

'''simple docstring'''



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       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					List[str]     =					UnCLIPImageVariationPipeline
      SCREAMING_SNAKE_CASE__			:					Optional[int]     =					IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
      SCREAMING_SNAKE_CASE__			:					Any     =					IMAGE_VARIATION_BATCH_PARAMS

      SCREAMING_SNAKE_CASE__			:					Optional[int]     =					[
          '''generator''',
          '''return_dict''',
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]
      SCREAMING_SNAKE_CASE__			:					List[str]     =					False




      @property
      def 						__UpperCAmelCase      (       self			:		Any ):



          """simple docstring"""





          return 32




      @property
      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





          return 32




      @property
      def 						__UpperCAmelCase      (       self			:		str ):



          """simple docstring"""





          return self.time_input_dim




      @property
      def 						__UpperCAmelCase      (       self			:		Any ):



          """simple docstring"""





          return self.time_input_dim * 4




      @property
      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          return 100




      @property
      def 						__UpperCAmelCase      (       self			:		List[str] ):



          """simple docstring"""





          _snake_case			:			List[Any]				       =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
          return tokenizer




      @property
      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





          torch.manual_seed(0 )
          _snake_case			:			Any				       =		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			:		Optional[Any] ):



          """simple docstring"""





          torch.manual_seed(0 )
          _snake_case			:			Dict				       =		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(UpperCAmelCase__ )




      @property
      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          torch.manual_seed(0 )

          _snake_case			:			int				       =		{
              'clip_embeddings_dim': self.text_embedder_hidden_size,
              'time_embed_dim': self.time_embed_dim,
              'cross_attention_dim': self.cross_attention_dim,
          }

          _snake_case			:			str				       =		UnCLIPTextProjModel(**UpperCAmelCase__ )
          return model




      @property
      def 						__UpperCAmelCase      (       self			:		Any ):



          """simple docstring"""





          torch.manual_seed(0 )

          _snake_case			:			Union[str, Any]				       =		{
              '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			:			List[Any]				       =		UNetaDConditionModel(**UpperCAmelCase__ )
          return model




      @property
      def 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          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 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          torch.manual_seed(0 )

          _snake_case			:			Any				       =		UNetaDModel(**self.dummy_super_res_kwargs )
          return model




      @property
      def 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          torch.manual_seed(1 )

          _snake_case			:			List[str]				       =		UNetaDModel(**self.dummy_super_res_kwargs )
          return model




      def 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          _snake_case			:			Optional[Any]				       =		self.dummy_decoder
          _snake_case			:			str				       =		self.dummy_text_proj
          _snake_case			:			Any				       =		self.dummy_text_encoder
          _snake_case			:			Optional[int]				       =		self.dummy_tokenizer
          _snake_case			:			str				       =		self.dummy_super_res_first
          _snake_case			:			Union[str, Any]				       =		self.dummy_super_res_last

          _snake_case			:			Optional[Any]				       =		UnCLIPScheduler(
              variance_type='learned_range'  ,       prediction_type='epsilon'  ,       num_train_timesteps=1000  ,       )

          _snake_case			:			Union[str, Any]				       =		UnCLIPScheduler(
              variance_type='fixed_small_log'  ,       prediction_type='epsilon'  ,       num_train_timesteps=1000  ,       )

          _snake_case			:			List[Any]				       =		CLIPImageProcessor(crop_size=32  ,       size=32 )

          _snake_case			:			Dict				       =		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 						__UpperCAmelCase      (       self			:		Optional[Any]  ,       snake_case			:		int  ,       snake_case			:		Union[str, Any]=0  ,       snake_case			:		Dict=True ):



          """simple docstring"""





          _snake_case			:			Any				       =		floats_tensor((1, 3, 32, 32)  ,       rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
          if str(UpperCAmelCase__ ).startswith('mps' ):
              _snake_case			:			Optional[int]				       =		torch.manual_seed(UpperCAmelCase__ )
          else:
              _snake_case			:			Dict				       =		torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )

          if pil_image:
              _snake_case			:			Optional[int]				       =		input_image * 0.5 + 0.5
              _snake_case			:			Optional[int]				       =		input_image.clamp(0  ,       1 )
              _snake_case			:			Union[str, Any]				       =		input_image.cpu().permute(0  ,       2  ,       3  ,       1 ).float().numpy()
              _snake_case			:			Union[str, Any]				       =		DiffusionPipeline.numpy_to_pil(UpperCAmelCase__ )[0]

          return {
              "image": input_image,
              "generator": generator,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "np",
          }




      def 						__UpperCAmelCase      (       self			:		str ):



          """simple docstring"""





          _snake_case			:			str				       =		'cpu'

          _snake_case			:			str				       =		self.get_dummy_components()

          _snake_case			:			str				       =		self.pipeline_class(**UpperCAmelCase__ )
          _snake_case			:			Optional[int]				       =		pipe.to(UpperCAmelCase__ )

          pipe.set_progress_bar_config(disable=UpperCAmelCase__ )

