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__  :  Optional[int]											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Optional[int]											=     {
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"mobilevitv2"







					def __init__(    self       :     Tuple   ,lowerCamelCase__       :     List[str]=3   ,lowerCamelCase__       :     Union[str, Any]=256   ,lowerCamelCase__       :     List[str]=2   ,lowerCamelCase__       :     Optional[Any]=2.0   ,lowerCamelCase__       :     Dict="swish"   ,lowerCamelCase__       :     str=3   ,lowerCamelCase__       :     int=32   ,lowerCamelCase__       :     str=0.1   ,lowerCamelCase__       :     Optional[int]=0.0_2   ,lowerCamelCase__       :     str=1e-5   ,lowerCamelCase__       :     Any=512   ,lowerCamelCase__       :     Optional[int]=[6, 12, 18]   ,lowerCamelCase__       :     Any=0.1   ,lowerCamelCase__       :     str=255   ,lowerCamelCase__       :     Optional[int]=[2, 4, 3]   ,lowerCamelCase__       :     Optional[Any]=[128, 192, 256]   ,lowerCamelCase__       :     Optional[Any]=1.0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=0.0   ,lowerCamelCase__       :     int=0.0   ,**lowerCamelCase__       :     Union[str, Any]   ,):
									super().__init__(**lowerCamelCase__      )

									UpperCAmelCase__					=							num_channels
									UpperCAmelCase__					=							image_size
									UpperCAmelCase__					=							patch_size
									UpperCAmelCase__					=							expand_ratio
									UpperCAmelCase__					=							hidden_act
									UpperCAmelCase__					=							conv_kernel_size
									UpperCAmelCase__					=							output_stride
									UpperCAmelCase__					=							initializer_range
									UpperCAmelCase__					=							layer_norm_eps
									UpperCAmelCase__					=							n_attn_blocks
									UpperCAmelCase__					=							base_attn_unit_dims
									UpperCAmelCase__					=							width_multiplier
									UpperCAmelCase__					=							ffn_multiplier
									UpperCAmelCase__					=							ffn_dropout
									UpperCAmelCase__					=							attn_dropout
									UpperCAmelCase__					=							classifier_dropout_prob

									# decode head attributes for semantic segmentation
									UpperCAmelCase__					=							aspp_out_channels
									UpperCAmelCase__					=							atrous_rates
									UpperCAmelCase__					=							aspp_dropout_prob
									UpperCAmelCase__					=							semantic_loss_ignore_index






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			version.parse("1.11"    )







					@property
					def 		__lowerCAmelCase						(    self       :     str      ):
									return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]      )







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







					@property
					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									return 1e-4




"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			KandinskyPriorPipeline
					snake_case__       =			["prompt"]
					snake_case__       =			["prompt", "negative_prompt"]
					snake_case__       =			[
					    "num_images_per_prompt",
					    "generator",
					    "num_inference_steps",
					    "latents",
					    "negative_prompt",
					    "guidance_scale",
					    "output_type",
					    "return_dict",
					]
					snake_case__       =			False







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return self.time_input_dim







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 100







					@property
					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
									return tokenizer







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPTextConfig(
									    bos_token_id=0   ,eos_token_id=2   ,hidden_size=self.text_embedder_hidden_size   ,projection_dim=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 CLIPTextModelWithProjection(lowerCamelCase__      )







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									torch.manual_seed(0      )

									UpperCAmelCase__					=							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									UpperCAmelCase__					=							PriorTransformer(**lowerCamelCase__      )
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									UpperCAmelCase__					=							nn.Parameter(torch.ones(model.clip_std.shape      )      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size   ,image_size=224   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=37   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=14   ,)

									UpperCAmelCase__					=							CLIPVisionModelWithProjection(lowerCamelCase__      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							CLIPImageProcessor(
									    crop_size=224   ,do_center_crop=lowerCamelCase__   ,do_normalize=lowerCamelCase__   ,do_resize=lowerCamelCase__   ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,resample=3   ,size=224   ,)

									return image_processor







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.dummy_prior
									UpperCAmelCase__					=							self.dummy_image_encoder
									UpperCAmelCase__					=							self.dummy_text_encoder
									UpperCAmelCase__					=							self.dummy_tokenizer
									UpperCAmelCase__					=							self.dummy_image_processor

									UpperCAmelCase__					=							UnCLIPScheduler(
									    variance_type='fixed_small_log'   ,prediction_type='sample'   ,num_train_timesteps=1_000   ,clip_sample=lowerCamelCase__   ,clip_sample_range=1_0.0   ,)

									UpperCAmelCase__					=							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'scheduler': scheduler,
									    'image_processor': image_processor,
									}

									return components







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[int]=0      ):
									if str(lowerCamelCase__      ).startswith('mps'      ):
													UpperCAmelCase__					=							torch.manual_seed(lowerCamelCase__      )
									else:
													UpperCAmelCase__					=							torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )
									UpperCAmelCase__					=							{
									    'prompt': 'horse',
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							'cpu'

									UpperCAmelCase__					=							self.get_dummy_components()

									UpperCAmelCase__					=							self.pipeline_class(**lowerCamelCase__      )
									UpperCAmelCase__					=							pipe.to(lowerCamelCase__      )

									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									UpperCAmelCase__					=							pipe(**self.get_dummy_inputs(lowerCamelCase__      )      )
									UpperCAmelCase__					=							output.image_embeds

									UpperCAmelCase__					=							pipe(
									    **self.get_dummy_inputs(lowerCamelCase__      )   ,return_dict=lowerCamelCase__   ,)[0]

									UpperCAmelCase__					=							image[0, -10:]
									UpperCAmelCase__					=							image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase__					=							np.array(
									    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							True
									UpperCAmelCase__					=							False

									self._test_inference_batch_single_identical(
									    test_max_difference=lowerCamelCase__   ,relax_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)



