
import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		List[Any]    	=     UnCLIPImageVariationPipeline
      _A						:		Optional[int]    	=     IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      _A						:		Union[str, Any]    	=     IMAGE_VARIATION_BATCH_PARAMS

      _A						:		List[Any]    	=     [
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      _A						:		int    	=     False
      @property
      def A_			(			self ):
          return 32
      @property
      def A_			(			self ):
          return 32
      @property
      def A_			(			self ):
          return self.time_input_dim
      @property
      def A_			(			self ):
          return self.time_input_dim * 4
      @property
      def A_			(			self ):
          return 1_00
      @property
      def A_			(			self ):
          snake_case__						=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
          return tokenizer
      @property
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				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=10_00		,	)
          return CLIPTextModelWithProjection(lowerCamelCase )
      @property
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size		,	projection_dim=self.text_embedder_hidden_size		,	num_hidden_layers=5		,	num_attention_heads=4		,	image_size=32		,	intermediate_size=37		,	patch_size=1		,	)
          return CLIPVisionModelWithProjection(lowerCamelCase )
      @property
      def A_			(			self ):
          torch.manual_seed(0 )

          snake_case__						=				{
              "clip_embeddings_dim": self.text_embedder_hidden_size,
              "time_embed_dim": self.time_embed_dim,
              "cross_attention_dim": self.cross_attention_dim,
          }

          snake_case__						=				UnCLIPTextProjModel(**lowerCamelCase )
          return model
      @property
      def A_			(			self ):
          torch.manual_seed(0 )

          snake_case__						=				{
              "sample_size": 32,
              # RGB in channels
              "in_channels": 3,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 6,
              "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
              "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
              "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "layers_per_block": 1,
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": "identity",
          }

          snake_case__						=				UNetaDConditionModel(**lowerCamelCase )
          return model
      @property
      def A_			(			self ):
          return {
              "sample_size": 64,
              "layers_per_block": 1,
              "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
              "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "in_channels": 6,
              "out_channels": 3,
          }
      @property
      def A_			(			self ):
          torch.manual_seed(0 )

          snake_case__						=				UNetaDModel(**self.dummy_super_res_kwargs )
          return model
      @property
      def A_			(			self ):
          # seeded differently to get different unet than `self.dummy_super_res_first`
          torch.manual_seed(1 )

          snake_case__						=				UNetaDModel(**self.dummy_super_res_kwargs )
          return model
      def A_			(			self ):
          snake_case__						=				self.dummy_decoder
          snake_case__						=				self.dummy_text_proj
          snake_case__						=				self.dummy_text_encoder
          snake_case__						=				self.dummy_tokenizer
          snake_case__						=				self.dummy_super_res_first
          snake_case__						=				self.dummy_super_res_last

          snake_case__						=				UnCLIPScheduler(
              variance_type="learned_range"		,	prediction_type="epsilon"		,	num_train_timesteps=10_00		,	)

          snake_case__						=				UnCLIPScheduler(
              variance_type="fixed_small_log"		,	prediction_type="epsilon"		,	num_train_timesteps=10_00		,	)

          snake_case__						=				CLIPImageProcessor(crop_size=32		,	size=32 )

          snake_case__						=				self.dummy_image_encoder

          return {
              "decoder": decoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_proj": text_proj,
              "feature_extractor": feature_extractor,
              "image_encoder": image_encoder,
              "super_res_first": super_res_first,
              "super_res_last": super_res_last,
              "decoder_scheduler": decoder_scheduler,
              "super_res_scheduler": super_res_scheduler,
          }
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=0		,	lowerCamelCase=True ):
          snake_case__						=				floats_tensor((1, 3, 32, 32)		,	rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
          if str(lowerCamelCase ).startswith("mps" ):
              snake_case__						=				torch.manual_seed(lowerCamelCase )
          else:
              snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )

          if pil_image:
              snake_case__						=				input_image * 0.5 + 0.5
              snake_case__						=				input_image.clamp(0		,	1 )
              snake_case__						=				input_image.cpu().permute(0		,	2		,	3		,	1 ).float().numpy()
              snake_case__						=				DiffusionPipeline.numpy_to_pil(lowerCamelCase )[0]

          return {
              "image": input_image,
              "generator": generator,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "np",
          }
      def A_			(			self ):
          snake_case__						=				"cpu"