          _snake_case			:			int				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )

          _snake_case			:			Optional[int]				       =		pipe(**UpperCAmelCase__ )
          _snake_case			:			int				       =		output.images

          _snake_case			:			Dict				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )

          _snake_case			:			Optional[int]				       =		pipe(
              **UpperCAmelCase__  ,       return_dict=UpperCAmelCase__  ,       )[0]

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

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

          _snake_case			:			str				       =		np.array(
              [
                  0.9997,
                  0.0002,
                  0.9997,
                  0.9997,
                  0.9969,
                  0.0023,
                  0.9997,
                  0.9969,
                  0.9970,
              ] )

          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			:		Optional[int] ):



          """simple docstring"""





          _snake_case			:			Union[str, Any]				       =		'cpu'

          _snake_case			:			Optional[Any]				       =		self.get_dummy_components()

          _snake_case			:			Union[str, Any]				       =		self.pipeline_class(**UpperCAmelCase__ )
          _snake_case			:			str				       =		pipe.to(UpperCAmelCase__ )

          pipe.set_progress_bar_config(disable=UpperCAmelCase__ )

          _snake_case			:			str				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )

          _snake_case			:			Any				       =		pipe(**UpperCAmelCase__ )
          _snake_case			:			List[Any]				       =		output.images

          _snake_case			:			List[Any]				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )

          _snake_case			:			Union[str, Any]				       =		pipe(
              **UpperCAmelCase__  ,       return_dict=UpperCAmelCase__  ,       )[0]

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

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

          _snake_case			:			List[str]				       =		np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )

          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			:		Dict ):



          """simple docstring"""





          _snake_case			:			str				       =		'cpu'

          _snake_case			:			Optional[Any]				       =		self.get_dummy_components()

          _snake_case			:			Union[str, Any]				       =		self.pipeline_class(**UpperCAmelCase__ )
          _snake_case			:			List[Any]				       =		pipe.to(UpperCAmelCase__ )

          pipe.set_progress_bar_config(disable=UpperCAmelCase__ )

          _snake_case			:			Dict				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )
          _snake_case			:			Optional[int]				       =		[
              pipeline_inputs['image'],
              pipeline_inputs['image'],
          ]

          _snake_case			:			Any				       =		pipe(**UpperCAmelCase__ )
          _snake_case			:			str				       =		output.images

          _snake_case			:			Union[str, Any]				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )
          _snake_case			:			Union[str, Any]				       =		[
              tuple_pipeline_inputs['image'],
              tuple_pipeline_inputs['image'],
          ]

          _snake_case			:			List[Any]				       =		pipe(
              **UpperCAmelCase__  ,       return_dict=UpperCAmelCase__  ,       )[0]

          _snake_case			:			Optional[Any]				       =		image[0, -3:, -3:, -1]
          _snake_case			:			List[str]				       =		image_from_tuple[0, -3:, -3:, -1]

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

          _snake_case			:			Dict				       =		np.array(
              [
                  0.9997,
                  0.9989,
                  0.0008,
                  0.0021,
                  0.9960,
                  0.0018,
                  0.0014,
                  0.0002,
                  0.9933,
              ] )

          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			:		Union[str, Any] ):



          """simple docstring"""





          _snake_case			:			int				       =		torch.device('cpu' )

          class       SCREAMING_SNAKE_CASE       :






                '''simple docstring'''






                SCREAMING_SNAKE_CASE__			:					Dict     =					1



          _snake_case			:			Optional[Any]				       =		self.get_dummy_components()

          _snake_case			:			Union[str, Any]				       =		self.pipeline_class(**UpperCAmelCase__ )
          _snake_case			:			Tuple				       =		pipe.to(UpperCAmelCase__ )

          pipe.set_progress_bar_config(disable=UpperCAmelCase__ )

          _snake_case			:			Dict				       =		torch.Generator(device=UpperCAmelCase__ ).manual_seed(0 )
          _snake_case			:			Any				       =		pipe.decoder.dtype
          _snake_case			:			Dict				       =		1

          _snake_case			:			str				       =		(
              batch_size,
              pipe.decoder.config.in_channels,
              pipe.decoder.config.sample_size,
              pipe.decoder.config.sample_size,
          )
          _snake_case			:			Optional[Any]				       =		pipe.prepare_latents(
              UpperCAmelCase__  ,       dtype=UpperCAmelCase__  ,       device=UpperCAmelCase__  ,       generator=UpperCAmelCase__  ,       latents=UpperCAmelCase__  ,       scheduler=DummyScheduler() )

          _snake_case			:			Union[str, Any]				       =		(
              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			:			List[Any]				       =		pipe.prepare_latents(
              UpperCAmelCase__  ,       dtype=UpperCAmelCase__  ,       device=UpperCAmelCase__  ,       generator=UpperCAmelCase__  ,       latents=UpperCAmelCase__  ,       scheduler=DummyScheduler() )

