





'''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


__SCREAMING_SNAKE_CASE     :Tuple       =				False







class   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    List[str]    ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




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




 @property
 def      lowercase	(					self       :    List[Any]    ):
      return 1_2




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      return 3_2




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   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       :    List[Any]    ):
      _UpperCAmelCase     							=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
      return tokenizer




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   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(__UpperCamelCase    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      torch.manual_seed(0    )

      _UpperCAmelCase     							=   1_2
      _UpperCAmelCase     							=   1_2

      _UpperCAmelCase     							=   {
          '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',
      }

      _UpperCAmelCase     							=   TransformeraDModel(**__UpperCamelCase    )
      return model




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   'cpu'

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

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

      _UpperCAmelCase     							=   'teddy bear playing in the pool'

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

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

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

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

      _UpperCAmelCase     							=   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      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   'cpu'

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

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

      _UpperCAmelCase     							=   'teddy bear playing in the pool'

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

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

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

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

      _UpperCAmelCase     							=   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      lowercase	(					self       :    List[str]    ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




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

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

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

      _UpperCAmelCase     							=   output.images[0]

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





"""simple docstring"""







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__SCREAMING_SNAKE_CASE    	="python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
       pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
       pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
       if pkg in deps:
              if pkg == "tokenizers":
                     # must be loaded here, or else tqdm check may fail
                     from .utils import is_tokenizers_available

                     if not is_tokenizers_available():
                            continue  # not required, check version only if installed

              require_version_core(deps[pkg])
       else:
              raise ValueError(F"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None      ):
     require_version(deps[pkg]     ,			__SCREAMING_SNAKE_CASE      )

