



'''simple docstring'''





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__      					=     "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"




def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Dict     ,_snake_case  :						Union[str, Any]=8       ):





    '''simple docstring'''


    lowercase__		       =						height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    lowercase__		       =						width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor



class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							Dict:
           super().__init__()

           self.register_modules(
               unet=UpperCAmelCase_     ,scheduler=UpperCAmelCase_     ,movq=UpperCAmelCase_     ,)
           lowercase__		       =						2 ** (len(self.movq.config.block_out_channels      ) - 1)



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Union[str, Any]:
           if latents is None:
               lowercase__		       =						randn_tensor(UpperCAmelCase_     ,generator=UpperCAmelCase_     ,device=UpperCAmelCase_     ,dtype=UpperCAmelCase_      )
           else:
               if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
               lowercase__		       =						latents.to(UpperCAmelCase_      )

           lowercase__		       =						latents * scheduler.init_noise_sigma
           return latents



       def 							_a      (				self     ,UpperCAmelCase_=0      )  ->							Tuple:
           if is_accelerate_available():
               from accelerate import cpu_offload
           else:
               raise ImportError("Please install accelerate via `pip install accelerate`"      )

           lowercase__		       =						torch.device(F'''cuda:{gpu_id}'''      )

           lowercase__		       =						[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_=0      )  ->							List[Any]:
           if is_accelerate_available() and is_accelerate_version(">="     ,"0.17.0.dev0"      ):
               from accelerate import cpu_offload_with_hook
           else:
               raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."      )

           lowercase__		       =						torch.device(F'''cuda:{gpu_id}'''      )

           if self.device.type != "cpu":
               self.to("cpu"     ,silence_dtype_warnings=UpperCAmelCase_      )
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           lowercase__		       =						None
           for cpu_offloaded_model in [self.unet, self.movq]:
               lowercase__       ,       lowercase__		       =						cpu_offload_with_hook(UpperCAmelCase_     ,UpperCAmelCase_     ,prev_module_hook=UpperCAmelCase_      )

           # We'll offload the last model manually.
           lowercase__		       =						hook



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 							_a      (				self      )  ->							Dict:
           if not hasattr(self.unet     ,"_hf_hook"      ):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(UpperCAmelCase_     ,"_hf_hook"      )
                   and hasattr(module._hf_hook     ,"execution_device"      )
                   and module._hf_hook.execution_device is not None
               ):
                   return torch.device(module._hf_hook.execution_device      )
           return self.device







       @torch.no_grad()
       @replace_example_docstring(UpperCAmelCase_      )
       def __call__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = 512     ,UpperCAmelCase_ = 512     ,UpperCAmelCase_ = 100     ,UpperCAmelCase_ = 4.0     ,UpperCAmelCase_ = 1     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = "pil"     ,UpperCAmelCase_ = True     ,)  ->							List[Any]:
           lowercase__		       =						self._execution_device

           lowercase__		       =						guidance_scale > 1.0

           if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
               lowercase__		       =						torch.cat(UpperCAmelCase_     ,dim=0      )
           if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
               lowercase__		       =						torch.cat(UpperCAmelCase_     ,dim=0      )
           if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
               lowercase__		       =						torch.cat(UpperCAmelCase_     ,dim=0      )

           lowercase__		       =						image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
               lowercase__		       =						image_embeds.repeat_interleave(UpperCAmelCase_     ,dim=0      )
               lowercase__		       =						negative_image_embeds.repeat_interleave(UpperCAmelCase_     ,dim=0      )
               lowercase__		       =						hint.repeat_interleave(UpperCAmelCase_     ,dim=0      )

           lowercase__		       =						torch.cat([negative_image_embeds, image_embeds]     ,dim=0      ).to(dtype=self.unet.dtype     ,device=UpperCAmelCase_      )
           lowercase__		       =						torch.cat([hint, hint]     ,dim=0      ).to(dtype=self.unet.dtype     ,device=UpperCAmelCase_      )
           self.scheduler.set_timesteps(UpperCAmelCase_     ,device=UpperCAmelCase_      )
           lowercase__		       =						self.scheduler.timesteps

           lowercase__		       =						self.movq.config.latent_channels

           lowercase__       ,       lowercase__		       =						downscale_height_and_width(UpperCAmelCase_     ,UpperCAmelCase_     ,self.movq_scale_factor      )

