
"""simple docstring"""

import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	KandinskyVaaPriorEmbaEmbPipeline
	lowercase_        =	['prompt', 'image']
	lowercase_        =	['prompt', 'image']
	lowercase_        =	[
	    'num_images_per_prompt',
	    'strength',
	    'generator',
	    'num_inference_steps',
	    'latents',
	    'negative_prompt',
	    'guidance_scale',
	    'output_type',
	    'return_dict',
	]
	lowercase_        =	False




	@property
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return 32




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			return 32




	@property
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			return self.time_input_dim




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			return self.time_input_dim * 4




	@property
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			return 100




	@property
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			str		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
			return tokenizer




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			torch.manual_seed(0					)
			lowerCamelCase     :			List[str]		=    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-0_5		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1000		, )
			return CLIPTextModelWithProjection(UpperCAmelCase_					)




	@property
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			torch.manual_seed(0					)

			lowerCamelCase     :			Union[str, Any]		=    {
			    'num_attention_heads': 2,
			    'attention_head_dim': 12,
			    'embedding_dim': self.text_embedder_hidden_size,
			    'num_layers': 1,
			}

			lowerCamelCase     :			List[str]		=    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
			lowerCamelCase     :			List[str]		=    nn.Parameter(torch.ones(model.clip_std.shape					)					)
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			torch.manual_seed(0					)
			lowerCamelCase     :			List[str]		=    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		, )

			lowerCamelCase     :			str		=    CLIPVisionModelWithProjection(UpperCAmelCase_					)
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Union[str, Any]		=    CLIPImageProcessor(
			    crop_size=224		, do_center_crop=UpperCAmelCase_		, do_normalize=UpperCAmelCase_		, do_resize=UpperCAmelCase_		, image_mean=[0.48145466, 0.4578275, 0.40821073]		, image_std=[0.26862954, 0.26130258, 0.27577711]		, resample=3		, size=224		, )

			return image_processor




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Tuple		=    self.dummy_prior
			lowerCamelCase     :			Optional[int]		=    self.dummy_image_encoder
			lowerCamelCase     :			Optional[Any]		=    self.dummy_text_encoder
			lowerCamelCase     :			Optional[int]		=    self.dummy_tokenizer
			lowerCamelCase     :			str		=    self.dummy_image_processor

			lowerCamelCase     :			Dict		=    UnCLIPScheduler(
			    variance_type='fixed_small_log'		, prediction_type='sample'		, num_train_timesteps=1000		, clip_sample=UpperCAmelCase_		, clip_sample_range=10.0		, )

			lowerCamelCase     :			str		=    {
			    '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					)		->							List[str]:
			if str(UpperCAmelCase_					).startswith('mps'					):
					lowerCamelCase     :			List[str]		=    torch.manual_seed(UpperCAmelCase_					)
			else:
					lowerCamelCase     :			Union[str, Any]		=    torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)

			lowerCamelCase     :			Union[str, Any]		=    floats_tensor((1, 3, 64, 64)		, rng=random.Random(UpperCAmelCase_					)					).to(UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    image.cpu().permute(0		, 2		, 3		, 1					)[0]
			lowerCamelCase     :			Tuple		=    Image.fromarray(np.uinta(UpperCAmelCase_					)					).convert('RGB'					).resize((256, 256)					)

			lowerCamelCase     :			List[Any]		=    {
			    'prompt': 'horse',
			    'image': init_image,
			    'strength': 0.5,
			    'generator': generator,
			    'guidance_scale': 4.0,
			    'num_inference_steps': 2,
			    'output_type': 'np',
			}
			return inputs




