






'''simple docstring'''


import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = XCLIPTextConfig()

						# derive patch size from model name
						UpperCamelCase__         = model_name.find("""patch"""					)
						UpperCamelCase__         = int(model_name[start_idx + len("""patch"""					) : start_idx + len("""patch"""					) + 2]					)
						UpperCamelCase__         = XCLIPVisionConfig(patch_size=UpperCAmelCase__						,      num_frames=UpperCAmelCase__					)

						if "large" in model_name:
												UpperCamelCase__         = 768
												UpperCamelCase__         = 3072
												UpperCamelCase__         = 12

												UpperCamelCase__         = 1024
												UpperCamelCase__         = 4096
												UpperCamelCase__         = 16
												UpperCamelCase__         = 24
												UpperCamelCase__         = 768
												UpperCamelCase__         = 3072

						if model_name == "xclip-large-patch14-16-frames":
												UpperCamelCase__         = 336

						UpperCamelCase__         = XCLIPConfig.from_text_vision_configs(UpperCAmelCase__						,      UpperCAmelCase__					)

						if "large" in model_name:
												UpperCamelCase__         = 768

						return config







def 	__magic_name__( _A					):
						'''simple docstring'''





						if name == "token_embedding.weight":
												UpperCamelCase__         = name.replace("""token_embedding.weight"""						,      """text_model.embeddings.token_embedding.weight"""					)
						if name == "positional_embedding":
												UpperCamelCase__         = name.replace("""positional_embedding"""						,      """text_model.embeddings.position_embedding.weight"""					)
						if "ln_1" in name:
												UpperCamelCase__         = name.replace("""ln_1"""						,      """layer_norm1"""					)
						if "ln_2" in name:
												UpperCamelCase__         = name.replace("""ln_2"""						,      """layer_norm2"""					)
						if "c_fc" in name:
												UpperCamelCase__         = name.replace("""c_fc"""						,      """fc1"""					)
						if "c_proj" in name:
												UpperCamelCase__         = name.replace("""c_proj"""						,      """fc2"""					)
						if name.startswith("""transformer.resblocks"""					):
												UpperCamelCase__         = name.replace("""transformer.resblocks"""						,      """text_model.encoder.layers"""					)
						if "attn.out_proj" in name and "message" not in name:
												UpperCamelCase__         = name.replace("""attn.out_proj"""						,      """self_attn.out_proj"""					)
						if "ln_final" in name:
												UpperCamelCase__         = name.replace("""ln_final"""						,      """text_model.final_layer_norm"""					)
						# visual encoder
						if name == "visual.class_embedding":
												UpperCamelCase__         = name.replace("""visual.class_embedding"""						,      """vision_model.embeddings.class_embedding"""					)
						if name == "visual.positional_embedding":
												UpperCamelCase__         = name.replace("""visual.positional_embedding"""						,      """vision_model.embeddings.position_embedding.weight"""					)
						if name.startswith("""visual.transformer.resblocks"""					):
												UpperCamelCase__         = name.replace("""visual.transformer.resblocks"""						,      """vision_model.encoder.layers"""					)
						if "visual.conv1" in name:
												UpperCamelCase__         = name.replace("""visual.conv1"""						,      """vision_model.embeddings.patch_embedding"""					)
						if "visual.ln_pre" in name:
												UpperCamelCase__         = name.replace("""visual.ln_pre"""						,      """vision_model.pre_layernorm"""					)
						if "visual.ln_post" in name:
												UpperCamelCase__         = name.replace("""visual.ln_post"""						,      """vision_model.post_layernorm"""					)
						if "visual.proj" in name:
												UpperCamelCase__         = name.replace("""visual.proj"""						,      """visual_projection.weight"""					)
						if "text_projection" in name:
												UpperCamelCase__         = name.replace("""text_projection"""						,      """text_projection.weight"""					)
						# things on top
						if "prompts_visual_proj" in name:
												UpperCamelCase__         = name.replace("""prompts_visual_proj"""						,      """prompts_visual_projection"""					)
						if "prompts_visual_ln" in name:
												UpperCamelCase__         = name.replace("""prompts_visual_ln"""						,      """prompts_visual_layernorm"""					)
						# mit
						if name == "mit.positional_embedding":
												UpperCamelCase__         = name.replace("""positional"""						,      """position"""					)
						if name.startswith("""mit.resblocks"""					):
												UpperCamelCase__         = name.replace("""mit.resblocks"""						,      """mit.encoder.layers"""					)
						# prompts generator
						if name.startswith("""prompts_generator.norm"""					):
												UpperCamelCase__         = name.replace("""prompts_generator.norm"""						,      """prompts_generator.layernorm"""					)

						return name







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						for key in orig_state_dict.copy().keys():
												UpperCamelCase__         = orig_state_dict.pop(UpperCAmelCase__					)

