
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 							A__		(     __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
						_UpperCAmelCase    :   Any   	=      KandinskyPipeline
						_UpperCAmelCase    :   Any   	=      [
						    """prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						]
						_UpperCAmelCase    :   Union[str, Any]   	=      ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
						_UpperCAmelCase    :   Dict   	=      [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_UpperCAmelCase    :   List[Any]   	=      False
						@property
						def 						UpperCamelCase__							(				self				):
							return 3_2
						@property
						def 						UpperCamelCase__							(				self				):
							return 3_2
						@property
						def 						UpperCamelCase__							(				self				):
							return self.time_input_dim
						@property
						def 						UpperCamelCase__							(				self				):
							return self.time_input_dim * 4
						@property
						def 						UpperCamelCase__							(				self				):
							return 1_0_0
						@property
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[Any]          =				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
							return tokenizer
						@property
						def 						UpperCamelCase__							(				self				):
							torch.manual_seed(0				)
							lowerCamelCase		:     Union[str, Any]          =				MCLIPConfig(
							    numDims=self.cross_attention_dim   ,  transformerDimensions=self.text_embedder_hidden_size   ,  hidden_size=self.text_embedder_hidden_size   ,  intermediate_size=3_7   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  vocab_size=1_0_0_5   ,  )

							lowerCamelCase		:     Dict          =				MultilingualCLIP(__magic_name__				)
							lowerCamelCase		:     Optional[Any]          =				text_encoder.eval()

							return text_encoder
						@property
						def 						UpperCamelCase__							(				self				):
							torch.manual_seed(0				)

							lowerCamelCase		:     Any          =				{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """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,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							lowerCamelCase		:     Optional[int]          =				UNetaDConditionModel(**__magic_name__				)
							return model
						@property
						def 						UpperCamelCase__							(				self				):
							return {
							    "block_out_channels": [3_2, 6_4],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 1_2,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}
						@property
						def 						UpperCamelCase__							(				self				):
							torch.manual_seed(0				)
							lowerCamelCase		:     Dict          =				VQModel(**self.dummy_movq_kwargs				)
							return model
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Tuple          =				self.dummy_text_encoder
							lowerCamelCase		:     int          =				self.dummy_tokenizer
							lowerCamelCase		:     Optional[int]          =				self.dummy_unet
							lowerCamelCase		:     str          =				self.dummy_movq

							lowerCamelCase		:     Optional[int]          =				DDIMScheduler(
							    num_train_timesteps=1_0_0_0   ,  beta_schedule="""linear"""   ,  beta_start=0.00_085   ,  beta_end=0.012   ,  clip_sample=__magic_name__   ,  set_alpha_to_one=__magic_name__   ,  steps_offset=1   ,  prediction_type="""epsilon"""   ,  thresholding=__magic_name__   ,  )

							lowerCamelCase		:     Any          =				{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__=0				):
							lowerCamelCase		:     str          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(__magic_name__				)				).to(__magic_name__				)
							lowerCamelCase		:     Optional[int]          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(seed + 1				)				).to(__magic_name__				)
							if str(__magic_name__				).startswith("""mps"""				):
								lowerCamelCase		:     Union[str, Any]          =				torch.manual_seed(__magic_name__				)
							else:
								lowerCamelCase		:     Tuple          =				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
							lowerCamelCase		:     Optional[int]          =				{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 6_4,
							    """width""": 6_4,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs






						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Optional[int]          =				"""cpu"""

							lowerCamelCase		:     str          =				self.get_dummy_components()

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

							pipe.set_progress_bar_config(disable=__magic_name__				)

							lowerCamelCase		:     str          =				pipe(**self.get_dummy_inputs(__magic_name__				)				)
							lowerCamelCase		:     str          =				output.images

							lowerCamelCase		:     Dict          =				pipe(
							    **self.get_dummy_inputs(__magic_name__				)   ,  return_dict=__magic_name__   ,  )[0]

							lowerCamelCase		:     int          =				image[0, -3:, -3:, -1]
							lowerCamelCase		:     Tuple          =				image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)

							lowerCamelCase		:     int          =				np.array(
							    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]				)

							assert (
							    np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
							), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
							), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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






						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Tuple          =				load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""				)

							lowerCamelCase		:     List[Any]          =				KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""   ,  torch_dtype=torch.floataa				)
							pipe_prior.to(__magic_name__				)

							lowerCamelCase		:     List[str]          =				KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""   ,  torch_dtype=torch.floataa				)
							lowerCamelCase		:     str          =				pipeline.to(__magic_name__				)
							pipeline.set_progress_bar_config(disable=__magic_name__				)

							lowerCamelCase		:     Dict          =				"""red cat, 4k photo"""

							lowerCamelCase		:     Tuple          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
							lowerCamelCase		:     Any          =				pipe_prior(
							    __magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=5   ,  negative_prompt=""""""   ,  ).to_tuple()

							lowerCamelCase		:     Dict          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
							lowerCamelCase		:     Optional[Any]          =				pipeline(
							    __magic_name__   ,  image_embeds=__magic_name__   ,  negative_image_embeds=__magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=1_0_0   ,  output_type="""np"""   ,  )

							lowerCamelCase		:     str          =				output.images[0]

							assert image.shape == (5_1_2, 5_1_2, 3)

							assert_mean_pixel_difference(__magic_name__   ,  __magic_name__				)






from collections import defaultdict





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     defaultdict[str, int]          =				defaultdict(lowerCamelCase							)
 # 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}''')




