
import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

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




@skip_mps
class 						lowercase__  (						_UpperCAmelCase     ,       _UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[str]				       =IFPipeline
	A__    :   str				       =TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
	A__    :   Optional[int]				       =TEXT_TO_IMAGE_BATCH_PARAMS
	A__    :   Dict				       =PipelineTesterMixin.required_optional_params - {"""latents"""}
	def      A_				(			self							:   Dict							):
						return self._get_dummy_components()
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Optional[int]=0							):
						if str(UpperCAmelCase_							).startswith('mps'							):
											SCREAMING_SNAKE_CASE__													= torch.manual_seed(UpperCAmelCase_							)
						else:
											SCREAMING_SNAKE_CASE__													= torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs
	def      A_				(			self							:   Tuple							):
						self._test_save_load_optional_components()
	@unittest.skipIf(torch_device != 'cuda'       ,   reason='float16 requires CUDA'							)
	def      A_				(			self							:   str							):
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1e-1							)
	def      A_				(			self							:   Dict							):
						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2							)
	def      A_				(			self							:   List[str]							):
						self._test_save_load_local()
	def      A_				(			self							:   Dict							):
						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2       ,   )





	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available()       ,   reason='XFormers attention is only available with CUDA and `xformers` installed'       ,   )
	def      A_				(			self							:   List[Any]							):
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)





@slow
@require_torch_gpu
class 						lowercase__  (						unittest.TestCase  ):
	def      A_				(			self							:   List[str]							):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
	def      A_				(			self							:   Dict							):
						# if

						SCREAMING_SNAKE_CASE__													= IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'       ,   variant='fp16'       ,   torch_dtype=torch.floataa							)

						SCREAMING_SNAKE_CASE__													= IFSuperResolutionPipeline.from_pretrained(
						    'DeepFloyd/IF-II-L-v1.0'       ,   variant='fp16'       ,   torch_dtype=torch.floataa       ,   text_encoder=UpperCAmelCase_       ,   tokenizer=UpperCAmelCase_							)

						# pre compute text embeddings and remove T5 to save memory

						pipe_a.text_encoder.to('cuda'							)

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= pipe_a.encode_prompt('anime turtle'       ,   device='cuda'							)

						del pipe_a.tokenizer
						del pipe_a.text_encoder
						gc.collect()

						SCREAMING_SNAKE_CASE__													= None
						SCREAMING_SNAKE_CASE__													= None

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

						self._test_if(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# img2img

						SCREAMING_SNAKE_CASE__													= IFImgaImgPipeline(**pipe_a.components							)
						SCREAMING_SNAKE_CASE__													= IFImgaImgSuperResolutionPipeline(**pipe_a.components							)

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

						self._test_if_imgaimg(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# inpainting

						SCREAMING_SNAKE_CASE__													= IFInpaintingPipeline(**pipe_a.components							)
						SCREAMING_SNAKE_CASE__													= IFInpaintingSuperResolutionPipeline(**pipe_a.components							)

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

						self._test_if_inpainting(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Union[str, Any]							):
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__													= torch.Generator(device='cpu'							).manual_seed(0							)
						SCREAMING_SNAKE_CASE__													= pipe_a(
						    prompt_embeds=UpperCAmelCase_       ,   negative_prompt_embeds=UpperCAmelCase_       ,   num_inference_steps=2       ,   generator=UpperCAmelCase_       ,   output_type='np'       ,   )

						SCREAMING_SNAKE_CASE__													= output.images[0]

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

						SCREAMING_SNAKE_CASE__													= torch.cuda.max_memory_allocated()
						assert mem_bytes < 13 * 10**9

						SCREAMING_SNAKE_CASE__													= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'							)
						assert_mean_pixel_difference(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__													= torch.Generator(device='cpu'							).manual_seed(0							)

						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(0							)							).to(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= pipe_a(
						    prompt_embeds=UpperCAmelCase_       ,   negative_prompt_embeds=UpperCAmelCase_       ,   image=UpperCAmelCase_       ,   generator=UpperCAmelCase_       ,   num_inference_steps=2       ,   output_type='np'       ,   )

						SCREAMING_SNAKE_CASE__													= output.images[0]

						assert image.shape == (256, 256, 3)

						SCREAMING_SNAKE_CASE__													= torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						SCREAMING_SNAKE_CASE__													= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'							)
						assert_mean_pixel_difference(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Union[str, Any]							):
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(0							)							).to(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= torch.Generator(device='cpu'							).manual_seed(0							)

						SCREAMING_SNAKE_CASE__													= pipe_a(
						    prompt_embeds=UpperCAmelCase_       ,   negative_prompt_embeds=UpperCAmelCase_       ,   image=UpperCAmelCase_       ,   num_inference_steps=2       ,   generator=UpperCAmelCase_       ,   output_type='np'       ,   )

						SCREAMING_SNAKE_CASE__													= output.images[0]

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

						SCREAMING_SNAKE_CASE__													= torch.cuda.max_memory_allocated()
						assert mem_bytes < 10 * 10**9

						SCREAMING_SNAKE_CASE__													= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'							)
						assert_mean_pixel_difference(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__													= torch.Generator(device='cpu'							).manual_seed(0							)

