



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							 =					False

class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






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


							@property
							def _a      (							self		):
											return 12


							@property
							def _a      (							self		):
											return 12


							@property
							def _a      (							self		):
											return 32


							@property
							def _a      (							self		):
											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 _a      (							self		):
											a__				      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)
											return tokenizer


							@property
							def _a      (							self		):
											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-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=1_000  ,  )
											return CLIPTextModel(a_		)


							@property
							def _a      (							self		):
											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(**a_		)
											return model


							def _a      (							self		):
											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=a_		)

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

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

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

											a__				      =		torch.Generator(device=a_		).manual_seed(0		)
											a__				      =		pipe(
											    [prompt]  ,  generator=a_  ,  output_type="""np"""  ,  return_dict=a_  ,  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 _a      (							self		):
											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=a_  ,  hidden_size=self.text_embedder_hidden_size  ,  length=tokenizer.model_max_length		)

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

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

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

											a__				      =		torch.Generator(device=a_		).manual_seed(0		)
											a__				      =		pipe(
											    [prompt]  ,  generator=a_  ,  output_type="""np"""  ,  return_dict=a_  ,  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  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






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







							def _a      (							self		):
											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(a_		)
											pipeline.set_progress_bar_config(disable=a_		)

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

											a__				      =		output.images[0]

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






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

