






"""simple docstring"""







import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__a							:						Optional[int]   							=			{0: 'null', 1: 'short', 2: 'long', 3: 'yes', 4: 'no'}
__a							:						Dict   							=			set(''.join(['‘', '’', '´', '`', '.', ',', '-', '"']))





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=0			,  lowerCamelCase_=None):
					return [" ".join(x.split(''' ''')[begin:end]) for x in answers if len(x.split(''' ''')) > 1]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=False):
					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										a__ 							=	(
										    given_answers + get_sub_answers(lowerCamelCase_			,  begin=1) + get_sub_answers(lowerCamelCase_			,  end=-1)
										)
					a__ 							=	[]
					for answer in given_answers:
										a__ 							=	answer.replace('''_'''			,  ''' ''').lower()
										a__ 							=	''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias)
										answers.append(''' '''.join(alias.split()).strip())
					return set(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_=1			,  lowerCamelCase_=100):
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)

					a__ 							=	best_end_idx[:, None] - best_start_idx[None, :]
					a__ 							=	jnp.logical_or(widths < 0			,  widths > max_size)
					a__ 							=	(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
					a__ 							=	jnp.argmax(lowerCamelCase_).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					a__ 							=	sample['''question''']['''text''']
					a__ 							=	sample['''document''']['''tokens''']['''token''']
					a__ 							=	sample['''document''']['''tokens''']['''is_html''']
					a__ 							=	sample['''annotations''']['''long_answer''']
					a__ 							=	sample['''annotations''']['''short_answers''']

					a__ 							=	''' '''.join([context[i] for i in range(len(lowerCamelCase_)) if not is_html[i]])

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					a__ 							=	[]
					for s in short_answers:
										short_targets.extend(s['''text'''])
					a__ 							=	list(set(lowerCamelCase_))

					a__ 							=	[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										a__ 							=	context[s['''start_token'''] : s['''end_token''']]
										a__ 							=	is_html[s['''start_token'''] : s['''end_token''']]
										a__ 							=	''' '''.join([answer[i] for i in range(len(lowerCamelCase_)) if not html[i]])
										if new_answer not in long_targets:
															long_targets.append(lowerCamelCase_)

					a__ 							=	'''long_short''' if len(short_targets + long_targets) > 0 else '''null'''

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}





def 							SCREAMING_SNAKE_CASE							(   ):
					a__ 							=	load_from_disk('''natural-questions-validation''')
					a__ 							=	dataset.map(lowerCamelCase_).remove_columns(['''annotations''', '''document''', '''id'''])
					print(lowerCamelCase_)

					a__ 							=	dataset.filter(lambda lowerCamelCase_: (len(x['''question''']) + len(x['''context'''])) < 4 * 4096)
					a__ 							=	short_validation_dataset.filter(lambda lowerCamelCase_: x["category"] != "null")
					short_validation_dataset

					a__ 							=	'''vasudevgupta/flax-bigbird-natural-questions'''
					a__ 							=	FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase_)
					a__ 							=	BigBirdTokenizerFast.from_pretrained(lowerCamelCase_)

					@jax.jit
					def forward(*lowerCamelCase_			,  **lowerCamelCase_):
										a__							,a__							,a__ 							=	model(*lowerCamelCase_			,  **lowerCamelCase_)
										return start_logits, end_logits, jnp.argmax(lowerCamelCase_			,  axis=-1)

					def evaluate(lowerCamelCase_):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										a__ 							=	tokenizer(
										    example['''question''']			,  example['''context''']			,  return_tensors='''np'''			,  max_length=4096			,  padding='''max_length'''			,  truncation=lowerCamelCase_			,  )

										a__							,a__							,a__ 							=	forward(**lowerCamelCase_)

										a__ 							=	CATEGORY_MAPPING[category.item()]

										a__ 							=	example['''long'''] + example['''short''']
										if example["category"] in ["yes", "no", "null"]:
															a__ 							=	[example['''category''']]
										a__ 							=	example['''category'''] != '''null'''
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															a__ 							=	[predicted_category]
															a__ 							=	example['''output'''] == example['''targets''']
															a__ 							=	predicted_category != '''null'''
															return example

										a__ 							=	38 if predicted_category == '''short''' else 1024
										a__							,a__ 							=	get_best_valid_start_end_idx(
										    start_scores[0]			,  end_scores[0]			,  top_k=8			,  max_size=lowerCamelCase_)

										a__ 							=	inputs['''input_ids'''][0].tolist()
										a__ 							=	[tokenizer.decode(input_ids[start_score : end_score + 1])]

										a__ 							=	expand_to_aliases(example['''targets''']			,  make_sub_answers=lowerCamelCase_)
										a__ 							=	expand_to_aliases(example['''output'''])

										# some preprocessing to both prediction and answer
										a__ 							=	{''''''.join(a.split()) for a in answers}
										a__ 							=	{''''''.join(p.split()) for p in predictions}
										a__ 							=	{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

										# if there is a common element, it's a exact match
										a__ 							=	len(list(answers & predictions)) > 0
										a__ 							=	predicted_category != '''null''' and len(lowerCamelCase_) > 0

										return example

					a__ 							=	short_validation_dataset.map(lowerCamelCase_)

					a__ 							=	len(lowerCamelCase_)
					a__ 							=	len(short_validation_dataset.filter(lambda lowerCamelCase_: x["match"] == 1))
					print('''EM score:'''			,  (matched / total) * 100			,  '''%''')


if __name__ == "__main__":
	main()