						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 256, 256)       ,   rng=random.Random(0							)							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(0							)							).to(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= pipe_a(
						    prompt_embeds=UpperCAmelCase_       ,   negative_prompt_embeds=UpperCAmelCase_       ,   image=UpperCAmelCase_       ,   original_image=UpperCAmelCase_       ,   generator=UpperCAmelCase_       ,   num_inference_steps=2       ,   output_type='np'       ,   )

						SCREAMING_SNAKE_CASE__													= output.images[0]

						assert image.shape == (256, 256, 3)

						SCREAMING_SNAKE_CASE__													= torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						SCREAMING_SNAKE_CASE__													= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'							)
						assert_mean_pixel_difference(UpperCAmelCase_       ,   UpperCAmelCase_							)





	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[Any]							):
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(0							)							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(1							)							).to(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= torch.Generator(device='cpu'							).manual_seed(0							)
						SCREAMING_SNAKE_CASE__													= pipe_a(
						    prompt_embeds=UpperCAmelCase_       ,   negative_prompt_embeds=UpperCAmelCase_       ,   image=UpperCAmelCase_       ,   mask_image=UpperCAmelCase_       ,   num_inference_steps=2       ,   generator=UpperCAmelCase_       ,   output_type='np'       ,   )

						SCREAMING_SNAKE_CASE__													= output.images[0]

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

						SCREAMING_SNAKE_CASE__													= torch.cuda.max_memory_allocated()
						assert mem_bytes < 10 * 10**9

						SCREAMING_SNAKE_CASE__													= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'							)
						assert_mean_pixel_difference(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__													= torch.Generator(device='cpu'							).manual_seed(0							)

						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(0							)							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 256, 256)       ,   rng=random.Random(0							)							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= floats_tensor((1, 3, 256, 256)       ,   rng=random.Random(1							)							).to(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= pipe_a(
						    prompt_embeds=UpperCAmelCase_       ,   negative_prompt_embeds=UpperCAmelCase_       ,   image=UpperCAmelCase_       ,   mask_image=UpperCAmelCase_       ,   original_image=UpperCAmelCase_       ,   generator=UpperCAmelCase_       ,   num_inference_steps=2       ,   output_type='np'       ,   )

						SCREAMING_SNAKE_CASE__													= output.images[0]

						assert image.shape == (256, 256, 3)

						SCREAMING_SNAKE_CASE__													= torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						SCREAMING_SNAKE_CASE__													= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'							)
						assert_mean_pixel_difference(UpperCAmelCase_       ,   UpperCAmelCase_							)






def 							_lowercase							(     )		->		Dict:
					'''simple docstring'''


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







from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
				import tensorflow as tf

				from ..tf_utils import stable_softmax


if is_torch_available():
				import torch


__snake_case								=  logging.get_logger(__name__)




@add_end_docstrings(
    _UpperCAmelCase     ,       R"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """     ,       )
class 						lowercase__  (						_UpperCAmelCase  ):
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   GenericTensor							):
						if self.framework == "tf":
											SCREAMING_SNAKE_CASE__													= tf.where(input_ids == self.tokenizer.mask_token_id							).numpy()
						elif self.framework == "pt":
											SCREAMING_SNAKE_CASE__													= torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,   as_tuple=UpperCAmelCase_							)
						else:
											raise ValueError('Unsupported framework'							)
						return masked_index
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   GenericTensor							):
						SCREAMING_SNAKE_CASE__													= self.get_masked_index(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= np.prod(masked_index.shape							)
						if numel < 1:
											raise PipelineException(
											    'fill-mask'       ,   self.model.base_model_prefix       ,   F'No mask_token ({self.tokenizer.mask_token}) found on the input'       ,   )
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   GenericTensor							):
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							):
											for model_input in model_inputs:
																self._ensure_exactly_one_mask_token(model_input['input_ids'][0]							)
						else:
											for input_ids in model_inputs["input_ids"]:
																self._ensure_exactly_one_mask_token(UpperCAmelCase_							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   str=None       ,   **UpperCAmelCase_							:   Union[str, Any]							):
						if return_tensors is None:
											SCREAMING_SNAKE_CASE__													= self.framework
						SCREAMING_SNAKE_CASE__													= self.tokenizer(UpperCAmelCase_       ,   return_tensors=UpperCAmelCase_							)
						self.ensure_exactly_one_mask_token(UpperCAmelCase_							)
						return model_inputs
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__													= self.model(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= model_inputs['input_ids']
						return model_outputs
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   Union[str, Any]=5       ,   UpperCAmelCase_							:   Dict=None							):
						# Cap top_k if there are targets
						if target_ids is not None and target_ids.shape[0] < top_k:
											SCREAMING_SNAKE_CASE__													= target_ids.shape[0]
						SCREAMING_SNAKE_CASE__													= model_outputs['input_ids'][0]
						SCREAMING_SNAKE_CASE__													= model_outputs['logits']

						if self.framework == "tf":
											SCREAMING_SNAKE_CASE__													= tf.where(input_ids == self.tokenizer.mask_token_id							).numpy()[:, 0]