          _snake_case			:			List[Any]				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )

          _snake_case			:			List[str]				       =		pipe(
              **UpperCAmelCase__  ,       decoder_latents=UpperCAmelCase__  ,       super_res_latents=UpperCAmelCase__ ).images

          _snake_case			:			Optional[int]				       =		self.get_dummy_inputs(UpperCAmelCase__  ,       pil_image=UpperCAmelCase__ )
          # Don't pass image, instead pass embedding
          _snake_case			:			Optional[Any]				       =		pipeline_inputs.pop('image' )
          _snake_case			:			Optional[int]				       =		pipe.image_encoder(UpperCAmelCase__ ).image_embeds

          _snake_case			:			List[Any]				       =		pipe(
              **UpperCAmelCase__  ,       decoder_latents=UpperCAmelCase__  ,       super_res_latents=UpperCAmelCase__  ,       image_embeddings=UpperCAmelCase__  ,       ).images

          # make sure passing text embeddings manually is identical
          assert np.abs(img_out_a - img_out_a ).max() < 1e-4




      @skip_mps
      def 						__UpperCAmelCase      (       self			:		Tuple ):



          """simple docstring"""





          _snake_case			:			List[str]				       =		torch_device == 'cpu'

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

          self._test_attention_slicing_forward_pass(
              test_max_difference=UpperCAmelCase__  ,       expected_max_diff=UpperCAmelCase__ )




      @skip_mps
      def 						__UpperCAmelCase      (       self			:		List[str] ):



          """simple docstring"""





          _snake_case			:			Tuple				       =		torch_device == 'cpu'
          _snake_case			:			List[Any]				       =		True
          _snake_case			:			Union[str, Any]				       =		[
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]

          self._test_inference_batch_single_identical(
              test_max_difference=UpperCAmelCase__  ,       relax_max_difference=UpperCAmelCase__  ,       additional_params_copy_to_batched_inputs=UpperCAmelCase__  ,       )




      def 						__UpperCAmelCase      (       self			:		Any ):



          """simple docstring"""





          _snake_case			:			Dict				       =		[
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]

          if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              _snake_case			:			Tuple				       =		[2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=UpperCAmelCase__  ,       additional_params_copy_to_batched_inputs=UpperCAmelCase__  ,       )
          else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=UpperCAmelCase__ )




      @skip_mps
      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          return super().test_dict_tuple_outputs_equivalent()




      @skip_mps
      def 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          return super().test_save_load_local()




      @skip_mps
      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





          return super().test_save_load_optional_components()



@slow
@require_torch_gpu
class       SCREAMING_SNAKE_CASE       (				unittest.TestCase       ):






      '''simple docstring'''






      def 						__UpperCAmelCase      (       self			:		Any ):



          """simple docstring"""





          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          _snake_case			:			Optional[Any]				       =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
          _snake_case			:			str				       =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )

          _snake_case			:			str				       =		UnCLIPImageVariationPipeline.from_pretrained(
              'kakaobrain/karlo-v1-alpha-image-variations'  ,       torch_dtype=torch.floataa )
          _snake_case			:			List[Any]				       =		pipeline.to(UpperCAmelCase__ )
          pipeline.set_progress_bar_config(disable=UpperCAmelCase__ )

          _snake_case			:			Any				       =		torch.Generator(device='cpu' ).manual_seed(0 )
          _snake_case			:			Tuple				       =		pipeline(
              UpperCAmelCase__  ,       generator=UpperCAmelCase__  ,       output_type='np'  ,       )

          _snake_case			:			Any				       =		output.images[0]

          assert image.shape == (256, 256, 3)

          assert_mean_pixel_difference(UpperCAmelCase__  ,       UpperCAmelCase__  ,       15 )









from __future__ import annotations

import requests

_lowerCamelCase      :						str							=  set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = 1					,							lowercase_ = "new"					,							lowercase_ = None		)  ->			dict:



       """simple docstring"""

       A__ 					=       wanted_data or []
       if invalid_search_terms := ", ".join(sorted(set(lowercase_		) - valid_terms		)		):
              A__ 					=       f"""Invalid search term: {invalid_search_terms}"""
              raise ValueError(lowercase_		)
       A__ 					=       requests.get(
           f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""					,							headers={'''User-agent''': '''A random string'''}					,							)
       if response.status_code == 429:
              raise requests.HTTPError

       A__ 					=       response.json()
       if not wanted_data:
              return {id_: data["data"]["children"][id_] for id_ in range(lowercase_		)}

       A__ 					=       {}
       for id_ in range(lowercase_		):
              A__ 					=       {
                  item: data['''data''']['''children'''][id_]['''data'''][item] for item in wanted_data
              }
       return data_dict


if __name__ == "__main__":
  # If you get Error 429, that means you are rate limited.Try after some time
  print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))


