				{
              "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",
          }

          lowercase       :							Tuple									=				TransformeraDModel(**_a							)
          return model
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"cpu"

          lowercase       :							Any									=				self.dummy_vqvae
          lowercase       :							Optional[int]									=				self.dummy_text_encoder
          lowercase       :							int									=				self.dummy_tokenizer
          lowercase       :							Dict									=				self.dummy_transformer
          lowercase       :							Tuple									=				VQDiffusionScheduler(self.num_embed							)
          lowercase       :							Optional[int]									=				LearnedClassifierFreeSamplingEmbeddings(learnable=_a							)

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

          lowercase       :							Union[str, Any]									=				"teddy bear playing in the pool"

          lowercase       :							str									=				torch.Generator(device=_a							).manual_seed(0							)
          lowercase       :							List[str]									=				pipe([prompt]      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"							)
          lowercase       :							str									=				output.images

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

          lowercase       :							Dict									=				image[0, -3:, -3:, -1]
          lowercase       :							str									=				image_from_tuple[0, -3:, -3:, -1]

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

          lowercase       :							Any									=				np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]							)

          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      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				"cpu"

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

          lowercase       :							Tuple									=				VQDiffusionPipeline(
              vqvae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       transformer=_a      ,       scheduler=_a      ,       learned_classifier_free_sampling_embeddings=_a      ,       )
          lowercase       :							Union[str, Any]									=				pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Any									=				"teddy bear playing in the pool"

          lowercase       :							Dict									=				torch.Generator(device=_a							).manual_seed(0							)
          lowercase       :							Dict									=				pipe([prompt]      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"							)
          lowercase       :							Tuple									=				output.images

          lowercase       :							Dict									=				torch.Generator(device=_a							).manual_seed(0							)
          lowercase       :							Union[str, Any]									=				pipe(
              [prompt]      ,       generator=_a      ,       output_type="np"      ,       return_dict=_a      ,       num_inference_steps=2							)[0]

          lowercase       :							Optional[int]									=				image[0, -3:, -3:, -1]
          lowercase       :							Any									=				image_from_tuple[0, -3:, -3:, -1]

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

          lowercase       :							List[str]									=				np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]							)

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

   def      __magic_name__     (     self							):
          lowercase       :							Any									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"							)

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

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

          lowercase       :							int									=				output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image							).max() < 2.0




"""simple docstring"""




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 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


