




"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class        UpperCAmelCase_  (	snake_case ,			unittest.TestCase						):
		UpperCamelCase									=UnCLIPPipeline
		UpperCamelCase									=TEXT_TO_IMAGE_PARAMS - {
		    "negative_prompt",
		    "height",
		    "width",
		    "negative_prompt_embeds",
		    "guidance_scale",
		    "prompt_embeds",
		    "cross_attention_kwargs",
		}
		UpperCamelCase									=TEXT_TO_IMAGE_BATCH_PARAMS
		UpperCamelCase									=[
		    "generator",
		    "return_dict",
		    "prior_num_inference_steps",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]
		UpperCamelCase									=False
		@property
		def 					_lowerCamelCase    (		self   )		->   str:
					return 32
		@property
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					return 32
		@property
		def 					_lowerCamelCase    (		self   )		->   Dict:
					return self.time_input_dim
		@property
		def 					_lowerCamelCase    (		self   )		->   Any:
					return self.time_input_dim * 4
		@property
		def 					_lowerCamelCase    (		self   )		->   str:
					return 1_00
		@property
		def 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       List[Any]				=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
					return tokenizer
		@property
		def 					_lowerCamelCase    (		self   )		->   Any:
					torch.manual_seed(0   )
					__lowercase	:       List[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=10_00							,		)
					return CLIPTextModelWithProjection(UpperCamelCase_   )
		@property
		def 					_lowerCamelCase    (		self   )		->   str:
					torch.manual_seed(0   )

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

					__lowercase	:       str				=   PriorTransformer(**UpperCamelCase_   )
					return model
		@property
		def 					_lowerCamelCase    (		self   )		->   int:
					torch.manual_seed(0   )

					__lowercase	:       str				=   {
					    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
					    '''time_embed_dim''': self.time_embed_dim,
					    '''cross_attention_dim''': self.cross_attention_dim,
					}

					__lowercase	:       Union[str, Any]				=   UnCLIPTextProjModel(**UpperCamelCase_   )
					return model
		@property
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					torch.manual_seed(0   )

					__lowercase	:       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''',
					}

					__lowercase	:       Dict				=   UNetaDConditionModel(**UpperCamelCase_   )
					return model
		@property
		def 					_lowerCamelCase    (		self   )		->   Dict:
					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 					_lowerCamelCase    (		self   )		->   Dict:
					torch.manual_seed(0   )

					__lowercase	:       List[str]				=   UNetaDModel(**self.dummy_super_res_kwargs   )
					return model
		@property
		def 					_lowerCamelCase    (		self   )		->   Any:
					# seeded differently to get different unet than `self.dummy_super_res_first`
					torch.manual_seed(1   )

					__lowercase	:       Dict				=   UNetaDModel(**self.dummy_super_res_kwargs   )
					return model
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       Optional[Any]				=   self.dummy_prior
					__lowercase	:       Union[str, Any]				=   self.dummy_decoder
					__lowercase	:       List[str]				=   self.dummy_text_proj
					__lowercase	:       Optional[Any]				=   self.dummy_text_encoder
					__lowercase	:       Dict				=   self.dummy_tokenizer
					__lowercase	:       Dict				=   self.dummy_super_res_first
					__lowercase	:       Dict				=   self.dummy_super_res_last

					__lowercase	:       Any				=   UnCLIPScheduler(
					    variance_type='''fixed_small_log'''							,		prediction_type='''sample'''							,		num_train_timesteps=10_00							,		clip_sample_range=5.0							,		)

					__lowercase	:       Optional[Any]				=   UnCLIPScheduler(
					    variance_type='''learned_range'''							,		prediction_type='''epsilon'''							,		num_train_timesteps=10_00							,		)

					__lowercase	:       Optional[Any]				=   UnCLIPScheduler(
					    variance_type='''fixed_small_log'''							,		prediction_type='''epsilon'''							,		num_train_timesteps=10_00							,		)

