



import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class    __lowerCAmelCase					(	unittest.TestCase			):
				__lowerCamelCase					=							'''hf-internal-testing/tiny-stable-diffusion-pipe'''
				__lowerCamelCase					=							1
				__lowerCamelCase					=							77
				__lowerCamelCase					=							32
				__lowerCamelCase					=							4
				__lowerCamelCase					=							__lowerCamelCase					=							64
				__lowerCamelCase					=							torch.manual_seed(0			)




				def 				snake_case  (				self		,							_snake_case=0.9999						):



									"""simple docstring"""





									_lowerCAmelCase         =       UNetaDConditionModel.from_pretrained(self.model_id		,							subfolder="""unet"""						)
									_lowerCAmelCase         =       unet.to(_snake_case						)
									_lowerCAmelCase         =       EMAModel(unet.parameters()		,							decay=_snake_case		,							model_cls=_snake_case		,							model_config=unet.config						)
									return unet, ema_unet




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       torch.randn(
									    self.batch_size		,							self.num_in_channels		,							self.latent_height		,							self.latent_width		,							generator=self.generator						).to(_snake_case						)
									_lowerCAmelCase         =       torch.randint(0		,							1000		,							size=(self.batch_size,)		,							generator=self.generator						).to(_snake_case						)
									_lowerCAmelCase         =       torch.randn(
									    self.batch_size		,							self.prompt_length		,							self.text_encoder_hidden_dim		,							generator=self.generator						).to(_snake_case						)
									return noisy_latents, timesteps, encoder_hidden_states




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       {}
									for k, param in unet.state_dict().items():
														_lowerCAmelCase         =       torch.randn_like(_snake_case						) + (param * torch.randn_like(_snake_case						))
														updated_state_dict.update({k: updated_param}						)
									unet.load_state_dict(_snake_case						)
									return unet




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.get_models()
									# Take the first (hypothetical) EMA step.
									ema_unet.step(unet.parameters()						)
									assert ema_unet.optimization_step == 1

									# Take two more.
									for _ in range(2						):
														ema_unet.step(unet.parameters()						)
									assert ema_unet.optimization_step == 3




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.get_models()
									# Since the `unet` is not being updated (i.e., backprop'd)
									# there won't be any difference between the `params` of `unet`
									# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
									ema_unet.step(unet.parameters()						)
									_lowerCAmelCase         =       list(unet.parameters()						)
									for s_param, param in zip(ema_unet.shadow_params		,							_snake_case						):
														assert torch.allclose(_snake_case		,							_snake_case						)

									# The above holds true even if we call `ema.step()` multiple times since
									# `unet` params are still not being updated.
									for _ in range(4						):
														ema_unet.step(unet.parameters()						)
									for s_param, param in zip(ema_unet.shadow_params		,							_snake_case						):
														assert torch.allclose(_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.get_models()
									# Here we simulate the parameter updates for `unet`. Since there might
									# be some parameters which are initialized to zero we take extra care to
									# initialize their values to something non-zero before the multiplication.
									_lowerCAmelCase         =       self.simulate_backprop(_snake_case						)

									# Take the EMA step.
									ema_unet.step(unet_pseudo_updated_step_one.parameters()						)

									# Now the EMA'd parameters won't be equal to the original model parameters.
									_lowerCAmelCase         =       list(unet_pseudo_updated_step_one.parameters()						)
									for s_param, param in zip(ema_unet.shadow_params		,							_snake_case						):
														assert ~torch.allclose(_snake_case		,							_snake_case						)

									# Ensure this is the case when we take multiple EMA steps.
									for _ in range(4						):
														ema_unet.step(unet.parameters()						)
									for s_param, param in zip(ema_unet.shadow_params		,							_snake_case						):
														assert ~torch.allclose(_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.get_models()

									# First backprop + EMA
									_lowerCAmelCase         =       self.simulate_backprop(_snake_case						)
									ema_unet.step(unet_step_one.parameters()						)
									_lowerCAmelCase         =       ema_unet.shadow_params

