





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
				from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from transformers import VivitImageProcessor

class      A_	:
	def __init__(   self							, _A							, _A=2							, _A=True							, _A=True							, _A=1_0							, _A=1_0							, _A=8							, _A=[2, 4, 4]							, _A=3							, _A=7_6_8							, _A=5							, _A=4							, _A=3_7							, _A="gelu_fast"							, _A=0.0							, _A=0.0							, _A=0.02							, _A=1E-06							, _A=True							, _A=None							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    parent
				UpperCAmelCase       		=    batch_size
				UpperCAmelCase       		=    is_training
				UpperCAmelCase       		=    use_labels
				UpperCAmelCase       		=    num_labels
				UpperCAmelCase       		=    image_size
				UpperCAmelCase       		=    num_frames
				UpperCAmelCase       		=    tubelet_size
				UpperCAmelCase       		=    num_channels
				UpperCAmelCase       		=    hidden_size
				UpperCAmelCase       		=    num_hidden_layers
				UpperCAmelCase       		=    num_attention_heads
				UpperCAmelCase       		=    intermediate_size
				UpperCAmelCase       		=    hidden_act
				UpperCAmelCase       		=    hidden_dropout_prob
				UpperCAmelCase       		=    attention_probs_dropout_prob
				UpperCAmelCase       		=    initializer_range
				UpperCAmelCase       		=    layer_norm_eps
				UpperCAmelCase       		=    qkv_bias
				UpperCAmelCase       		=    scope

				UpperCAmelCase       		=    (
				    (self.image_size // self.tubelet_size[2])
				    * (self.image_size // self.tubelet_size[1])
				    * (self.num_frames // self.tubelet_size[0])
				) + 1  # CLS token
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    floats_tensor(
				    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase       		=    None
				if self.use_labels:
							UpperCAmelCase       		=    ids_tensor([self.batch_size]							, self.num_labels		)

				UpperCAmelCase       		=    self.get_config()

				return config, pixel_values, labels
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    VivitConfig(
				    num_frames=self.num_frames							, image_size=self.image_size							, tubelet_size=self.tubelet_size							, num_channels=self.num_channels							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, initializer_range=self.initializer_range							, layer_norm_eps=self.layer_norm_eps							, qkv_bias=self.qkv_bias							, )
				UpperCAmelCase       		=    self.num_labels
				return config
	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    VivitModel(config=__SCREAMING_SNAKE_CASE		)
				model.to(__SCREAMING_SNAKE_CASE		)
				model.eval()
				UpperCAmelCase       		=    model(__SCREAMING_SNAKE_CASE		)
				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)		)
	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    VivitForVideoClassification(__SCREAMING_SNAKE_CASE		)
				model.to(__SCREAMING_SNAKE_CASE		)
				model.eval()

				UpperCAmelCase       		=    model(__SCREAMING_SNAKE_CASE		)

				# verify the logits shape
				UpperCAmelCase       		=    torch.Size((self.batch_size, self.num_labels)		)
				self.parent.assertEqual(result.logits.shape							, __SCREAMING_SNAKE_CASE		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.prepare_config_and_inputs()
				UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    config_and_inputs
				UpperCAmelCase       		=    {'''pixel_values''': pixel_values}
				return config, inputs_dict

@require_torch
class      A_	(UpperCamelCase_					,							unittest.TestCase      ):
	UpperCAmelCase__ =      (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

	UpperCAmelCase__ =      False
	UpperCAmelCase__ =      False
	UpperCAmelCase__ =      False
	UpperCAmelCase__ =      False
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    VivitModelTester(self		)
				UpperCAmelCase       		=    ConfigTester(self							, config_class=__SCREAMING_SNAKE_CASE							, has_text_modality=__SCREAMING_SNAKE_CASE							, hidden_size=3_7		)
	def 					_lowercase    (   self							, _A							, _A							, _A=False		):






				'''simple docstring'''







				UpperCAmelCase       		=    copy.deepcopy(__SCREAMING_SNAKE_CASE		)

				if return_labels:
							if model_class in get_values(__SCREAMING_SNAKE_CASE		):
										UpperCAmelCase       		=    torch.zeros(
										    self.model_tester.batch_size							, dtype=torch.long							, device=__SCREAMING_SNAKE_CASE		)

				return inputs_dict
	def 					_lowercase    (   self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	@unittest.skip(reason='''Vivit does not use inputs_embeds'''		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				pass
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    model_class(__SCREAMING_SNAKE_CASE		)
							self.assertIsInstance(model.get_input_embeddings()							, (nn.Module)		)
							UpperCAmelCase       		=    model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE							, nn.Linear		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    model_class(__SCREAMING_SNAKE_CASE		)
							UpperCAmelCase       		=    inspect.signature(model.forward		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							UpperCAmelCase       		=    [*signature.parameters.keys()]