					__lowercase	:       str				=   {
					    '''prior''': prior,
					    '''decoder''': decoder,
					    '''text_proj''': text_proj,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''super_res_first''': super_res_first,
					    '''super_res_last''': super_res_last,
					    '''prior_scheduler''': prior_scheduler,
					    '''decoder_scheduler''': decoder_scheduler,
					    '''super_res_scheduler''': super_res_scheduler,
					}

					return components
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=0   )		->   List[str]:
					if str(UpperCamelCase_   ).startswith('''mps'''   ):
								__lowercase	:       List[Any]				=   torch.manual_seed(UpperCamelCase_   )
					else:
								__lowercase	:       Tuple				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
					__lowercase	:       int				=   {
					    '''prompt''': '''horse''',
					    '''generator''': generator,
					    '''prior_num_inference_steps''': 2,
					    '''decoder_num_inference_steps''': 2,
					    '''super_res_num_inference_steps''': 2,
					    '''output_type''': '''numpy''',
					}
					return inputs
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       Dict				=   '''cpu'''

					__lowercase	:       Dict				=   self.get_dummy_components()

					__lowercase	:       Optional[int]				=   self.pipeline_class(**UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   pipe.to(UpperCamelCase_   )

					pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       List[str]				=   pipe(**self.get_dummy_inputs(UpperCamelCase_   )   )
					__lowercase	:       List[Any]				=   output.images

					__lowercase	:       Any				=   pipe(
					    **self.get_dummy_inputs(UpperCamelCase_   )							,		return_dict=UpperCamelCase_							,		)[0]

					__lowercase	:       Union[str, Any]				=   image[0, -3:, -3:, -1]
					__lowercase	:       Optional[int]				=   image_from_tuple[0, -3:, -3:, -1]

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

					__lowercase	:       Union[str, Any]				=   np.array(
					    [
					        0.9_9_9_7,
					        0.9_9_8_8,
					        0.0_0_2_8,
					        0.9_9_9_7,
					        0.9_9_8_4,
					        0.9_9_6_5,
					        0.0_0_2_9,
					        0.9_9_8_6,
					        0.0_0_2_5,
					    ]   )

					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 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       int				=   torch.device('''cpu'''   )

					class        UpperCAmelCase_  :
							UpperCamelCase									=1


					__lowercase	:       str				=   self.get_dummy_components()

					__lowercase	:       str				=   self.pipeline_class(**UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   pipe.to(UpperCamelCase_   )

					__lowercase	:       Dict				=   components['''prior''']
					__lowercase	:       str				=   components['''decoder''']
					__lowercase	:       Optional[Any]				=   components['''super_res_first''']
					__lowercase	:       List[Any]				=   components['''tokenizer''']
					__lowercase	:       Optional[Any]				=   components['''text_encoder''']

					__lowercase	:       Any				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
					__lowercase	:       Dict				=   prior.dtype
					__lowercase	:       str				=   1

					__lowercase	:       Tuple				=   (batch_size, prior.config.embedding_dim)
					__lowercase	:       str				=   pipe.prepare_latents(
					    UpperCamelCase_							,		dtype=UpperCamelCase_							,		device=UpperCamelCase_							,		generator=UpperCamelCase_							,		latents=UpperCamelCase_							,		scheduler=DummyScheduler()   )
					__lowercase	:       Any				=   (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
					__lowercase	:       Optional[Any]				=   pipe.prepare_latents(
					    UpperCamelCase_							,		dtype=UpperCamelCase_							,		device=UpperCamelCase_							,		generator=UpperCamelCase_							,		latents=UpperCamelCase_							,		scheduler=DummyScheduler()   )

					__lowercase	:       int				=   (
					    batch_size,
					    super_res_first.config.in_channels // 2,
					    super_res_first.config.sample_size,
					    super_res_first.config.sample_size,
					)
					__lowercase	:       Union[str, Any]				=   pipe.prepare_latents(
					    UpperCamelCase_							,		dtype=UpperCamelCase_							,		device=UpperCamelCase_							,		generator=UpperCamelCase_							,		latents=UpperCamelCase_							,		scheduler=DummyScheduler()   )

					pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   '''this is a prompt example'''

					__lowercase	:       List[str]				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
					__lowercase	:       Any				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		prior_num_inference_steps=2							,		decoder_num_inference_steps=2							,		super_res_num_inference_steps=2							,		prior_latents=UpperCamelCase_							,		decoder_latents=UpperCamelCase_							,		super_res_latents=UpperCamelCase_							,		output_type='''np'''							,		)
					__lowercase	:       int				=   output.images