           # create initial latent
           lowercase__		       =						self.prepare_latents(
               (batch_size, num_channels_latents, height, width)     ,image_embeds.dtype     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,self.scheduler     ,)

           for i, t in enumerate(self.progress_bar(UpperCAmelCase_      )      ):
               # expand the latents if we are doing classifier free guidance
               lowercase__		       =						torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

               lowercase__		       =						{"image_embeds": image_embeds, "hint": hint}
               lowercase__		       =						self.unet(
                   sample=UpperCAmelCase_     ,timestep=UpperCAmelCase_     ,encoder_hidden_states=UpperCAmelCase_     ,added_cond_kwargs=UpperCAmelCase_     ,return_dict=UpperCAmelCase_     ,)[0]

               if do_classifier_free_guidance:
                   lowercase__       ,       lowercase__		       =						noise_pred.split(latents.shape[1]     ,dim=1      )
                   lowercase__       ,       lowercase__		       =						noise_pred.chunk(2      )
                   lowercase__       ,       lowercase__		       =						variance_pred.chunk(2      )
                   lowercase__		       =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   lowercase__		       =						torch.cat([noise_pred, variance_pred_text]     ,dim=1      )

               if not (
                   hasattr(self.scheduler.config     ,"variance_type"      )
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                   lowercase__       ,       lowercase__		       =						noise_pred.split(latents.shape[1]     ,dim=1      )

               # compute the previous noisy sample x_t -> x_t-1
               lowercase__		       =						self.scheduler.step(
                   UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,generator=UpperCAmelCase_     ,)[0]
           # post-processing
           lowercase__		       =						self.movq.decode(UpperCAmelCase_     ,force_not_quantize=UpperCAmelCase_      )["sample"]

           if output_type not in ["pt", "np", "pil"]:
               raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''      )

           if output_type in ["np", "pil"]:
               lowercase__		       =						image * 0.5 + 0.5
               lowercase__		       =						image.clamp(0     ,1      )
               lowercase__		       =						image.cpu().permute(0     ,2     ,3     ,1      ).float().numpy()

           if output_type == "pil":
               lowercase__		       =						self.numpy_to_pil(UpperCAmelCase_      )

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=UpperCAmelCase_      )









'''simple docstring'''





import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[int]						   =					CodeGenTokenizer
       lowerCAmelCase__  :List[Any]						   =					CodeGenTokenizerFast
       lowerCAmelCase__  :str						   =					True
       lowerCAmelCase__  :Tuple						   =					{"add_prefix_space": True}
       lowerCAmelCase__  :Dict						   =					False



       def 							_a      (				self      )  ->							Union[str, Any]:
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowercase__		       =						[
               "l",
               "o",
               "w",
               "e",
               "r",
               "s",
               "t",
               "i",
               "d",
               "n",
               "\u0120",
               "\u0120l",
               "\u0120n",
               "\u0120lo",
               "\u0120low",
               "er",
               "\u0120lowest",
               "\u0120newer",
               "\u0120wider",
               "<unk>",
               "<|endoftext|>",
           ]
           lowercase__		       =						dict(zip(UpperCAmelCase_     ,range(len(UpperCAmelCase_      )      )      )      )
           lowercase__		       =						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
           lowercase__		       =						{"unk_token": "<unk>"}

           lowercase__		       =						os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"]      )
           lowercase__		       =						os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["merges_file"]      )
           with open(self.vocab_file     ,"w"     ,encoding="utf-8"      ) as fp:
               fp.write(json.dumps(UpperCAmelCase_      ) + "\n"      )
           with open(self.merges_file     ,"w"     ,encoding="utf-8"      ) as fp:
               fp.write("\n".join(UpperCAmelCase_      )      )



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							int:
           kwargs.update(self.special_tokens_map      )
           return CodeGenTokenizer.from_pretrained(self.tmpdirname     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							Tuple:
           kwargs.update(self.special_tokens_map      )
           return CodeGenTokenizerFast.from_pretrained(self.tmpdirname     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						"lower newer"
           lowercase__		       =						"lower newer"
           return input_text, output_text



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						CodeGenTokenizer(self.vocab_file     ,self.merges_file     ,**self.special_tokens_map      )
           lowercase__		       =						"lower newer"
           lowercase__		       =						["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
           lowercase__		       =						tokenizer.tokenize(UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						tokens + [tokenizer.unk_token]
           lowercase__		       =						[14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           if not self.test_rust_tokenizer:
               return

           lowercase__		       =						self.get_tokenizer()
           lowercase__		       =						self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_      )

           lowercase__		       =						"lower newer"

           # Testing tokenization
           lowercase__		       =						tokenizer.tokenize(UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.tokenize(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Testing conversion to ids without special tokens
           lowercase__		       =						tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Testing conversion to ids with special tokens
           lowercase__		       =						self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.encode(UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Testing the unknown token
           lowercase__		       =						tokens + [rust_tokenizer.unk_token]
           lowercase__		       =						[14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )     ,UpperCAmelCase_      )



       def 							_a      (				self     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Any:
           # It's very difficult to mix/test pretokenization with byte-level
           # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
           pass



       def 							_a      (				self     ,UpperCAmelCase_=15      )  ->							Union[str, Any]:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                   lowercase__		       =						self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_     ,**UpperCAmelCase_      )

