'''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


_UpperCAmelCase    :     int         =       False







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Union[str, Any]:
        return 12
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			str:
        return 12
   @property
   def 	__UpperCAmelCase   (      self      :			int     )  ->			str:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        torch.manual_seed(0     )
        _A    		=  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 	__UpperCAmelCase   (      self      :			Tuple     )  ->			int:
        _A    		=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
        return tokenizer
   @property
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[int]:
        torch.manual_seed(0     )
        _A    		=  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(__lowerCAmelCase     )
   @property
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			str:
        torch.manual_seed(0     )

        _A    		=  12
        _A    		=  12

        _A    		=  {
            """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""",
        }

        _A    		=  TransformeraDModel(**__lowerCAmelCase     )
        return model
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Dict:
        _A    		=  """cpu"""

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

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

        _A    		=  """teddy bear playing in the pool"""

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

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

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

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

        _A    		=  np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]     )

        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 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[int]:
        _A    		=  """cpu"""

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

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

        _A    		=  """teddy bear playing in the pool"""

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

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

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

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

        _A    		=  np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]     )

        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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

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

   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			int:
        _A    		=  load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'     )

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

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

        _A    		=  output.images[0]

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











'''simple docstring'''

from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