					__lowercase	:       int				=   tokenizer(
					    UpperCamelCase_							,		padding='''max_length'''							,		max_length=tokenizer.model_max_length							,		return_tensors='''pt'''							,		)
					__lowercase	:       Tuple				=   text_encoder(text_inputs.input_ids   )
					__lowercase	:       Union[str, Any]				=   text_inputs.attention_mask

					__lowercase	:       int				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
					__lowercase	:       Optional[Any]				=   pipe(
					    generator=UpperCamelCase_							,		prior_num_inference_steps=2							,		decoder_num_inference_steps=2							,		super_res_num_inference_steps=2							,		prior_latents=UpperCamelCase_							,		decoder_latents=UpperCamelCase_							,		super_res_latents=UpperCamelCase_							,		text_model_output=UpperCamelCase_							,		text_attention_mask=UpperCamelCase_							,		output_type='''np'''							,		)[0]

					# make sure passing text embeddings manually is identical
					assert np.abs(image - image_from_text   ).max() < 1E-4
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       Optional[int]				=   torch_device == '''cpu'''

					self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_							,		expected_max_diff=0.0_1   )
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       List[Any]				=   torch_device == '''cpu'''
					__lowercase	:       Optional[Any]				=   True
					__lowercase	:       Any				=   [
					    '''prior_num_inference_steps''',
					    '''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 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       int				=   [
					    '''prior_num_inference_steps''',
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]

					if torch_device == "mps":
								# TODO: MPS errors with larger batch sizes
								__lowercase	:       Optional[int]				=   [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 					_lowerCamelCase    (		self   )		->   Optional[int]:
					return super().test_dict_tuple_outputs_equivalent()
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					return super().test_save_load_local()
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					return super().test_save_load_optional_components()


@nightly
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   str:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       List[Any]				=   load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/unclip/karlo_v1_alpha_horse_cpu.npy'''   )

					__lowercase	:       int				=   UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''   )
					pipeline.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       List[Any]				=   torch.manual_seed(0   )
					__lowercase	:       List[str]				=   pipeline(
					    '''horse'''							,		num_images_per_prompt=1							,		generator=UpperCamelCase_							,		output_type='''np'''							,		)

					__lowercase	:       Tuple				=   output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image   ).max() < 1E-1


@slow
@require_torch_gpu
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Dict:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       Optional[int]				=   load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/unclip/karlo_v1_alpha_horse_fp16.npy'''   )

					__lowercase	:       Tuple				=   UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''							,		torch_dtype=torch.floataa   )
					__lowercase	:       Any				=   pipeline.to(UpperCamelCase_   )
					pipeline.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       str				=   torch.Generator(device='''cpu'''   ).manual_seed(0   )
					__lowercase	:       Dict				=   pipeline(
					    '''horse'''							,		generator=UpperCamelCase_							,		output_type='''np'''							,		)

					__lowercase	:       Optional[Any]				=   output.images[0]

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

					assert_mean_pixel_difference(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					__lowercase	:       int				=   UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''							,		torch_dtype=torch.floataa   )
					__lowercase	:       Union[str, Any]				=   pipe.to(UpperCamelCase_   )
					pipe.set_progress_bar_config(disable=UpperCamelCase_   )
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					__lowercase	:       Tuple				=   pipe(
					    '''horse'''							,		num_images_per_prompt=1							,		prior_num_inference_steps=2							,		decoder_num_inference_steps=2							,		super_res_num_inference_steps=2							,		output_type='''np'''							,		)

					__lowercase	:       str				=   torch.cuda.max_memory_allocated()
					# make sure that less than 7 GB is allocated
					assert mem_bytes < 7 * 10**9









"""simple docstring"""




from collections import defaultdict







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       defaultdict[str, int]				=   defaultdict(__UpperCamelCase					)
			# Creating a dictionary containing count of each word
			for word in sentence.split():
						occurrence[word] += 1
			return occurrence


if __name__ == "__main__":
			for word, count in word_occurrence('INPUT STRING').items():
						print(F"{word}: {count}")