                   # Simple input
                   lowercase__		       =						"This is a simple input"
                   lowercase__		       =						["This is a simple input 1", "This is a simple input 2"]
                   lowercase__		       =						("This is a simple input", "This is a pair")
                   lowercase__		       =						[
                       ("This is a simple input 1", "This is a simple input 2"),
                       ("This is a simple pair 1", "This is a simple pair 2"),
                   ]

                   # Simple input tests
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Simple input
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Simple input
                   self.assertRaises(
                       UpperCAmelCase_     ,tokenizer_r.batch_encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"     ,)

                   # Pair input
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Pair input
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Pair input
                   self.assertRaises(
                       UpperCAmelCase_     ,tokenizer_r.batch_encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"     ,)



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						CodeGenTokenizer.from_pretrained(self.tmpdirname     ,pad_token="<pad>"      )

           # Simple input
           lowercase__		       =						"This is a simple input"
           lowercase__		       =						["This is a simple input looooooooong", "This is a simple input"]
           lowercase__		       =						("This is a simple input", "This is a pair")
           lowercase__		       =						[
               ("This is a simple input loooooong", "This is a simple input"),
               ("This is a simple pair loooooong", "This is a simple pair"),
           ]

           lowercase__		       =						tokenizer.pad_token_id

           lowercase__		       =						tokenizer(UpperCAmelCase_     ,padding="max_length"     ,max_length=30     ,return_tensors="np"      )
           lowercase__		       =						tokenizer(UpperCAmelCase_     ,padding=UpperCAmelCase_     ,truncate=UpperCAmelCase_     ,return_tensors="np"      )
           lowercase__		       =						tokenizer(*UpperCAmelCase_     ,padding="max_length"     ,max_length=60     ,return_tensors="np"      )
           lowercase__		       =						tokenizer(UpperCAmelCase_     ,padding=UpperCAmelCase_     ,truncate=UpperCAmelCase_     ,return_tensors="np"      )

           # s
           # test single string max_length padding
           self.assertEqual(out_s["input_ids"].shape[-1]     ,30      )
           self.assertTrue(pad_token_id in out_s["input_ids"]      )
           self.assertTrue(0 in out_s["attention_mask"]      )

           # s2
           # test automatic padding
           self.assertEqual(out_sa["input_ids"].shape[-1]     ,33      )
           # long slice doesn't have padding
           self.assertFalse(pad_token_id in out_sa["input_ids"][0]      )
           self.assertFalse(0 in out_sa["attention_mask"][0]      )
           # short slice does have padding
           self.assertTrue(pad_token_id in out_sa["input_ids"][1]      )
           self.assertTrue(0 in out_sa["attention_mask"][1]      )

           # p
           # test single pair max_length padding
           self.assertEqual(out_p["input_ids"].shape[-1]     ,60      )
           self.assertTrue(pad_token_id in out_p["input_ids"]      )
           self.assertTrue(0 in out_p["attention_mask"]      )

           # p2
           # test automatic padding pair
           self.assertEqual(out_pa["input_ids"].shape[-1]     ,52      )
           # long slice pair doesn't have padding
           self.assertFalse(pad_token_id in out_pa["input_ids"][0]      )
           self.assertFalse(0 in out_pa["attention_mask"][0]      )
           # short slice pair does have padding
           self.assertTrue(pad_token_id in out_pa["input_ids"][1]      )
           self.assertTrue(0 in out_pa["attention_mask"][1]      )



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						"$$$"
           lowercase__		       =						CodeGenTokenizer.from_pretrained(self.tmpdirname     ,bos_token=UpperCAmelCase_     ,add_bos_token=UpperCAmelCase_      )

           lowercase__		       =						"This is a simple input"
           lowercase__		       =						["This is a simple input 1", "This is a simple input 2"]

           lowercase__		       =						tokenizer.bos_token_id

           lowercase__		       =						tokenizer(UpperCAmelCase_      )
           lowercase__		       =						tokenizer(UpperCAmelCase_      )

           self.assertEqual(out_s.input_ids[0]     ,UpperCAmelCase_      )
           self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

           lowercase__		       =						tokenizer.decode(out_s.input_ids      )
           lowercase__		       =						tokenizer.batch_decode(out_sa.input_ids      )

           self.assertEqual(decode_s.split()[0]     ,UpperCAmelCase_      )
           self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )



       @slow
       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"      )

           lowercase__		       =						"\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
           lowercase__		       =						"\nif len_a > len_b:      result = a\nelse:      result = b"

           lowercase__		       =						tokenizer.encode(UpperCAmelCase_      )
           lowercase__		       =						["^#", re.escape("<|endoftext|>"      ), "^'''", "^\"\"\"", "\n\n\n"]
           lowercase__		       =						tokenizer.decode(UpperCAmelCase_     ,truncate_before_pattern=UpperCAmelCase_      )
           self.assertEqual(UpperCAmelCase_     ,UpperCAmelCase_      )







       def 							_a      (				self      )  ->							Any:
           pass




