

import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      str       =  ShapEPipeline
  SCREAMING_SNAKE_CASE    :      str       =  ['''prompt''']
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  ['''prompt''']
  SCREAMING_SNAKE_CASE    :      Dict       =  [
      '''num_images_per_prompt''',
      '''num_inference_steps''',
      '''generator''',
      '''latents''',
      '''guidance_scale''',
      '''frame_size''',
      '''output_type''',
      '''return_dict''',
  ]
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  False

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 32

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 32

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.time_input_dim * 4

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 8

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
        return tokenizer

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_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(_SCREAMING_SNAKE_CASE					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     {
            'num_attention_heads': 2,
            'attention_head_dim': 16,
            'embedding_dim': self.time_input_dim,
            'num_embeddings': 32,
            'embedding_proj_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'num_layers': 1,
            'clip_embed_dim': self.time_input_dim * 2,
            'additional_embeddings': 0,
            'time_embed_act_fn': 'gelu',
            'norm_in_type': 'layer',
            'encoder_hid_proj_type': None,
            'added_emb_type': None,
        }

        SCREAMING_SNAKE_CASE_				:  Tuple           =     PriorTransformer(**_SCREAMING_SNAKE_CASE					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {
            'param_shapes': (
                (self.renderer_dim, 93),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
            ),
            'd_latent': self.time_input_dim,
            'd_hidden': self.renderer_dim,
            'n_output': 12,
            'background': (
                0.1,
                0.1,
                0.1,
            ),
        }
        SCREAMING_SNAKE_CASE_				:  List[str]           =     ShapERenderer(**_SCREAMING_SNAKE_CASE					)
        return model

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_prior
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.dummy_tokenizer
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_renderer

        SCREAMING_SNAKE_CASE_				:  str           =     HeunDiscreteScheduler(
            beta_schedule='exp' ,					num_train_timesteps=1024 ,					prediction_type='sample' ,					use_karras_sigmas=_SCREAMING_SNAKE_CASE ,					clip_sample=_SCREAMING_SNAKE_CASE ,					clip_sample_range=1.0 ,					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {
            'prior': prior,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'renderer': renderer,
            'scheduler': scheduler,
        }

        return components

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0					):





        """simple docstring"""







        if str(_SCREAMING_SNAKE_CASE					).startswith('mps'					):
              SCREAMING_SNAKE_CASE_				:  Any           =     torch.manual_seed(_SCREAMING_SNAKE_CASE					)
        else:
              SCREAMING_SNAKE_CASE_				:  str           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     {
            'prompt': 'horse',
            'generator': generator,
            'num_inference_steps': 1,
            'frame_size': 32,
            'output_type': 'np',
        }
        return inputs

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     'cpu'

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

        SCREAMING_SNAKE_CASE_				:  Any           =     self.pipeline_class(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     pipe.to(_SCREAMING_SNAKE_CASE					)

        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     output.images[0]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     image[0, -3:, -3:, -1]

        assert image.shape == (20, 32, 32, 3)

        SCREAMING_SNAKE_CASE_				:  Any           =     np.array(
            [
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
            ]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self._test_inference_batch_consistent(batch_sizes=[1, 2]					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     torch_device == 'cpu'
        SCREAMING_SNAKE_CASE_				:  Tuple           =     True

        self._test_inference_batch_single_identical(
            batch_size=2 ,					test_max_difference=_SCREAMING_SNAKE_CASE ,					relax_max_difference=_SCREAMING_SNAKE_CASE ,					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.pipeline_class(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     1
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     2

        SCREAMING_SNAKE_CASE_				:  Any           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)

        for key in inputs.keys():
              if key in self.batch_params:
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     batch_size * [inputs[key]]

        SCREAMING_SNAKE_CASE_				:  str           =     pipe(**_SCREAMING_SNAKE_CASE ,					num_images_per_prompt=_SCREAMING_SNAKE_CASE					)[0]

        assert images.shape[0] == batch_size * num_images_per_prompt

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/shap_e/test_shap_e_np_out.npy'					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     ShapEPipeline.from_pretrained('openai/shap-e'					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(0					)

