

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      snake_case_      (					_a      ,			_a      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =UNetaDModel
							__UpperCAmelCase      ="""sample"""




							@property
							def 							A__	(			self				):
												__lowerCAmelCase       =				4
												__lowerCAmelCase       =				1_4
												__lowerCAmelCase       =				1_6

												__lowerCAmelCase       =				floats_tensor((batch_size, num_features, seq_len)				).to(_A				)
												__lowerCAmelCase       =				torch.tensor([1_0] * batch_size				).to(_A				)

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




							@property
							def 							A__	(			self				):
												return (4, 1_4, 1_6)




							@property
							def 							A__	(			self				):
												return (4, 1_4, 1_6)




							def 							A__	(			self				):
												pass




							def 							A__	(			self				):
												pass




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




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




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




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




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




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




							def 							A__	(			self				):
												__lowerCAmelCase       =				{
												    'block_out_channels': (3_2, 6_4, 1_2_8, 2_5_6),
												    'in_channels': 1_4,
												    'out_channels': 1_4,
												    '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',
												}
												__lowerCAmelCase       =				self.dummy_input
												return init_dict, inputs_dict




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

												__lowerCAmelCase       =				model.config.in_channels
												__lowerCAmelCase       =				1_6
												__lowerCAmelCase       =				torch.randn((1, seq_len, num_features)				).permute(
												    0       ,    2       ,    1				)  # match original, we can update values and remove
												__lowerCAmelCase       =				torch.full((num_features,)       ,    0				)

												with torch.no_grad():
																	__lowerCAmelCase       =				model(_A       ,    _A				).sample.permute(0       ,    2       ,    1				)

												__lowerCAmelCase       =				output[0, -3:, -3:].flatten()
												# fmt: off
												__lowerCAmelCase       =				torch.tensor([-2.13_7172, 1.142_6016, 0.368_8687, -0.76_6922, 0.730_3146, 0.1103_8864, -0.476_0633, 0.1327_0172, 0.0259_1348]				)
												# fmt: on
												self.assertTrue(torch.allclose(_A       ,    _A       ,    rtol=1e-3				)				)




							def 							A__	(			self				):
												# Not implemented yet for this UNet
												pass




							@slow
							def 							A__	(			self				):
												__lowerCAmelCase       =				'harmonai/maestro-150k'
												__lowerCAmelCase       =				UNetaDModel.from_pretrained(_A       ,    subfolder='unet'				)
												model.to(_A				)

												__lowerCAmelCase       =				6_5_5_3_6
												__lowerCAmelCase       =				torch.sin(torch.arange(_A				)[None, None, :].repeat(1       ,    2       ,    1				)				).to(_A				)
												__lowerCAmelCase       =				torch.tensor([1]				).to(_A				)

												with torch.no_grad():
																	__lowerCAmelCase       =				model(_A       ,    _A				).sample

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

												assert (output_sum - 224.0896).abs() < 0.5
												assert (output_max - 0.0607).abs() < 4e-4





class      snake_case_      (					_a      ,			_a      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =UNetaDModel
							__UpperCAmelCase      ="""sample"""




							@property
							def 							A__	(			self				):
												__lowerCAmelCase       =				4
												__lowerCAmelCase       =				1_4
												__lowerCAmelCase       =				1_6

												__lowerCAmelCase       =				floats_tensor((batch_size, num_features, seq_len)				).to(_A				)
												__lowerCAmelCase       =				torch.tensor([1_0] * batch_size				).to(_A				)

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




							@property
							def 							A__	(			self				):
												return (4, 1_4, 1_6)




							@property
							def 							A__	(			self				):
												return (4, 1_4, 1)




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




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




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




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




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




							@unittest.skipIf(torch_device == 'mps'       ,    'mish op not supported in MPS'				)
							def 							A__	(			self				):
												# UNetRL is a value-function is different output shape
												__lowerCAmelCase,						__lowerCAmelCase       =				self.prepare_init_args_and_inputs_for_common()
												__lowerCAmelCase       =				self.model_class(**_A				)
												model.to(_A				)
												model.eval()

												with torch.no_grad():
																	__lowerCAmelCase       =				model(**_A				)

																	if isinstance(_A       ,    _A				):
																						__lowerCAmelCase       =				output.sample

