



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


snake_case__					:     int    		=   False

class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



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



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							return 12



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






							'''simple docstring'''



							return 12



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






							'''simple docstring'''



							return 32



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






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			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     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



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



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			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     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							torch.manual_seed(0	)

							__lowercase		       =			12
							__lowercase		       =			12

							__lowercase		       =			{
							    "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		       =			TransformeraDModel(**A_	)
							return model



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			"cpu"

							__lowercase		       =			self.dummy_vqvae
							__lowercase		       =			self.dummy_text_encoder
							__lowercase		       =			self.dummy_tokenizer
							__lowercase		       =			self.dummy_transformer
							__lowercase		       =			VQDiffusionScheduler(self.num_embed	)
							__lowercase		       =			LearnedClassifierFreeSamplingEmbeddings(learnable=A_	)

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

							__lowercase		       =			"teddy bear playing in the pool"

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

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

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

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

							__lowercase		       =			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     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			"cpu"

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

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

							__lowercase		       =			"teddy bear playing in the pool"

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

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

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

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

							__lowercase		       =			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   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



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

							__lowercase		       =			VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"	)
							__lowercase		       =			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		       =			torch.Generator(device=A_	).manual_seed(0	)
							__lowercase		       =			pipeline(
							    "teddy bear playing in the pool"							,      num_images_per_prompt=1							,      generator=A_							,      output_type="np"							,      )

							__lowercase		       =			output.images[0]

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








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case__					:     List[Any]    		=   logging.get_logger(__name__)

snake_case__					:     List[str]    		=   {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     List[Any]      							=      """yolos"""



						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							Union[str, Any]=768							,      lowerCamelCase	:							int=12							,      lowerCamelCase	:							Union[str, Any]=12							,      lowerCamelCase	:							Optional[Any]=3_072							,      lowerCamelCase	:							Optional[int]="gelu"							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							Any=0.02							,      lowerCamelCase	:							Optional[Any]=1e-12							,      lowerCamelCase	:							Optional[Any]=[512, 864]							,      lowerCamelCase	:							str=16							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							str=True							,      lowerCamelCase	:							List[Any]=100							,      lowerCamelCase	:							Dict=True							,      lowerCamelCase	:							Dict=False							,      lowerCamelCase	:							List[str]=1							,      lowerCamelCase	:							str=5							,      lowerCamelCase	:							Any=2							,      lowerCamelCase	:							str=5							,      lowerCamelCase	:							Optional[int]=2							,      lowerCamelCase	:							List[Any]=0.1							,      **lowerCamelCase	:							List[Any]							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size
							__lowercase		       =			hidden_act
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			initializer_range
							__lowercase		       =			layer_norm_eps
							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			qkv_bias
							__lowercase		       =			num_detection_tokens
							__lowercase		       =			use_mid_position_embeddings
							__lowercase		       =			auxiliary_loss
							# Hungarian matcher
							__lowercase		       =			class_cost
							__lowercase		       =			bbox_cost
							__lowercase		       =			giou_cost
							# Loss coefficients
							__lowercase		       =			bbox_loss_coefficient
							__lowercase		       =			giou_loss_coefficient
							__lowercase		       =			eos_coefficient

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      version.parse("""1.11"""	)



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






							'''simple docstring'''



							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]	)



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return 1e-4



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return 12