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Dict		=    'cpu'

			lowerCamelCase     :			int		=    self.get_dummy_components()

			lowerCamelCase     :			Optional[Any]		=    self.pipeline_class(**UpperCAmelCase_					)
			lowerCamelCase     :			Optional[Any]		=    pipe.to(UpperCAmelCase_					)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    pipe(**self.get_dummy_inputs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Union[str, Any]		=    output.image_embeds

			lowerCamelCase     :			List[Any]		=    pipe(
			    **self.get_dummy_inputs(UpperCAmelCase_					)		, return_dict=UpperCAmelCase_		, )[0]

			lowerCamelCase     :			List[Any]		=    image[0, -10:]
			lowerCamelCase     :			Optional[int]		=    image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			lowerCamelCase     :			List[str]		=    np.array(
			    [
			        0.1071284,
			        1.3330271,
			        0.61260223,
			        -0.6691065,
			        -0.3846852,
			        -1.0303661,
			        0.22716111,
			        0.03348901,
			        0.30040675,
			        -0.24805029,
			    ]					)

			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					)		->							str:
			lowerCamelCase     :			List[str]		=    torch_device == 'cpu'
			lowerCamelCase     :			List[Any]		=    True
			lowerCamelCase     :			List[Any]		=    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					)		->							Dict:
			lowerCamelCase     :			Union[str, Any]		=    torch_device == 'cpu'
			lowerCamelCase     :			Optional[int]		=    False

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










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


lowerCAmelCase__       :	Any     				=logging.get_logger(__name__)

lowerCAmelCase__       :	Union[str, Any]     				={
    'google/reformer-crime-and-punishment': (
        'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'
    ),
    'google/reformer-enwik8': 'https://huggingface.co/google/reformer-enwik8/resolve/main/config.json',
}







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""reformer"""
							_UpperCAmelCase     =		["""past_buckets_states"""]
							_UpperCAmelCase     =		{}






							def __init__(	self       ,		lowerCAmelCase__=6_4       ,		lowerCAmelCase__=["local", "lsh", "local", "lsh", "local", "lsh"]       ,		lowerCAmelCase__=1.0       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=[6_4, 6_4]       ,		lowerCAmelCase__=[6_4, 1_9_2]       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=5_1_2       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__="relu"       ,		lowerCAmelCase__=0.05       ,		lowerCAmelCase__=2_5_6       ,		lowerCAmelCase__=0.02       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=1E-12       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=0.05       ,		lowerCAmelCase__=6_4       ,		lowerCAmelCase__=6_4       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=4_0_9_6       ,		lowerCAmelCase__=1_2       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=3_2_0       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=None       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= hash_seed
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= vocab_size
										SCREAMING_SNAKE_CASE_						:			Any       						= attention_head_size
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= hidden_size
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= num_attention_heads
										SCREAMING_SNAKE_CASE_						:			Any       						= num_hashes
										SCREAMING_SNAKE_CASE_						:			int       						= len(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= tuple(lowerCAmelCase__    ) if isinstance(lowerCAmelCase__       ,		lowerCAmelCase__    ) else num_buckets
										SCREAMING_SNAKE_CASE_						:			int       						= lsh_attn_chunk_length
										SCREAMING_SNAKE_CASE_						:			List[str]       						= local_attn_chunk_length
										SCREAMING_SNAKE_CASE_						:			Tuple       						= lsh_num_chunks_after
										SCREAMING_SNAKE_CASE_						:			str       						= lsh_num_chunks_before
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= local_num_chunks_after
										SCREAMING_SNAKE_CASE_						:			Any       						= local_num_chunks_before
										SCREAMING_SNAKE_CASE_						:			Any       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Dict       						= feed_forward_size
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= hidden_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Tuple       						= lsh_attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Any       						= local_attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			str       						= max_position_embeddings
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			List[str]       						= axial_pos_embds
										SCREAMING_SNAKE_CASE_						:			List[str]       						= tuple(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= tuple(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= axial_norm_std
										SCREAMING_SNAKE_CASE_						:			Any       						= chunk_size_lm_head
										SCREAMING_SNAKE_CASE_						:			int       						= attn_layers
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= use_cache
										SCREAMING_SNAKE_CASE_						:			Tuple       						= classifier_dropout
										super().__init__(
										    pad_token_id=lowerCAmelCase__       ,		eos_token_id=lowerCAmelCase__       ,		is_decoder=lowerCAmelCase__       ,		tie_word_embeddings=lowerCAmelCase__       ,		**lowerCAmelCase__       ,		)