          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(**lowerCamelCase )
          snake_case__						=				output.images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(
              **lowerCamelCase		,	return_dict=lowerCamelCase		,	)[0]

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

          assert image.shape == (1, 64, 64, 3)

          snake_case__						=				np.array(
              [
                  0.9_9_9_7,
                  0.0_0_0_2,
                  0.9_9_9_7,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.0_0_2_3,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.9_9_7_0,
              ] )

          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 A_			(			self ):
          snake_case__						=				"cpu"

          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(**lowerCamelCase )
          snake_case__						=				output.images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(
              **lowerCamelCase		,	return_dict=lowerCamelCase		,	)[0]

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

          assert image.shape == (1, 64, 64, 3)

          snake_case__						=				np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1] )

          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 A_			(			self ):
          snake_case__						=				"cpu"

          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )
          snake_case__						=				[
              pipeline_inputs["image"],
              pipeline_inputs["image"],
          ]

          snake_case__						=				pipe(**lowerCamelCase )
          snake_case__						=				output.images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )
          snake_case__						=				[
              tuple_pipeline_inputs["image"],
              tuple_pipeline_inputs["image"],
          ]

          snake_case__						=				pipe(
              **lowerCamelCase		,	return_dict=lowerCamelCase		,	)[0]

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

          assert image.shape == (2, 64, 64, 3)

          snake_case__						=				np.array(
              [
                  0.9_9_9_7,
                  0.9_9_8_9,
                  0.0_0_0_8,
                  0.0_0_2_1,
                  0.9_9_6_0,
                  0.0_0_1_8,
                  0.0_0_1_4,
                  0.0_0_0_2,
                  0.9_9_3_3,
              ] )

          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 A_			(			self ):
          snake_case__						=				torch.device("cpu" )




          class 					_SCREAMING_SNAKE_CASE  :
                _A						:		Dict    	=     1





          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(0 )
          snake_case__						=				pipe.decoder.dtype
          snake_case__						=				1

          snake_case__						=				(
              batch_size,
              pipe.decoder.config.in_channels,
              pipe.decoder.config.sample_size,
              pipe.decoder.config.sample_size,
          )
          snake_case__						=				pipe.prepare_latents(
              lowerCamelCase		,	dtype=lowerCamelCase		,	device=lowerCamelCase		,	generator=lowerCamelCase		,	latents=lowerCamelCase		,	scheduler=DummyScheduler() )

          snake_case__						=				(
              batch_size,
              pipe.super_res_first.config.in_channels // 2,
              pipe.super_res_first.config.sample_size,
              pipe.super_res_first.config.sample_size,
          )
          snake_case__						=				pipe.prepare_latents(
              lowerCamelCase		,	dtype=lowerCamelCase		,	device=lowerCamelCase		,	generator=lowerCamelCase		,	latents=lowerCamelCase		,	scheduler=DummyScheduler() )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(
              **lowerCamelCase		,	decoder_latents=lowerCamelCase		,	super_res_latents=lowerCamelCase ).images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )
          # Don't pass image, instead pass embedding
          snake_case__						=				pipeline_inputs.pop("image" )
          snake_case__						=				pipe.image_encoder(lowerCamelCase ).image_embeds

          snake_case__						=				pipe(
              **lowerCamelCase		,	decoder_latents=lowerCamelCase		,	super_res_latents=lowerCamelCase		,	image_embeddings=lowerCamelCase		,	).images

          # make sure passing text embeddings manually is identical
          assert np.abs(img_out_a - img_out_a ).max() < 1e-4
      @skip_mps
      def A_			(			self ):
          snake_case__						=				torch_device == "cpu"

          # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
          snake_case__						=				1e-2

          self._test_attention_slicing_forward_pass(
              test_max_difference=lowerCamelCase		,	expected_max_diff=lowerCamelCase )
      @skip_mps
      def A_			(			self ):
          snake_case__						=				torch_device == "cpu"
          snake_case__						=				True
          snake_case__						=				[
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

          self._test_inference_batch_single_identical(
              test_max_difference=lowerCamelCase		,	relax_max_difference=lowerCamelCase		,	additional_params_copy_to_batched_inputs=lowerCamelCase		,	)
      def A_			(			self ):
          snake_case__						=				[
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

          if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              snake_case__						=				[2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=lowerCamelCase		,	additional_params_copy_to_batched_inputs=lowerCamelCase		,	)
          else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=lowerCamelCase )
      @skip_mps
      def A_			(			self ):
          return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def A_			(			self ):
          return super().test_save_load_local()



      @skip_mps
      def A_			(			self ):
          return super().test_save_load_optional_components()





@slow
@require_torch_gpu
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def A_			(			self ):
          snake_case__						=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" )
          snake_case__						=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/unclip/karlo_v1_alpha_cat_variation_fp16.npy" )

          snake_case__						=				UnCLIPImageVariationPipeline.from_pretrained(
              "kakaobrain/karlo-v1-alpha-image-variations"		,	torch_dtype=torch.floataa )
          snake_case__						=				pipeline.to(lowerCamelCase )
          pipeline.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				torch.Generator(device="cpu" ).manual_seed(0 )
          snake_case__						=				pipeline(
              lowerCamelCase		,	generator=lowerCamelCase		,	output_type="np"		,	)

          snake_case__						=				output.images[0]

          assert image.shape == (2_56, 2_56, 3)

          assert_mean_pixel_difference(lowerCamelCase		,	lowerCamelCase		,	15 )





import argparse
import struct
import unittest




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase ):
          snake_case__						=				data

          # Initialize hash values
          snake_case__						=				[
              0X6A_09E_667,
              0XBB_67A_E85,
              0X3C_6EF_372,
              0XA5_4FF_53A,
              0X51_0E5_27F,
              0X9B_056_88C,
              0X1F_83D_9AB,
              0X5B_E0C_D19,
          ]

          # Initialize round constants
          snake_case__						=				[
              0X42_8A2_F98,
              0X71_374_491,
              0XB5_C0F_BCF,
              0XE9_B5D_BA5,
              0X39_56C_25B,
              0X59_F11_1F1,
              0X92_3F8_2A4,
              0XAB_1C5_ED5,
              0XD8_07A_A98,
              0X12_835_B01,
              0X24_318_5BE,
              0X55_0C7_DC3,
              0X72_BE5_D74,
              0X80_DEB_1FE,
              0X9B_DC0_6A7,
              0XC1_9BF_174,
              0XE4_9B6_9C1,
              0XEF_BE4_786,
              0X0F_C19_DC6,
              0X24_0CA_1CC,
              0X2D_E92_C6F,
              0X4A_748_4AA,
              0X5C_B0A_9DC,
              0X76_F98_8DA,
              0X98_3E5_152,
              0XA8_31C_66D,
              0XB0_032_7C8,
              0XBF_597_FC7,
              0XC6_E00_BF3,
              0XD5_A79_147,
              0X06_CA6_351,
              0X14_292_967,
              0X27_B70_A85,
              0X2E_1B2_138,
              0X4D_2C6_DFC,
              0X53_380_D13,
              0X65_0A7_354,
              0X76_6A0_ABB,
              0X81_C2C_92E,
              0X92_722_C85,
              0XA2_BFE_8A1,
              0XA8_1A6_64B,
              0XC2_4B8_B70,
              0XC7_6C5_1A3,
              0XD1_92E_819,
              0XD6_990_624,
              0XF4_0E3_585,
              0X10_6AA_070,
              0X19_A4C_116,
              0X1E_376_C08,
              0X27_487_74C,
              0X34_B0B_CB5,
              0X39_1C0_CB3,
              0X4E_D8A_A4A,
              0X5B_9CC_A4F,
              0X68_2E6_FF3,
              0X74_8F8_2EE,
              0X78_A56_36F,
              0X84_C87_814,
              0X8C_C70_208,
              0X90_BEF_FFA,
              0XA4_506_CEB,
              0XBE_F9A_3F7,
              0XC6_717_8F2,
          ]

