





"""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    (  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  UnCLIPPipeline
      lowercase_ :					Optional[Any]             =  TEXT_TO_IMAGE_PARAMS - {
          """negative_prompt""",
          """height""",
          """width""",
          """negative_prompt_embeds""",
          """guidance_scale""",
          """prompt_embeds""",
          """cross_attention_kwargs""",
      }
      lowercase_ :					Optional[int]             =  TEXT_TO_IMAGE_BATCH_PARAMS
      lowercase_ :					Any             =  [
          """generator""",
          """return_dict""",
          """prior_num_inference_steps""",
          """decoder_num_inference_steps""",
          """super_res_num_inference_steps""",
      ]
      lowercase_ :					Union[str, Any]             =  False

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim * 4

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1_0_0

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
        return tokenizer

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )
        A_    :			int									=    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=3_7   , layer_norm_eps=1E-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=1_0_0_0   , )
        return CLIPTextModelWithProjection(snake_case_    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			int									=    {
            'num_attention_heads': 2,
            'attention_head_dim': 1_2,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        A_    :			int									=    PriorTransformer(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Optional[Any]									=    {
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        A_    :			Tuple									=    UnCLIPTextProjModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			str									=    {
            'sample_size': 3_2,
            # 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',
        }

        A_    :			Tuple									=    UNetaDConditionModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return {
            "sample_size": 6_4,
            "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    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Union[str, Any]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(1    )

        A_    :			Optional[int]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    self.dummy_prior
        A_    :			Tuple									=    self.dummy_decoder
        A_    :			int									=    self.dummy_text_proj
        A_    :			Tuple									=    self.dummy_text_encoder
        A_    :			Optional[Any]									=    self.dummy_tokenizer
        A_    :			Any									=    self.dummy_super_res_first
        A_    :			Optional[Any]									=    self.dummy_super_res_last

        A_    :			Optional[int]									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='sample'   , num_train_timesteps=1_0_0_0   , clip_sample_range=5.0   , )

        A_    :			Tuple									=    UnCLIPScheduler(
            variance_type='learned_range'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			Any									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			int									=    {
            '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   , snake_case_   , snake_case_=0    ):

        """simple docstring"""

        if str(snake_case_    ).startswith('mps'    ):
          A_    :			Tuple									=    torch.manual_seed(snake_case_    )
        else:
          A_    :			Dict									=    torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
        A_    :			Optional[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    ):

        """simple docstring"""

        A_    :			List[Any]									=    'cpu'

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

        A_    :			Any									=    self.pipeline_class(**snake_case_    )
        A_    :			Dict									=    pipe.to(snake_case_    )

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    pipe(**self.get_dummy_inputs(snake_case_    )    )
        A_    :			int									=    output.images

        A_    :			Union[str, Any]									=    pipe(
            **self.get_dummy_inputs(snake_case_    )   , return_dict=snake_case_   , )[0]

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

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

        A_    :			Union[str, Any]									=    np.array(
            [
                0.99_97,
                0.99_88,
                0.00_28,
                0.99_97,
                0.99_84,
                0.99_65,
                0.00_29,
                0.99_86,
                0.00_25,
            ]    )

        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    ):

        """simple docstring"""

        A_    :			List[Any]									=    torch.device('cpu'    )

        class 					_UpperCAmelCase    :



              '''simple docstring'''
              lowercase_ :					Any             =  1


        A_    :			List[Any]									=    self.get_dummy_components()

        A_    :			List[str]									=    self.pipeline_class(**snake_case_    )
        A_    :			Any									=    pipe.to(snake_case_    )

        A_    :			int									=    components['prior']
        A_    :			Optional[int]									=    components['decoder']
        A_    :			Any									=    components['super_res_first']
        A_    :			List[Any]									=    components['tokenizer']
        A_    :			List[str]									=    components['text_encoder']

        A_    :			Any									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[Any]									=    prior.dtype
        A_    :			Dict									=    1

        A_    :			Any									=    (batch_size, prior.config.embedding_dim)
        A_    :			List[Any]									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )
        A_    :			Tuple									=    (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        A_    :			Any									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )

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

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			Union[str, Any]									=    'this is a prompt example'

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

        A_    :			Any									=    tokenizer(
            snake_case_   , padding='max_length'   , max_length=tokenizer.model_max_length   , return_tensors='pt'   , )
        A_    :			List[str]									=    text_encoder(text_inputs.input_ids    )
        A_    :			List[str]									=    text_inputs.attention_mask

        A_    :			int									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[int]									=    pipe(
            generator=snake_case_   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , prior_latents=snake_case_   , decoder_latents=snake_case_   , super_res_latents=snake_case_   , text_model_output=snake_case_   , text_attention_mask=snake_case_   , 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    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'

        self._test_attention_slicing_forward_pass(test_max_difference=snake_case_   , expected_max_diff=0.01    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'
        A_    :			Optional[Any]									=    True
        A_    :			List[str]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
          # TODO: MPS errors with larger batch sizes
          A_    :			List[str]									=    [2, 3]
          self._test_inference_batch_consistent(
              batch_sizes=snake_case_   , additional_params_copy_to_batched_inputs=snake_case_   , )
        else:
          self._test_inference_batch_consistent(
              additional_params_copy_to_batched_inputs=snake_case_    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_dict_tuple_outputs_equivalent()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_local()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_optional_components()


@nightly
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_cpu.npy'    )

        A_    :			str									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			Any									=    torch.manual_seed(0    )
        A_    :			str									=    pipeline(
            'horse'   , num_images_per_prompt=1   , generator=snake_case_   , output_type='np'   , )

        A_    :			int									=    output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)
        assert np.abs(expected_image - image    ).max() < 1E-1


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



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_fp16.npy'    )

        A_    :			int									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			str									=    pipeline.to(snake_case_    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    torch.Generator(device='cpu'    ).manual_seed(0    )
        A_    :			Any									=    pipeline(
            'horse'   , generator=snake_case_   , output_type='np'   , )

        A_    :			List[str]									=    output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)

        assert_mean_pixel_difference(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A_    :			List[Any]									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			Dict									=    pipe.to(snake_case_    )
        pipe.set_progress_bar_config(disable=snake_case_    )
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        A_    :			Union[str, Any]									=    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'   , )

        A_    :			str									=    torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 1_0**9
from __future__ import annotations

import sys







def     A__	(       __A						):







    '''simple docstring'''
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""						)
    return _fib(__A						)[0]







def     A__	(       __A						):







    '''simple docstring'''
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    _lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    _fib(n // 2						)
    _lowerCamelCase  :		List[Any]			    =    a * (b * 2 - a)
    _lowerCamelCase  :		Optional[int]			    =    a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
    lowerCAmelCase      :				Union[str, Any]     			=int(sys.argv[1])
    print(F"""fibonacci({n}) is {fibonacci(n)}""")