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




							def 							A__	(			self				):
												pass




							def 							A__	(			self				):
												pass




							def 							A__	(			self				):
												__lowerCAmelCase       =				{
												    'in_channels': 1_4,
												    'out_channels': 1_4,
												    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
												    'up_block_types': [],
												    'out_block_type': 'ValueFunction',
												    'mid_block_type': 'ValueFunctionMidBlock1D',
												    'block_out_channels': [3_2, 6_4, 1_2_8, 2_5_6],
												    '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',
												}
												__lowerCAmelCase       =				self.dummy_input
												return init_dict, inputs_dict




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

												__lowerCAmelCase       =				value_function.config.in_channels
												__lowerCAmelCase       =				1_4
												__lowerCAmelCase       =				torch.randn((1, seq_len, num_features)				).permute(
												    0       ,    2       ,    1				)  # match original, we can update values and remove
												__lowerCAmelCase       =				torch.full((num_features,)       ,    0				)

												with torch.no_grad():
																	__lowerCAmelCase       =				value_function(_A       ,    _A				).sample

												# fmt: off
												__lowerCAmelCase       =				torch.tensor([165.25] * seq_len				)
												# fmt: on
												self.assertTrue(torch.allclose(_A       ,    _A       ,    rtol=1e-3				)				)




							def 							A__	(			self				):
												# Not implemented yet for this UNet
												pass









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

from transformers import BigBirdTokenizerFast


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


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=0		,       UpperCAmelCase__=None   ):

					"""simple docstring"""






					return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					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"
										__lowerCAmelCase       =				(
										    given_answers + get_sub_answers(UpperCAmelCase__		,       begin=1   ) + get_sub_answers(UpperCAmelCase__		,       end=-1   )
										)
					__lowerCAmelCase       =				[]
					for answer in given_answers:
										__lowerCAmelCase       =				answer.replace('_'		,       ' '   ).lower()
										__lowerCAmelCase       =				''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
										answers.append(' '.join(alias.split()   ).strip()   )
					return set(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=1		,       UpperCAmelCase__=100   ):

					"""simple docstring"""






					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )
					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )

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

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


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				sample['question']['text']
					__lowerCAmelCase       =				sample['document']['tokens']['token']
					__lowerCAmelCase       =				sample['document']['tokens']['is_html']
					__lowerCAmelCase       =				sample['annotations']['long_answer']
					__lowerCAmelCase       =				sample['annotations']['short_answers']

					__lowerCAmelCase       =				' '.join([context[i] for i in range(len(UpperCAmelCase__   )   ) 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",
															}

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

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

					__lowerCAmelCase       =				'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 							__lowercase     (	):

					"""simple docstring"""






					__lowerCAmelCase       =				load_from_disk('natural-questions-validation'   )
					__lowerCAmelCase       =				dataset.map(UpperCAmelCase__   ).remove_columns(['annotations', 'document', 'id']   )
					print(UpperCAmelCase__   )

					__lowerCAmelCase       =				dataset.filter(lambda UpperCAmelCase__   : (len(x['question']   ) + len(x['context']   )) < 4 * 4_096   )
					__lowerCAmelCase       =				short_validation_dataset.filter(lambda UpperCAmelCase__   : x["category"] != "null"   )
					short_validation_dataset

					__lowerCAmelCase       =				'vasudevgupta/flax-bigbird-natural-questions'
					__lowerCAmelCase       =				FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCAmelCase__   )
					__lowerCAmelCase       =				BigBirdTokenizerFast.from_pretrained(UpperCAmelCase__   )

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

					def evaluate(UpperCAmelCase__   ):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										__lowerCAmelCase       =				tokenizer(
										    example['question']		,       example['context']		,       return_tensors='np'		,       max_length=4_096		,       padding='max_length'		,       truncation=UpperCAmelCase__		,       )

										__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase       =				forward(**UpperCAmelCase__   )

										__lowerCAmelCase       =				CATEGORY_MAPPING[category.item()]

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

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

										__lowerCAmelCase       =				38 if predicted_category == 'short' else 1_024
										__lowerCAmelCase,						__lowerCAmelCase       =				get_best_valid_start_end_idx(
										    start_scores[0]		,       end_scores[0]		,       top_k=8		,       max_size=UpperCAmelCase__   )

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

										__lowerCAmelCase       =				expand_to_aliases(example['targets']		,       make_sub_answers=UpperCAmelCase__   )
										__lowerCAmelCase       =				expand_to_aliases(example['output']   )

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

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

										return example

					__lowerCAmelCase       =				short_validation_dataset.map(UpperCAmelCase__   )

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


if __name__ == "__main__":
		main()