"""simple docstring"""







import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin







class 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




							@property
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

												a__ 							=	floats_tensor((batch_size, num_features, seq_len)     ).to(__A     )
												a__ 							=	torch.tensor([10] * batch_size     ).to(__A     )

												return {"sample": noise, "timestep": time_step}




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												pass




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												super().test_determinism()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_output()




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												a__ 							=	{
												    '''block_out_channels''': (32, 64, 128, 256),
												    '''in_channels''': 14,
												    '''out_channels''': 14,
												    '''time_embedding_type''': '''positional''',
												    '''use_timestep_embedding''': True,
												    '''flip_sin_to_cos''': False,
												    '''freq_shift''': 1.0,
												    '''out_block_type''': '''OutConv1DBlock''',
												    '''mid_block_type''': '''MidResTemporalBlock1D''',
												    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
												    '''up_block_types''': ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D'''),
												    '''act_fn''': '''mish''',
												}
												a__ 							=	self.dummy_input
												return init_dict, inputs_dict




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__							,a__ 							=	UNetaDModel.from_pretrained(
												    '''bglick13/hopper-medium-v2-value-function-hor32'''					,		output_loading_info=__A					,		subfolder='''unet'''     )
												self.assertIsNotNone(__A     )
												self.assertEqual(len(loading_info['''missing_keys''']     )					,		0     )

												model.to(__A     )
												a__ 							=	model(**self.dummy_input     )

												assert image is not None, "Make sure output is not None"




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__ 							=	UNetaDModel.from_pretrained('''bglick13/hopper-medium-v2-value-function-hor32'''					,		subfolder='''unet'''     )
												torch.manual_seed(0     )
												if torch.cuda.is_available():
																	torch.cuda.manual_seed_all(0     )

												a__ 							=	model.config.in_channels
												a__ 							=	16
												a__ 							=	torch.randn((1, seq_len, num_features)     ).permute(
												    0					,		2					,		1     )  # match original, we can update values and remove
												a__ 							=	torch.full((num_features,)					,		0     )

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample.permute(0					,		2					,		1     )

												a__ 							=	output[0, -3:, -3:].flatten()
												# fmt: off
												a__ 							=	torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )




							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												pass





							@slow
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	'''harmonai/maestro-150k'''
												a__ 							=	UNetaDModel.from_pretrained(__A					,		subfolder='''unet'''     )
												model.to(__A     )

												a__ 							=	65536
												a__ 							=	torch.sin(torch.arange(__A     )[None, None, :].repeat(1					,		2					,		1     )     ).to(__A     )
												a__ 							=	torch.tensor([1]     ).to(__A     )

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample

												a__ 							=	output.abs().sum()
												a__ 							=	output.abs().max()

												assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
												assert (output_max - 0.0_6_0_7).abs() < 4e-4







class 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




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

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

												a__ 							=	floats_tensor((batch_size, num_features, seq_len)     ).to(__A     )
												a__ 							=	torch.tensor([10] * batch_size     ).to(__A     )

												return {"sample": noise, "timestep": time_step}




							@property
							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												return (4, 14, 1)




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_determinism()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												a__							,a__ 							=	self.prepare_init_args_and_inputs_for_common()
												a__ 							=	self.model_class(**__A     )
												model.to(__A     )
												model.eval()

												with torch.no_grad():
																	a__ 							=	model(**__A     )

																	if isinstance(__A					,		__A     ):
																						a__ 							=	output.sample

												self.assertIsNotNone(__A     )
												a__ 							=	torch.Size((inputs_dict['''sample'''].shape[0], 1)     )
												self.assertEqual(output.shape					,		__A					,		'''Input and output shapes do not match'''     )




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	{
												    '''in_channels''': 14,
												    '''out_channels''': 14,
												    '''down_block_types''': ['''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''],
												    '''up_block_types''': [],
												    '''out_block_type''': '''ValueFunction''',
												    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
												    '''block_out_channels''': [32, 64, 128, 256],
												    '''layers_per_block''': 1,
												    '''downsample_each_block''': True,
												    '''use_timestep_embedding''': True,
												    '''freq_shift''': 1.0,
												    '''flip_sin_to_cos''': False,
												    '''time_embedding_type''': '''positional''',
												    '''act_fn''': '''mish''',
												}
												a__ 							=	self.dummy_input
												return init_dict, inputs_dict




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												a__							,a__ 							=	UNetaDModel.from_pretrained(
												    '''bglick13/hopper-medium-v2-value-function-hor32'''					,		output_loading_info=__A					,		subfolder='''value_function'''     )
												self.assertIsNotNone(__A     )
												self.assertEqual(len(vf_loading_info['''missing_keys''']     )					,		0     )

												value_function.to(__A     )
												a__ 							=	value_function(**self.dummy_input     )

												assert image is not None, "Make sure output is not None"




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Union[str, Any]     ):

												'''simple docstring'''

												a__							,a__ 							=	UNetaDModel.from_pretrained(
												    '''bglick13/hopper-medium-v2-value-function-hor32'''					,		output_loading_info=__A					,		subfolder='''value_function'''     )
												torch.manual_seed(0     )
												if torch.cuda.is_available():
																	torch.cuda.manual_seed_all(0     )

												a__ 							=	value_function.config.in_channels
												a__ 							=	14
												a__ 							=	torch.randn((1, seq_len, num_features)     ).permute(
												    0					,		2					,		1     )  # match original, we can update values and remove
												a__ 							=	torch.full((num_features,)					,		0     )

												with torch.no_grad():
																	a__ 							=	value_function(__A					,		__A     ).sample

												# fmt: off
												a__ 							=	torch.tensor([1_6_5.2_5] * seq_len     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )





							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												pass