							UpperCAmelCase       		=    ['''pixel_values''', '''head_mask''']
							self.assertListEqual(arg_names[:2]							, __SCREAMING_SNAKE_CASE		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_video_classification(*__SCREAMING_SNAKE_CASE		)
	@slow
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase       		=    VivitModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase       		=    True

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    self.model_tester.seq_length

							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    False
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    model_class(__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()
							with torch.no_grad():
										UpperCAmelCase       		=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE		)		)
							UpperCAmelCase       		=    outputs.attentions
							self.assertEqual(len(__SCREAMING_SNAKE_CASE		)							, self.model_tester.num_hidden_layers		)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    model_class(__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()
							with torch.no_grad():
										UpperCAmelCase       		=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE		)		)
							UpperCAmelCase       		=    outputs.attentions
							self.assertEqual(len(__SCREAMING_SNAKE_CASE		)							, self.model_tester.num_hidden_layers		)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]		)							, [self.model_tester.num_attention_heads, seq_len, seq_len]							, )
							UpperCAmelCase       		=    len(__SCREAMING_SNAKE_CASE		)

							# Check attention is always last and order is fine
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    model_class(__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()
							with torch.no_grad():
										UpperCAmelCase       		=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE		)		)

							self.assertEqual(out_len + 1							, len(__SCREAMING_SNAKE_CASE		)		)

							UpperCAmelCase       		=    outputs.attentions

							self.assertEqual(len(__SCREAMING_SNAKE_CASE		)							, self.model_tester.num_hidden_layers		)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]		)							, [self.model_tester.num_attention_heads, seq_len, seq_len]							, )
	def 					_lowercase    (   self		):






				'''simple docstring'''







				def check_hidden_states_output(_A							, _A							, _A		):
							UpperCAmelCase       		=    model_class(__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()

							with torch.no_grad():
										UpperCAmelCase       		=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE		)		)

							UpperCAmelCase       		=    outputs.hidden_states
							UpperCAmelCase       		=    self.model_tester.num_hidden_layers + 1
							self.assertEqual(len(__SCREAMING_SNAKE_CASE		)							, __SCREAMING_SNAKE_CASE		)

							UpperCAmelCase       		=    self.model_tester.seq_length

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]		)							, [seq_length, self.model_tester.hidden_size]							, )

				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    True
							check_hidden_states_output(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase       		=    True

							check_hidden_states_output(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE		)







	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				pass





def 						__SCREAMING_SNAKE_CASE  (    )  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    hf_hub_download(
			    repo_id='''hf-internal-testing/spaghetti-video'''      ,      filename='''eating_spaghetti_32_frames.npy'''      ,      repo_type='''dataset'''			)
			UpperCAmelCase       		=    np.load(snake_case__			)
			return list(snake_case__			)






@require_torch
@require_vision
class      A_	(unittest.TestCase      ):
	@cached_property
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return VivitImageProcessor() if is_vision_available() else None







	@slow
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''		).to(__SCREAMING_SNAKE_CASE		)

				UpperCAmelCase       		=    self.default_image_processor
				UpperCAmelCase       		=    prepare_video()
				UpperCAmelCase       		=    image_processor(__SCREAMING_SNAKE_CASE							, return_tensors='''pt'''		).to(__SCREAMING_SNAKE_CASE		)

				# forward pass
				with torch.no_grad():
							UpperCAmelCase       		=    model(**__SCREAMING_SNAKE_CASE		)

				# verify the logits
				UpperCAmelCase       		=    torch.Size((1, 4_0_0)		)
				self.assertEqual(outputs.logits.shape							, __SCREAMING_SNAKE_CASE		)

				# taken from original model
				UpperCAmelCase       		=    torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]		).to(__SCREAMING_SNAKE_CASE		)

				self.assertTrue(torch.allclose(outputs.logits[0, :5]							, __SCREAMING_SNAKE_CASE							, atol=1E-4		)		)








import argparse
import json
import re
import string
import sys

import numpy as np


__A    :   Optional[Any]          =       0.5





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    ['''.''', ''',''', ''';''', ''':''']

			for token in remove_tokens:
						UpperCAmelCase       		=    ground_truth.replace(UpperCamelCase__      ,      ''''''			)
						UpperCAmelCase       		=    prediction.replace(UpperCamelCase__      ,      ''''''			)

