


"""simple docstring"""







import argparse
import json
import re
import string
import sys

import numpy as np


__a			 =   0.5






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

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

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

						UpperCAmelCase__		=    ground_truth.lower(), prediction.lower()
						UpperCAmelCase__		=    ground_truth.replace('''/'''	,			''' ''' ), prediction.replace('''/'''	,			''' ''' )
						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 					a__ (       _UpperCamelCase ):





						"""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 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						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 					a__ (       _UpperCamelCase ):





						"""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 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""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 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						return normalize_answer(UpperCamelCase__ ) == normalize_answer(UpperCamelCase__ )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""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 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						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__		=    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			 =   argparse.ArgumentParser(description="Evaluation for CUAD")
				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)
								__a			 =   dataset_json['data']
				with open(args.prediction_file) as prediction_file:
								__a			 =   json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



"""simple docstring"""







import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

						with open(_UpperCamelCase	,			'''wb''' ) as f:
												with requests.get(_UpperCamelCase	,			stream=_UpperCamelCase ) as req:
																		for chunk in req.iter_content(chunk_size=2048 ):
																								f.write(_UpperCamelCase )






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    VivitConfig()

						UpperCAmelCase__		=    400
						UpperCAmelCase__		=    '''huggingface/label-files'''
						UpperCAmelCase__		=    '''kinetics400-id2label.json'''

						UpperCAmelCase__		=    json.load(open(hf_hub_download(_UpperCamelCase	,			_UpperCamelCase	,			repo_type='''dataset''' )	,			'''r''' ) )
						UpperCAmelCase__		=    {int(_UpperCamelCase ): v for k, v in idalabel.items()}
						UpperCAmelCase__		=    idalabel
						UpperCAmelCase__		=    {v: k for k, v in idalabel.items()}
						return config






def 					a__ (       ):





						"""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(_UpperCamelCase )
						return list(_UpperCamelCase )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if np.ndim(_UpperCamelCase ) == 2:
												return transform_attention_bias(_UpperCamelCase )

						elif np.ndim(_UpperCamelCase ) == 3:
												return transform_attention_kernel(_UpperCamelCase )

						else:
												raise Exception(F"""Invalid number of dimesions: {np.ndim(_UpperCamelCase )}""" )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return current.flatten()






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return np.reshape(_UpperCamelCase	,			(current.shape[0], current.shape[1] * current.shape[2]) ).T






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return np.reshape(_UpperCamelCase	,			(current.shape[0] * current.shape[1], current.shape[2]) ).T






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer'''][F"""encoderblock_{i}"""]

						UpperCAmelCase__		=    F"""encoder.layer.{i}."""
						UpperCAmelCase__		=    {
						    prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
						    prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel'''] ),
						    prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
						    prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel'''] ),
						    prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
						    prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
						    prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
						    prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
						    prefix
						    + '''attention.attention.query.bias''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''query''']['''bias'''] ),
						    prefix
						    + '''attention.attention.query.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel'''] ),
						    prefix
						    + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias'''] ),
						    prefix
						    + '''attention.attention.key.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel'''] ),
						    prefix
						    + '''attention.attention.value.bias''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''value''']['''bias'''] ),
						    prefix
						    + '''attention.attention.value.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel'''] ),
						    prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
						    prefix
						    + '''attention.output.dense.weight''': transform_attention_output_weight(
						        state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel'''] ),
						}

						return new_state






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()] )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=False ):





						"""simple docstring"""

						UpperCAmelCase__		=    get_n_layers(_UpperCamelCase )

						UpperCAmelCase__		=    OrderedDict()

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

						UpperCAmelCase__		=    np.transpose(
						    state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']	,			(4, 3, 0, 1, 2) )
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''cls''']
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
						    '''pos_embedding'''
						]

						for i in range(_UpperCamelCase ):
												new_state.update(transform_state_encoder_block(_UpperCamelCase	,			_UpperCamelCase ) )

						if classification_head:
												UpperCAmelCase__		=    {'''vivit.''' + k: v for k, v in new_state.items()}
												UpperCAmelCase__		=    np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel'''] )
												UpperCAmelCase__		=    np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias'''] )

						return {k: torch.tensor(_UpperCamelCase ) for k, v in new_state.items()}






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    VivitImageProcessor()

						assert extractor.do_resize is True
						assert extractor.size == {"shortest_edge": 256}
						assert extractor.do_center_crop is True
						assert extractor.crop_size == {"width": 224, "height": 224}
						assert extractor.resample == PILImageResampling.BILINEAR

						# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
						# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
						# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
						assert extractor.do_normalize is False
						assert extractor.do_rescale is True
						assert extractor.rescale_factor == 1 / 255

						# zero-centering = True in original implementation
						assert extractor.do_zero_centering is True

						return extractor






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''checkpoint'''

						if not os.path.exists(_UpperCamelCase ):
												download_checkpoint(_UpperCamelCase )

						UpperCAmelCase__		=    restore_checkpoint(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    transform_state(_UpperCamelCase	,			classification_head=_UpperCamelCase )

						UpperCAmelCase__		=    get_vivit_config()

						assert config.image_size == 224
						assert config.num_frames == 32

						UpperCAmelCase__		=    VivitForVideoClassification(_UpperCamelCase )
						model.load_state_dict(_UpperCamelCase )
						model.eval()

						UpperCAmelCase__		=    get_processor()

						UpperCAmelCase__		=    prepare_video()
						UpperCAmelCase__		=    extractor(_UpperCamelCase	,			return_tensors='''pt''' )

						UpperCAmelCase__		=    model(**_UpperCamelCase )

						UpperCAmelCase__		=    torch.Size([1, 400] )
						UpperCAmelCase__		=    torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658] )

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :5]	,			_UpperCamelCase	,			atol=1E-4 ), outputs.logits[0, :5]

						model.save_pretrained(_UpperCamelCase )
						extractor.save_pretrained(_UpperCamelCase )


if __name__ == "__main__":
						__a			 =   argparse.ArgumentParser()

						parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

						__a			 =   parser.parse_args()
						convert(args.output_model_name)