
"""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 KandinskyVaaPriorPipeline, 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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		KandinskyVaaPriorPipeline
     lowercase		     =		["prompt"]
     lowercase		     =		["prompt", "negative_prompt"]
     lowercase		     =		[
         "num_images_per_prompt",
         "generator",
         "num_inference_steps",
         "latents",
         "negative_prompt",
         "guidance_scale",
         "output_type",
         "return_dict",
     ]
     lowercase		     =		False





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 100





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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=1000			,  )
      return CLIPTextModelWithProjection(__UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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(**__UpperCAmelCase   )
      # 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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(__UpperCAmelCase   )
      return model





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= CLIPImageProcessor(
          crop_size=224			,  do_center_crop=__UpperCAmelCase			,  do_normalize=__UpperCAmelCase			,  do_resize=__UpperCAmelCase			,  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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __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=1000			,  clip_sample=__UpperCAmelCase			,  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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'prompt': 'horse',
          'generator': generator,
          'guidance_scale': 4.0,
          'num_inference_steps': 2,
          'output_type': 'np',
      }
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 'cpu'

      __UpperCamelCase       						= self.get_dummy_components()

      __UpperCamelCase       						= self.pipeline_class(**__UpperCAmelCase   )
      __UpperCamelCase       						= pipe.to(__UpperCAmelCase   )

      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= pipe(**self.get_dummy_inputs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= output.image_embeds

      __UpperCamelCase       						= pipe(
          **self.get_dummy_inputs(__UpperCAmelCase   )			,  return_dict=__UpperCAmelCase			,  )[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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= torch_device == 'cpu'
      __UpperCamelCase       						= True
      __UpperCamelCase       						= False

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







     @skip_mps
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= torch_device == 'cpu'
      __UpperCamelCase       						= False

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





"""simple docstring"""

from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase			:       Tuple									=				logging.get_logger(__name__)

UpperCamelCase			:       List[str]									=				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"efficientformer"





     def __init__(			self			,  __UpperCAmelCase = [3, 2, 6, 4]			,  __UpperCAmelCase = [48, 96, 224, 448]			,  __UpperCAmelCase = [True, True, True, True]			,  __UpperCAmelCase = 448			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 7			,  __UpperCAmelCase = 5			,  __UpperCAmelCase = 8			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 16			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1E-5			,  __UpperCAmelCase = "gelu"			,  __UpperCAmelCase = 0.0_2			,  __UpperCAmelCase = 1E-12			,  __UpperCAmelCase = 224			,  __UpperCAmelCase = 1E-05			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= patch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= mlp_expansion_ratio
      __UpperCamelCase       						= downsamples
      __UpperCamelCase       						= dim
      __UpperCamelCase       						= key_dim
      __UpperCamelCase       						= attention_ratio
      __UpperCamelCase       						= resolution
      __UpperCamelCase       						= pool_size
      __UpperCamelCase       						= downsample_patch_size
      __UpperCamelCase       						= downsample_stride
      __UpperCamelCase       						= downsample_pad
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= num_metaad_blocks
      __UpperCamelCase       						= distillation
      __UpperCamelCase       						= use_layer_scale
      __UpperCamelCase       						= layer_scale_init_value
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= batch_norm_eps