			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.lower(), prediction.lower()
			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.replace('''/'''      ,      ''' '''			), prediction.replace('''/'''      ,      ''' '''			)
			UpperCAmelCase		,      UpperCAmelCase       		=    set(ground_truth.split(''' '''			)			), set(prediction.split(''' '''			)			)

			UpperCAmelCase       		=    ground_truth.intersection(UpperCamelCase__			)
			UpperCAmelCase       		=    ground_truth.union(UpperCamelCase__			)
			UpperCAmelCase       		=    len(UpperCamelCase__			) / len(UpperCamelCase__			)
			return jaccard





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__			)			)			)			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    0, 0, 0

			UpperCAmelCase       		=    '''Parties''' in qa_id

			# first check if ground truth is empty
			if len(UpperCamelCase__			) == 0:
						if len(UpperCamelCase__			) > 0:
									fp += len(UpperCamelCase__			)  # false positive for each one
			else:
						for ground_truth in ground_truths:
									assert len(UpperCamelCase__			) > 0
									# check if there is a match
									UpperCAmelCase       		=    False
									for pred in predictions:
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH
												if is_match:
															UpperCAmelCase       		=    True

									if match_found:
												tp += 1
									else:
												fn += 1

        # now also get any fps by looping through preds
						for pred in predictions:
									# Check if there's a match. if so, don't count (don't want to double count based on the above)
									# but if there's no match, then this is a false positive.
									# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
									# multiple predictions that are matched with the same answer.)
									UpperCAmelCase       		=    False
									for ground_truth in ground_truths:
												assert len(UpperCamelCase__			) > 0
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH
												if is_match:
															UpperCAmelCase       		=    True

									if not match_found:
												fp += 1

			UpperCAmelCase       		=    tp / (tp + fp) if tp + fp > 0 else np.nan
			UpperCAmelCase       		=    tp / (tp + fn) if tp + fn > 0 else np.nan

			return precision, recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

			UpperCAmelCase       		=    precisions[::-1]
			for i in range(1      ,      len(UpperCamelCase__			)			):
						UpperCAmelCase       		=    max(precision_best[i - 1]      ,      precision_best[i]			)
			UpperCAmelCase       		=    precision_best[::-1]
			return precisions





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    np.trapz(UpperCamelCase__      ,      UpperCamelCase__			)
			if np.isnan(UpperCamelCase__			):
						return 0
			return aupr





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    0
			for prec, recall in zip(UpperCamelCase__      ,      UpperCamelCase__			):
						if recall >= recall_thresh:
									UpperCAmelCase       		=    prec
									break
			return prec_at_recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    0
			for pred in predictions:
						for ground_truth in ground_truths:
									UpperCAmelCase       		=    metric_fn(UpperCamelCase__      ,      UpperCamelCase__			)
									if score == 1:  # break the loop when one prediction matches the ground truth
												break
						if score == 1:
									break
			return score





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    int:







			'''simple docstring'''

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

												precisions.append(UpperCamelCase__			)
												recalls.append(UpperCamelCase__			)

												if precision == 0 and recall == 0:
															fa += 0
												else:
															fa += 2 * (precision * recall) / (precision + recall)

												exact_match += metric_max_over_ground_truths(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    [x for _, x in sorted(zip(UpperCamelCase__      ,      UpperCamelCase__			)			)]
			recalls.sort()

			UpperCAmelCase       		=    100.0 * fa / total
			UpperCAmelCase       		=    100.0 * exact_match / total
			UpperCAmelCase       		=    get_aupr(UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    get_prec_at_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      recall_thresh=0.9			)
			UpperCAmelCase       		=    get_prec_at_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      recall_thresh=0.8			)

			return {
			    "exact_match": exact_match,
			    "f1": fa,
			    "aupr": aupr,
			    "prec_at_80_recall": prec_at_aa_recall,
			    "prec_at_90_recall": prec_at_aa_recall,
			}


if __name__ == "__main__":
				__A    :   Dict          =       argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__A    :   Any          =       parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__A    :   Any          =       json.load(dataset_file)
								__A    :   Optional[int]          =       dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__A    :   int          =       json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