									# Second backprop + EMA
									_lowerCAmelCase         =       self.simulate_backprop(_snake_case						)
									ema_unet.step(unet_step_two.parameters()						)
									_lowerCAmelCase         =       ema_unet.shadow_params

									for step_one, step_two in zip(_snake_case		,							_snake_case						):
														assert ~torch.allclose(_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.get_models(decay=0.0						)
									_lowerCAmelCase         =       self.simulate_backprop(_snake_case						)
									ema_unet.step(unet_step_one.parameters()						)
									_lowerCAmelCase         =       ema_unet.shadow_params

									_lowerCAmelCase         =       self.simulate_backprop(_snake_case						)
									ema_unet.step(unet_step_two.parameters()						)
									_lowerCAmelCase         =       ema_unet.shadow_params

									for step_one, step_two in zip(_snake_case		,							_snake_case						):
														assert torch.allclose(_snake_case		,							_snake_case						)




				@skip_mps
				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.get_models()
									_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase         =       self.get_dummy_inputs()

									with tempfile.TemporaryDirectory() as tmpdir:
														ema_unet.save_pretrained(_snake_case						)
														_lowerCAmelCase         =       UNetaDConditionModel.from_pretrained(_snake_case		,							model_cls=_snake_case						)
														_lowerCAmelCase         =       loaded_unet.to(unet.device						)

									# Since no EMA step has been performed the outputs should match.
									_lowerCAmelCase         =       unet(_snake_case		,							_snake_case		,							_snake_case						).sample
									_lowerCAmelCase         =       loaded_unet(_snake_case		,							_snake_case		,							_snake_case						).sample

									assert torch.allclose(_snake_case		,							_snake_case		,							atol=1e-4						)








import argparse
import json
import re
import string
import sys
from collections import Counter


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					def remove_articles(snake_case     ):
										return re.sub(R"""\b(a|an|the)\b"""      ,		""" """      ,		snake_case     )

					def white_space_fix(snake_case     ):
										return " ".join(text.split()     )

					def remove_punc(snake_case     ):
										_lowerCAmelCase         =       set(string.punctuation     )
										return "".join(ch for ch in text if ch not in exclude     )

					def lower(snake_case     ):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(snake_case     )     )     )     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       normalize_answer(snake_case     ).split()
					_lowerCAmelCase         =       normalize_answer(snake_case     ).split()
					_lowerCAmelCase         =       Counter(snake_case     ) & Counter(snake_case     )
					_lowerCAmelCase         =       sum(common.values()     )
					if num_same == 0:
										return 0
					_lowerCAmelCase         =       1.0 * num_same / len(snake_case     )
					_lowerCAmelCase         =       1.0 * num_same / len(snake_case     )
					_lowerCAmelCase         =       (2 * precision * recall) / (precision + recall)
					return fa


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					return normalize_answer(snake_case     ) == normalize_answer(snake_case     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       []
					for ground_truth in ground_truths:
										_lowerCAmelCase         =       metric_fn(snake_case      ,		snake_case     )
										scores_for_ground_truths.append(snake_case     )
					return max(snake_case     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       _lowerCAmelCase         =       _lowerCAmelCase         =       0
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									_lowerCAmelCase         =       """Unanswered question """ + qa["""id"""] + """ will receive score 0."""
																									print(snake_case      ,		file=sys.stderr     )
																									continue
																				_lowerCAmelCase         =       [x["""text"""] for x in qa["""answers"""]]
																				_lowerCAmelCase         =       predictions[qa["""id"""]]
																				exact_match += metric_max_over_ground_truths(snake_case      ,		snake_case      ,		snake_case     )
																				fa += metric_max_over_ground_truths(snake_case      ,		snake_case      ,		snake_case     )

					_lowerCAmelCase         =       100.0 * exact_match / total
					_lowerCAmelCase         =       100.0 * fa / total

					return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
	A__       =			"""1.1"""
	A__       =			argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
	parser.add_argument("""dataset_file""", help="""Dataset file""")
	parser.add_argument("""prediction_file""", help="""Prediction File""")
	A__       =			parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		A__       =			json.load(dataset_file)
		if dataset_json["version"] != expected_version:
			print(
			    """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
			    file=sys.stderr,
			)
		A__       =			dataset_json["""data"""]
	with open(args.prediction_file) as prediction_file:
		A__       =			json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))