        SCREAMING_SNAKE_CASE_				:  str           =     pipe(
            'a shark' ,					generator=_SCREAMING_SNAKE_CASE ,					guidance_scale=15.0 ,					num_inference_steps=64 ,					frame_size=64 ,					output_type='np' ,					).images[0]

        assert images.shape == (20, 64, 64, 3)

        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)









import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Dict       =  GPTSanJapaneseTokenizer
  SCREAMING_SNAKE_CASE    :      List[Any]       =  False
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  {'''do_clean_text''': False, '''add_prefix_space''': False}

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().setUp()

        # fmt: off
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
        # fmt: on
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
        SCREAMING_SNAKE_CASE_				:  int           =     {'unk_token': '<unk>'}

        SCREAMING_SNAKE_CASE_				:  Any           =     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['vocab_file']					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['emoji_file']					)
        with open(self.vocab_file ,					'w' ,					encoding='utf-8'					) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)
        with open(self.emoji_file ,					'w'					) as emoji_writer:
              emoji_writer.write(json.dumps(_SCREAMING_SNAKE_CASE					)					)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        kwargs.update(self.special_tokens_map					)
        return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     'こんにちは、世界。 \nこんばんは、世界。😀'
        return input_text, output_text

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_input_output_texts(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     tokenizer.decode(_SCREAMING_SNAKE_CASE ,					clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE					)
        return text, ids

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass  # TODO add if relevant

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass  # TODO add if relevant

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass  # TODO add if relevant

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_tokenizer()

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'こんにちは、世界。　こんばんは、㔺界。'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        # Testing conversion to ids without special tokens
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        # Testing conversion to ids with special tokens
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE_				:  str           =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_tokenizer()

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
        SCREAMING_SNAKE_CASE_				:  str           =     'こんにちは、、、、世界。こんばんは、、、、世界。'
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  int           =     'こんにちは、世界。'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     'こんばんは、㔺界。😀'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     'こんにちは、世界。こんばんは、世界。😀'
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.encode(prefix_text + input_text					)
        SCREAMING_SNAKE_CASE_				:  int           =     tokenizer.encode('' ,					prefix_text=prefix_text + input_text					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					prefix_text=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  Any           =     'こんにちは、世界。'
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     'こんばんは、㔺界。😀'

        SCREAMING_SNAKE_CASE_				:  int           =     len(tokenizer.encode(_SCREAMING_SNAKE_CASE					)					) - 2
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     len(tokenizer.encode(_SCREAMING_SNAKE_CASE					)					) - 2

        SCREAMING_SNAKE_CASE_				:  str           =     [1] + [0] * (len_prefix + len_text + 1)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [1] * (len_prefix + len_text + 1) + [0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     [1] + [1] * (len_prefix) + [0] * (len_text + 1)

        SCREAMING_SNAKE_CASE_				:  Any           =     tokenizer(prefix_text + input_text					).token_type_ids
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer('' ,					prefix_text=prefix_text + input_text					).token_type_ids
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer(_SCREAMING_SNAKE_CASE ,					prefix_text=_SCREAMING_SNAKE_CASE					).token_type_ids
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer.encode('あンいワ'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.encode('' ,					prefix_text='あンいワ'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.encode('いワ' ,					prefix_text='あン'					)

        self.assertEqual(tokenizer.decode(_SCREAMING_SNAKE_CASE					) ,					tokenizer.decode(_SCREAMING_SNAKE_CASE					)					)
        self.assertEqual(tokenizer.decode(_SCREAMING_SNAKE_CASE					) ,					tokenizer.decode(_SCREAMING_SNAKE_CASE					)					)
        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertEqual(x_token_a[1] ,					x_token_a[-1]					)  # SEG token
        self.assertEqual(x_token_a[1] ,					x_token_a[3]					)  # SEG token

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer(_SCREAMING_SNAKE_CASE ,					padding=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer.batch_encode_plus(_SCREAMING_SNAKE_CASE ,					padding=_SCREAMING_SNAKE_CASE					)

        # fmt: off
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
        SCREAMING_SNAKE_CASE_				:  int           =     [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
        SCREAMING_SNAKE_CASE_				:  Any           =     [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
        # fmt: on
        self.assertListEqual(x_token.input_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token.token_type_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token.attention_mask ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token_a.input_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token_a.token_type_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token_a.attention_mask ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass






