"""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       _lowercase		( lowerCamelCase_   ,   unittest.TestCase	):
				lowerCamelCase__							=							UnCLIPPipeline
				lowerCamelCase__							=							TEXT_TO_IMAGE_PARAMS - {
				    '''negative_prompt''',
				    '''height''',
				    '''width''',
				    '''negative_prompt_embeds''',
				    '''guidance_scale''',
				    '''prompt_embeds''',
				    '''cross_attention_kwargs''',
				}
				lowerCamelCase__							=							TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCamelCase__							=							[
				    '''generator''',
				    '''return_dict''',
				    '''prior_num_inference_steps''',
				    '''decoder_num_inference_steps''',
				    '''super_res_num_inference_steps''',
				]
				lowerCamelCase__							=							False

				@property
				def 							A			(  self							:     Any  ):


										'''simple docstring'''
										return 3_2

				@property
				def 							A			(  self							:     Union[str, Any]  ):


										'''simple docstring'''
										return 3_2

				@property
				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										return self.time_input_dim

				@property
				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										return self.time_input_dim * 4

				@property
				def 							A			(  self							:     Union[str, Any]  ):


										'''simple docstring'''
										return 1_0_0

				@property
				def 							A			(  self							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )
										return tokenizer

				@property
				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										torch.manual_seed(0  )
										UpperCAmelCase__       						=		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 							A			(  self							:     Dict  ):


										'''simple docstring'''
										torch.manual_seed(0  )

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

										UpperCAmelCase__       						=		PriorTransformer(**snake_case_  )
										return model

				@property
				def 							A			(  self							:     Union[str, Any]  ):


										'''simple docstring'''
										torch.manual_seed(0  )

										UpperCAmelCase__       						=		{
										    'clip_embeddings_dim': self.text_embedder_hidden_size,
										    'time_embed_dim': self.time_embed_dim,
										    'cross_attention_dim': self.cross_attention_dim,
										}

										UpperCAmelCase__       						=		UnCLIPTextProjModel(**snake_case_  )
										return model

				@property
				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										torch.manual_seed(0  )

										UpperCAmelCase__       						=		{
										    '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',
										}

										UpperCAmelCase__       						=		UNetaDConditionModel(**snake_case_  )
										return model

				@property
				def 							A			(  self							:     int  ):


										'''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 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										torch.manual_seed(0  )

										UpperCAmelCase__       						=		UNetaDModel(**self.dummy_super_res_kwargs  )
										return model

				@property
				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										torch.manual_seed(1  )

										UpperCAmelCase__       						=		UNetaDModel(**self.dummy_super_res_kwargs  )
										return model

				def 							A			(  self							:     Any  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.dummy_prior
										UpperCAmelCase__       						=		self.dummy_decoder
										UpperCAmelCase__       						=		self.dummy_text_proj
										UpperCAmelCase__       						=		self.dummy_text_encoder
										UpperCAmelCase__       						=		self.dummy_tokenizer
										UpperCAmelCase__       						=		self.dummy_super_res_first
										UpperCAmelCase__       						=		self.dummy_super_res_last

										UpperCAmelCase__       						=		UnCLIPScheduler(
										    variance_type="""fixed_small_log"""							,     prediction_type="""sample"""							,     num_train_timesteps=1_0_0_0							,     clip_sample_range=5.0							,     )

										UpperCAmelCase__       						=		UnCLIPScheduler(
										    variance_type="""learned_range"""							,     prediction_type="""epsilon"""							,     num_train_timesteps=1_0_0_0							,     )

										UpperCAmelCase__       						=		UnCLIPScheduler(
										    variance_type="""fixed_small_log"""							,     prediction_type="""epsilon"""							,     num_train_timesteps=1_0_0_0							,     )

										UpperCAmelCase__       						=		{
										    '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 							A			(  self							:     Union[str, Any]							,     _lowercase							:     Optional[Any]							,     _lowercase							:     Optional[int]=0  ):


										'''simple docstring'''

										if str(snake_case_  ).startswith("""mps"""  ):
																UpperCAmelCase__       						=		torch.manual_seed(snake_case_  )
										else:
																UpperCAmelCase__       						=		torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )
										UpperCAmelCase__       						=		{
										    '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 							A			(  self							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		'cpu'

										UpperCAmelCase__       						=		self.get_dummy_components()

										UpperCAmelCase__       						=		self.pipeline_class(**snake_case_  )
										UpperCAmelCase__       						=		pipe.to(snake_case_  )

										pipe.set_progress_bar_config(disable=snake_case_  )

										UpperCAmelCase__       						=		pipe(**self.get_dummy_inputs(snake_case_  )  )
										UpperCAmelCase__       						=		output.images

										UpperCAmelCase__       						=		pipe(
										    **self.get_dummy_inputs(snake_case_  )							,     return_dict=snake_case_							,     )[0]

										UpperCAmelCase__       						=		image[0, -3:, -3:, -1]
										UpperCAmelCase__       						=		image_from_tuple[0, -3:, -3:, -1]

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

										UpperCAmelCase__       						=		np.array(
										    [
										        0.9997,
										        0.9988,
										        0.0028,
										        0.9997,
										        0.9984,
										        0.9965,
										        0.0029,
										        0.9986,
										        0.0025,
										    ]  )

										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 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		torch.device("""cpu"""  )


										class       _lowercase		:
														lowerCamelCase__							=							1
										UpperCAmelCase__       						=		self.get_dummy_components()

										UpperCAmelCase__       						=		self.pipeline_class(**snake_case_  )
										UpperCAmelCase__       						=		pipe.to(snake_case_  )

										UpperCAmelCase__       						=		components['prior']
										UpperCAmelCase__       						=		components['decoder']
										UpperCAmelCase__       						=		components['super_res_first']
										UpperCAmelCase__       						=		components['tokenizer']
										UpperCAmelCase__       						=		components['text_encoder']

										UpperCAmelCase__       						=		torch.Generator(device=snake_case_  ).manual_seed(0  )
										UpperCAmelCase__       						=		prior.dtype
										UpperCAmelCase__       						=		1

										UpperCAmelCase__       						=		(batch_size, prior.config.embedding_dim)
										UpperCAmelCase__       						=		pipe.prepare_latents(
										    snake_case_							,     dtype=snake_case_							,     device=snake_case_							,     generator=snake_case_							,     latents=snake_case_							,     scheduler=DummyScheduler()  )
										UpperCAmelCase__       						=		(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
										UpperCAmelCase__       						=		pipe.prepare_latents(
										    snake_case_							,     dtype=snake_case_							,     device=snake_case_							,     generator=snake_case_							,     latents=snake_case_							,     scheduler=DummyScheduler()  )

										UpperCAmelCase__       						=		(
										    batch_size,
										    super_res_first.config.in_channels // 2,
										    super_res_first.config.sample_size,
										    super_res_first.config.sample_size,
										)
										UpperCAmelCase__       						=		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_  )

										UpperCAmelCase__       						=		'this is a prompt example'

										UpperCAmelCase__       						=		torch.Generator(device=snake_case_  ).manual_seed(0  )
										UpperCAmelCase__       						=		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"""							,     )
										UpperCAmelCase__       						=		output.images

										UpperCAmelCase__       						=		tokenizer(
										    snake_case_							,     padding="""max_length"""							,     max_length=tokenizer.model_max_length							,     return_tensors="""pt"""							,     )
										UpperCAmelCase__       						=		text_encoder(text_inputs.input_ids  )
										UpperCAmelCase__       						=		text_inputs.attention_mask

										UpperCAmelCase__       						=		torch.Generator(device=snake_case_  ).manual_seed(0  )
										UpperCAmelCase__       						=		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 							A			(  self							:     Any  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		torch_device == 'cpu'

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

				@skip_mps
				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		torch_device == 'cpu'
										UpperCAmelCase__       						=		True
										UpperCAmelCase__       						=		[
										    '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 							A			(  self							:     str  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		[
										    'prior_num_inference_steps',
										    'decoder_num_inference_steps',
										    'super_res_num_inference_steps',
										]

										if torch_device == "mps":
																# TODO: MPS errors with larger batch sizes
																UpperCAmelCase__       						=		[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 							A			(  self							:     int  ):


										'''simple docstring'''
										return super().test_dict_tuple_outputs_equivalent()

