


"""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				=						False




class 	lowercase__				(					unittest.TestCase				):





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





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      return 12





   @property
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      return 12





   @property
   def 		UpperCamelCase_  (							self)  ->  Dict:
      return 32





   @property
   def 		UpperCamelCase_  (							self)  ->  Dict:
      torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		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:
      _lowerCamelCase	:					Union[str, Any]   =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
      return tokenizer





   @property
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      torch.manual_seed(0)
      _lowerCamelCase	:					List[str]   =		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=1000      ,   )
      return CLIPTextModel(SCREAMING_SNAKE_CASE)





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      torch.manual_seed(0)

      _lowerCamelCase	:					Tuple   =		12
      _lowerCamelCase	:					List[Any]   =		12

      _lowerCamelCase	:					List[str]   =		{
          """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""",
      }

      _lowerCamelCase	:					Dict   =		TransformeraDModel(**SCREAMING_SNAKE_CASE)
      return model





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		"""cpu"""

      _lowerCamelCase	:					Optional[Any]   =		self.dummy_vqvae
      _lowerCamelCase	:					Dict   =		self.dummy_text_encoder
      _lowerCamelCase	:					int   =		self.dummy_tokenizer
      _lowerCamelCase	:					List[str]   =		self.dummy_transformer
      _lowerCamelCase	:					List[Any]   =		VQDiffusionScheduler(self.num_embed)
      _lowerCamelCase	:					Tuple   =		LearnedClassifierFreeSamplingEmbeddings(learnable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		VQDiffusionPipeline(
          vqvae=SCREAMING_SNAKE_CASE      ,   text_encoder=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE      ,   transformer=SCREAMING_SNAKE_CASE      ,   scheduler=SCREAMING_SNAKE_CASE      ,   learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					str   =		pipe.to(SCREAMING_SNAKE_CASE)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		"""teddy bear playing in the pool"""

      _lowerCamelCase	:					Union[str, Any]   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
      _lowerCamelCase	:					Dict   =		pipe([prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   num_inference_steps=2      ,   output_type="""np""")
      _lowerCamelCase	:					int   =		output.images

      _lowerCamelCase	:					int   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
      _lowerCamelCase	:					Dict   =		pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   output_type="""np"""      ,   return_dict=SCREAMING_SNAKE_CASE      ,   num_inference_steps=2)[0]

      _lowerCamelCase	:					Optional[int]   =		image[0, -3:, -3:, -1]
      _lowerCamelCase	:					Tuple   =		image_from_tuple[0, -3:, -3:, -1]

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

      _lowerCamelCase	:					Dict   =		np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92])

      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)  ->  str:
      _lowerCamelCase	:					Dict   =		"""cpu"""

      _lowerCamelCase	:					Optional[int]   =		self.dummy_vqvae
      _lowerCamelCase	:					Union[str, Any]   =		self.dummy_text_encoder
      _lowerCamelCase	:					Union[str, Any]   =		self.dummy_tokenizer
      _lowerCamelCase	:					Optional[int]   =		self.dummy_transformer
      _lowerCamelCase	:					Any   =		VQDiffusionScheduler(self.num_embed)
      _lowerCamelCase	:					Tuple   =		LearnedClassifierFreeSamplingEmbeddings(
          learnable=SCREAMING_SNAKE_CASE      ,   hidden_size=self.text_embedder_hidden_size      ,   length=tokenizer.model_max_length)

      _lowerCamelCase	:					Dict   =		VQDiffusionPipeline(
          vqvae=SCREAMING_SNAKE_CASE      ,   text_encoder=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE      ,   transformer=SCREAMING_SNAKE_CASE      ,   scheduler=SCREAMING_SNAKE_CASE      ,   learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Any   =		pipe.to(SCREAMING_SNAKE_CASE)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		"""teddy bear playing in the pool"""

      _lowerCamelCase	:					Any   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
      _lowerCamelCase	:					Optional[int]   =		pipe([prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   num_inference_steps=2      ,   output_type="""np""")
      _lowerCamelCase	:					Union[str, Any]   =		output.images

      _lowerCamelCase	:					List[str]   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
      _lowerCamelCase	:					str   =		pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   output_type="""np"""      ,   return_dict=SCREAMING_SNAKE_CASE      ,   num_inference_steps=2)[0]

      _lowerCamelCase	:					Any   =		image[0, -3:, -3:, -1]
      _lowerCamelCase	:					str   =		image_from_tuple[0, -3:, -3:, -1]

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

      _lowerCamelCase	:					List[str]   =		np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88])

      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				):





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Union[str, Any]   =		load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""")

      _lowerCamelCase	:					Dict   =		VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""")
      _lowerCamelCase	:					List[Any]   =		pipeline.to(SCREAMING_SNAKE_CASE)
      pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      # requires GPU generator for gumbel softmax
      # don't use GPU generator in tests though
      _lowerCamelCase	:					Union[str, Any]   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
      _lowerCamelCase	:					List[Any]   =		pipeline(
          """teddy bear playing in the pool"""      ,   num_images_per_prompt=1      ,   generator=SCREAMING_SNAKE_CASE      ,   output_type="""np"""      ,   )

      _lowerCamelCase	:					str   =		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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''mobilenet_v2'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=2_2_4				,     lowercase__=1.0				,     lowercase__=8				,     lowercase__=8				,     lowercase__=6				,     lowercase__=3_2				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu6"				,     lowercase__=True				,     lowercase__=0.8				,     lowercase__=0.0_2				,     lowercase__=0.0_0_1				,     lowercase__=2_5_5				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

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

							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Dict	    =	depth_multiplier
							__UpperCAmelCase			:							Optional[Any]	    =	depth_divisible_by
							__UpperCAmelCase			:							Union[str, Any]	    =	min_depth
							__UpperCAmelCase			:							Dict	    =	expand_ratio
							__UpperCAmelCase			:							int	    =	output_stride
							__UpperCAmelCase			:							Tuple	    =	first_layer_is_expansion
							__UpperCAmelCase			:							Union[str, Any]	    =	finegrained_output
							__UpperCAmelCase			:							int	    =	hidden_act
							__UpperCAmelCase			:							Tuple	    =	tf_padding
							__UpperCAmelCase			:							List[str]	    =	classifier_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	initializer_range
							__UpperCAmelCase			:							List[str]	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	semantic_loss_ignore_index






class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						version.parse('''1.11''' )


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


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