          snake_case__						=				self.preprocessing(self.data )
          self.final_hash()
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				B"\x80" + (B"\x00" * (63 - (len(lowerCamelCase ) + 8) % 64))
          snake_case__						=				struct.pack(">Q"		,	(len(lowerCamelCase ) * 8) )
          return data + padding + big_endian_integer
      def A_			(			self ):
          # Convert into blocks of 64 bytes
          snake_case__						=				[
              self.preprocessed_data[x : x + 64]
              for x in range(0		,	len(self.preprocessed_data )		,	64 )
          ]

          for block in self.blocks:
              # Convert the given block into a list of 4 byte integers
              snake_case__						=				list(struct.unpack(">16L"		,	lowerCamelCase ) )
              # add 48 0-ed integers
              words += [0] * 48

              snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				self.hashes

              for index in range(0		,	64 ):
                  if index > 15:
                      # modify the zero-ed indexes at the end of the array
                      snake_case__						=				(
                          self.ror(words[index - 15]		,	7 )
                          ^ self.ror(words[index - 15]		,	18 )
                          ^ (words[index - 15] >> 3)
                      )
                      snake_case__						=				(
                          self.ror(words[index - 2]		,	17 )
                          ^ self.ror(words[index - 2]		,	19 )
                          ^ (words[index - 2] >> 10)
                      )

                      snake_case__						=				(
                          words[index - 16] + sa + words[index - 7] + sa
                      ) % 0X100_000_000

                  # Compression
                  snake_case__						=				self.ror(lowerCamelCase		,	6 ) ^ self.ror(lowerCamelCase		,	11 ) ^ self.ror(lowerCamelCase		,	25 )
                  snake_case__						=				(e & f) ^ ((~e & 0XFF_FFF_FFF) & g)
                  snake_case__						=				(
                      h + sa + ch + self.round_constants[index] + words[index]
                  ) % 0X100_000_000
                  snake_case__						=				self.ror(lowerCamelCase		,	2 ) ^ self.ror(lowerCamelCase		,	13 ) ^ self.ror(lowerCamelCase		,	22 )
                  snake_case__						=				(a & b) ^ (a & c) ^ (b & c)
                  snake_case__						=				(sa + maj) % 0X100_000_000

                  snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				(
                      g,
                      f,
                      e,
                      ((d + tempa) % 0X100_000_000),
                      c,
                      b,
                      a,
                      ((tempa + tempa) % 0X100_000_000),
                  )

              snake_case__						=				[a, b, c, d, e, f, g, h]

              # Modify final values
              snake_case__						=				[
                  ((element + mutated_hash_values[index]) % 0X100_000_000)
                  for index, element in enumerate(self.hashes )
              ]

          snake_case__						=				"".join([hex(lowerCamelCase )[2:].zfill(8 ) for value in self.hashes] )



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          return 0XFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)





class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          import hashlib

          snake_case__						=				bytes("Test String"		,	"utf-8" )
          self.assertEqual(SHAaaa(lowerCamelCase ).hash		,	hashlib.shaaaa(lowerCamelCase ).hexdigest() )





def 	SCREAMING_SNAKE_CASE__    (      ):
    import doctest

    doctest.testmod()

    snake_case__						=				argparse.ArgumentParser()
    parser.add_argument(
        "-s"    ,		"--string"    ,		dest="input_string"    ,		default="Hello World!! Welcome to Cryptography"    ,		help="Hash the string"    ,		)
    parser.add_argument(
        "-f"    ,		"--file"    ,		dest="input_file"    ,		help="Hash contents of a file"							)

    snake_case__						=				parser.parse_args()

    snake_case__						=				args.input_string

    # hash input should be a bytestring
    if args.input_file:
        with open(args.input_file    ,		"rb"							) as f:
            snake_case__						=				f.read()
    else:
        snake_case__						=				bytes(__lowerCAmelCase    ,		"utf-8"							)

    print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
     main()