				@skip_mps
				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										return super().test_save_load_local()

				@skip_mps
				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										return super().test_save_load_optional_components()


@nightly
class       _lowercase		( unittest.TestCase	):

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/unclip/karlo_v1_alpha_horse_cpu.npy"""  )

										UpperCAmelCase__       						=		UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""  )
										pipeline.set_progress_bar_config(disable=snake_case_  )

										UpperCAmelCase__       						=		torch.manual_seed(0  )
										UpperCAmelCase__       						=		pipeline(
										    """horse"""							,     num_images_per_prompt=1							,     generator=snake_case_							,     output_type="""np"""							,     )

										UpperCAmelCase__       						=		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       _lowercase		( unittest.TestCase	):

				def 							A			(  self							:     Union[str, Any]  ):


										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 							A			(  self							:     str  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/unclip/karlo_v1_alpha_horse_fp16.npy"""  )

										UpperCAmelCase__       						=		UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							,     torch_dtype=torch.floataa  )
										UpperCAmelCase__       						=		pipeline.to(snake_case_  )
										pipeline.set_progress_bar_config(disable=snake_case_  )

										UpperCAmelCase__       						=		torch.Generator(device="""cpu"""  ).manual_seed(0  )
										UpperCAmelCase__       						=		pipeline(
										    """horse"""							,     generator=snake_case_							,     output_type="""np"""							,     )

										UpperCAmelCase__       						=		output.images[0]

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

										assert_mean_pixel_difference(snake_case_							,     snake_case_  )

				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										UpperCAmelCase__       						=		UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							,     torch_dtype=torch.floataa  )
										UpperCAmelCase__       						=		pipe.to(snake_case_  )
										pipe.set_progress_bar_config(disable=snake_case_  )
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										UpperCAmelCase__       						=		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"""							,     )

										UpperCAmelCase__       						=		torch.cuda.max_memory_allocated()
										# make sure that less than 7 GB is allocated
										assert mem_bytes < 7 * 1_0**9


'''simple docstring'''


import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


snake_case__	:     int      =    '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

snake_case__	:     List[str]      =    '''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

snake_case__	:     int      =    '''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION			)
class 	__SCREAMING_SNAKE_CASE		(			datasets.Metric			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return datasets.MetricInfo(
           description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
               {
                   'predictions': {
                       'id': datasets.Value('string'		),
                       'prediction_text': datasets.Value('string'		),
                       'no_answer_probability': datasets.Value('float32'		),
                   },
                   'references': {
                       'id': datasets.Value('string'		),
                       'answers': datasets.features.Sequence(
                           {'text': datasets.Value('string'		), 'answer_start': datasets.Value('int32'		)}		),
                   },
               }		) ,						codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,						reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,						)






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_=1.0		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = {p['id']: p['no_answer_probability'] for p in predictions}
       UpperCAmelCase_				:  Dict  = [{'paragraphs': [{'qas': references}]}]
       UpperCAmelCase_				:  Optional[int]  = {p['id']: p['prediction_text'] for p in predictions}

       UpperCAmelCase_				:  Optional[int]  = make_qid_to_has_ans(snake_case_		)  # maps qid to True/False
       UpperCAmelCase_				:  List[Any]  = [k for k, v in qid_to_has_ans.items() if v]
       UpperCAmelCase_				:  Union[str, Any]  = [k for k, v in qid_to_has_ans.items() if not v]

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = get_raw_scores(snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  Tuple  = apply_no_ans_threshold(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  Dict  = apply_no_ans_threshold(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  str  = make_eval_dict(snake_case_ ,						snake_case_		)

       if has_ans_qids:
            UpperCAmelCase_				:  Dict  = make_eval_dict(snake_case_ ,						snake_case_ ,						qid_list=snake_case_		)
            merge_eval(snake_case_ ,						snake_case_ ,						'HasAns'		)
       if no_ans_qids:
            UpperCAmelCase_				:  List[str]  = make_eval_dict(snake_case_ ,						snake_case_ ,						qid_list=snake_case_		)
            merge_eval(snake_case_ ,						snake_case_ ,						'NoAns'		)
       find_all_best_thresh(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       return dict(snake_case_		)