											SCREAMING_SNAKE_CASE__													= outputs.numpy()

											SCREAMING_SNAKE_CASE__													= outputs[0, masked_index, :]
											SCREAMING_SNAKE_CASE__													= stable_softmax(UpperCAmelCase_       ,   axis=-1							)
											if target_ids is not None:
																SCREAMING_SNAKE_CASE__													= tf.gather_nd(tf.squeeze(UpperCAmelCase_       ,   0							)       ,   target_ids.reshape(-1       ,   1							)							)
																SCREAMING_SNAKE_CASE__													= tf.expand_dims(UpperCAmelCase_       ,   0							)

											SCREAMING_SNAKE_CASE__													= tf.math.top_k(UpperCAmelCase_       ,   k=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= topk.values.numpy(), topk.indices.numpy()
						else:
											SCREAMING_SNAKE_CASE__													= torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,   as_tuple=UpperCAmelCase_							).squeeze(-1							)
											# Fill mask pipeline supports only one ${mask_token} per sample

											SCREAMING_SNAKE_CASE__													= outputs[0, masked_index, :]
											SCREAMING_SNAKE_CASE__													= logits.softmax(dim=-1							)
											if target_ids is not None:
																SCREAMING_SNAKE_CASE__													= probs[..., target_ids]

											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= probs.topk(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= values.shape[0] == 1
						for i, (_values, _predictions) in enumerate(zip(values.tolist()       ,   predictions.tolist()							)							):
											SCREAMING_SNAKE_CASE__													= []
											for v, p in zip(_values       ,   _predictions							):
																# Copy is important since we're going to modify this array in place
																SCREAMING_SNAKE_CASE__													= input_ids.numpy().copy()
																if target_ids is not None:
																					SCREAMING_SNAKE_CASE__													= target_ids[p].tolist()

																SCREAMING_SNAKE_CASE__													= p
																# Filter padding out:
																SCREAMING_SNAKE_CASE__													= tokens[np.where(tokens != self.tokenizer.pad_token_id							)]
																# Originally we skip special tokens to give readable output.
																# For multi masks though, the other [MASK] would be removed otherwise
																# making the output look odd, so we add them back
																SCREAMING_SNAKE_CASE__													= self.tokenizer.decode(UpperCAmelCase_       ,   skip_special_tokens=UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE__													= {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]							), 'sequence': sequence}
																row.append(UpperCAmelCase_							)
											result.append(UpperCAmelCase_							)
						if single_mask:
											return result[0]
						return result
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[Any]=None							):
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							):
											SCREAMING_SNAKE_CASE__													= [targets]
						try:
											SCREAMING_SNAKE_CASE__													= self.tokenizer.get_vocab()
						except Exception:
											SCREAMING_SNAKE_CASE__													= {}
						SCREAMING_SNAKE_CASE__													= []
						for target in targets:
											SCREAMING_SNAKE_CASE__													= vocab.get(UpperCAmelCase_       ,   UpperCAmelCase_							)
											if id_ is None:
																SCREAMING_SNAKE_CASE__													= self.tokenizer(
																    UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_       ,   return_attention_mask=UpperCAmelCase_       ,   return_token_type_ids=UpperCAmelCase_       ,   max_length=1       ,   truncation=UpperCAmelCase_       ,   )['input_ids']
																if len(UpperCAmelCase_							) == 0:
																					logger.warning(
																					    F'The specified target token `{target}` does not exist in the model vocabulary. '
																					    'We cannot replace it with anything meaningful, ignoring it'							)
																					continue
																SCREAMING_SNAKE_CASE__													= input_ids[0]
																# XXX: If users encounter this pass
																# it becomes pretty slow, so let's make sure
																# The warning enables them to fix the input to
																# get faster performance.
																logger.warning(
																    F'The specified target token `{target}` does not exist in the model vocabulary. '
																    F'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_							)}`.'							)
											target_ids.append(id_							)
						SCREAMING_SNAKE_CASE__													= list(set(UpperCAmelCase_							)							)
						if len(UpperCAmelCase_							) == 0:
											raise ValueError('At least one target must be provided when passed.'							)
						SCREAMING_SNAKE_CASE__													= np.array(UpperCAmelCase_							)
						return target_ids
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Optional[Any]=None       ,   UpperCAmelCase_							:   List[Any]=None							):
						SCREAMING_SNAKE_CASE__													= {}

						if targets is not None:
											SCREAMING_SNAKE_CASE__													= self.get_target_ids(UpperCAmelCase_       ,   UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= target_ids

						if top_k is not None:
											SCREAMING_SNAKE_CASE__													= top_k

						if self.tokenizer.mask_token_id is None:
											raise PipelineException(
											    'fill-mask'       ,   self.model.base_model_prefix       ,   'The tokenizer does not define a `mask_token`.'							)
						return {}, {}, postprocess_params





	def __call__(			self							:   Tuple       ,   UpperCAmelCase_							:   Union[str, Any]       ,   *UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= super().__call__(UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) and len(UpperCAmelCase_							) == 1:
											return outputs[0]
						return outputs





