





'''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


__magic_name__   :						Optional[Any]    =			False


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






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

     @property
     def 	UpperCamelCase(					self	):
       return 12

     @property
     def 	UpperCamelCase(					self	):
       return 12

     @property
     def 	UpperCamelCase(					self	):
       return 32

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				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	):
       _snake_case     		=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
       return tokenizer

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				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(lowerCamelCase	)

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)

       _snake_case     		=				12
       _snake_case     		=				12

       _snake_case     		=				{
           "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",
       }

       _snake_case     		=				TransformeraDModel(**lowerCamelCase	)
       return model

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"

       _snake_case     		=				self.dummy_vqvae
       _snake_case     		=				self.dummy_text_encoder
       _snake_case     		=				self.dummy_tokenizer
       _snake_case     		=				self.dummy_transformer
       _snake_case     		=				VQDiffusionScheduler(self.num_embed	)
       _snake_case     		=				LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCamelCase	)

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

       _snake_case     		=				"teddy bear playing in the pool"

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

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

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

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

       _snake_case     		=				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 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"

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

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

       _snake_case     		=				"teddy bear playing in the pool"

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

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

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

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

       _snake_case     		=				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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






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






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

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

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

       _snake_case     		=				output.images[0]

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









'''simple docstring'''



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


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''mobilenet_v1'''

     def __init__(					self						,   lowerCamelCase=3						,   lowerCamelCase=224						,   lowerCamelCase=1.0						,   lowerCamelCase=8						,   lowerCamelCase="relu6"						,   lowerCamelCase=True						,   lowerCamelCase=0.999						,   lowerCamelCase=0.02						,   lowerCamelCase=0.001						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)

       if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."	)

       _snake_case     		=				num_channels
       _snake_case     		=				image_size
       _snake_case     		=				depth_multiplier
       _snake_case     		=				min_depth
       _snake_case     		=				hidden_act
       _snake_case     		=				tf_padding
       _snake_case     		=				classifier_dropout_prob
       _snake_case     		=				initializer_range
       _snake_case     		=				layer_norm_eps







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict([("pixel_values", {0: "batch"})]	)

     @property
     def 	UpperCamelCase(					self	):
       if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]	)
       else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]	)






     @property
     def 	UpperCamelCase(					self	):
       return 1e-4