												if "attn.in_proj" in key:
																		UpperCamelCase__         = key.split("""."""					)
																		if key.startswith("""visual"""					):
																								UpperCamelCase__         = key_split[3]
																								UpperCamelCase__         = config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				UpperCamelCase__         = val[
																																				    :dim, :
																																				]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2, :
																																				]
																																				UpperCamelCase__         = val[
																																				    -dim:, :
																																				]
																														else:
																																				UpperCamelCase__         = val[
																																				    :dim
																																				]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2
																																				]
																																				UpperCamelCase__         = val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				UpperCamelCase__         = val[
																																				    :dim, :
																																				]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2, :
																																				]
																																				UpperCamelCase__         = val[
																																				    -dim:, :
																																				]
																														else:
																																				UpperCamelCase__         = val[:dim]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2
																																				]
																																				UpperCamelCase__         = val[-dim:]
																		elif key.startswith("""mit"""					):
																								UpperCamelCase__         = key_split[2]
																								UpperCamelCase__         = config.vision_config.mit_hidden_size
																								if "weight" in key:
																														UpperCamelCase__         = val[:dim, :]
																														UpperCamelCase__         = val[dim : dim * 2, :]
																														UpperCamelCase__         = val[-dim:, :]
																								else:
																														UpperCamelCase__         = val[:dim]
																														UpperCamelCase__         = val[dim : dim * 2]
																														UpperCamelCase__         = val[-dim:]
																		else:
																								UpperCamelCase__         = key_split[2]
																								UpperCamelCase__         = config.text_config.hidden_size
																								if "weight" in key:
																														UpperCamelCase__         = val[:dim, :]
																														UpperCamelCase__         = val[
																														    dim : dim * 2, :
																														]
																														UpperCamelCase__         = val[-dim:, :]
																								else:
																														UpperCamelCase__         = val[:dim]
																														UpperCamelCase__         = val[
																														    dim : dim * 2
																														]
																														UpperCamelCase__         = val[-dim:]
												else:
																		UpperCamelCase__         = rename_key(UpperCAmelCase__					)
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								UpperCamelCase__         = val.T
																		UpperCamelCase__         = val

						return orig_state_dict







def 	__magic_name__( _A					):
						'''simple docstring'''





						if num_frames == 8:
												UpperCamelCase__         = """eating_spaghetti_8_frames.npy"""
						elif num_frames == 16:
												UpperCamelCase__         = """eating_spaghetti.npy"""
						elif num_frames == 32:
												UpperCamelCase__         = """eating_spaghetti_32_frames.npy"""
						UpperCamelCase__         = hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""						,      filename=UpperCAmelCase__						,      repo_type="""dataset"""						,      )
						UpperCamelCase__         = np.load(UpperCAmelCase__					)
						return list(UpperCAmelCase__					)







def 	__magic_name__( _A						,      _A=None						,      _A=False					):
						'''simple docstring'''





						UpperCamelCase__         = {
						    # fully supervised kinetics-400 checkpoints
						    """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
						    """xclip-base-patch32-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
						    ),
						    """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
						    """xclip-base-patch16-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
						    ),
						    """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
						    """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
						    # fully supervised kinetics-600 checkpoints
						    """xclip-base-patch16-kinetics-600""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
						    ),
						    """xclip-base-patch16-kinetics-600-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
						    ),
						    """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
						    # few shot
						    """xclip-base-patch16-hmdb-2-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
						    ),
						    """xclip-base-patch16-hmdb-4-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
						    ),
						    """xclip-base-patch16-hmdb-8-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
						    ),
						    """xclip-base-patch16-hmdb-16-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
						    ),
						    """xclip-base-patch16-ucf-2-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
						    ),
						    """xclip-base-patch16-ucf-4-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
						    ),
						    """xclip-base-patch16-ucf-8-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
						    ),
						    """xclip-base-patch16-ucf-16-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
						    ),
						    # zero shot
						    """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
						}

						UpperCamelCase__         = model_to_url[model_name]
						UpperCamelCase__         = 8
						if "16-frames" in model_name:
												UpperCamelCase__         = 16
						elif "shot" in model_name:
												UpperCamelCase__         = 32

						UpperCamelCase__         = get_xclip_config(UpperCAmelCase__						,      UpperCAmelCase__					)
						UpperCamelCase__         = XCLIPModel(UpperCAmelCase__					)
						model.eval()

						if "drive" in checkpoint_url:
												UpperCamelCase__         = """pytorch_model.bin"""
												gdown.cached_download(UpperCAmelCase__						,      UpperCAmelCase__						,      quiet=UpperCAmelCase__					)
												UpperCamelCase__         = torch.load(UpperCAmelCase__						,      map_location="""cpu"""					)["""model"""]
						else:
												UpperCamelCase__         = torch.hub.load_state_dict_from_url(UpperCAmelCase__					)["""model"""]

						UpperCamelCase__         = convert_state_dict(UpperCAmelCase__						,      UpperCAmelCase__					)

						UpperCamelCase__         = XCLIPModel(UpperCAmelCase__					)
						UpperCamelCase__         = model.load_state_dict(UpperCAmelCase__						,      strict=UpperCAmelCase__					)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						UpperCamelCase__         = 336 if model_name == """xclip-large-patch14-16-frames""" else 224
						UpperCamelCase__         = VideoMAEImageProcessor(size=UpperCAmelCase__					)
						UpperCamelCase__         = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""					)
						UpperCamelCase__         = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""					)
						UpperCamelCase__         = XCLIPProcessor(image_processor=UpperCAmelCase__						,      tokenizer=UpperCAmelCase__					)

						UpperCamelCase__         = prepare_video(UpperCAmelCase__					)
						UpperCamelCase__         = processor(
						    text=["""playing sports""", """eating spaghetti""", """go shopping"""]						,      videos=UpperCAmelCase__						,      return_tensors="""pt"""						,      padding=UpperCAmelCase__					)

						print("""Shape of pixel values:"""						,      inputs.pixel_values.shape					)

						with torch.no_grad():
												UpperCamelCase__         = model(**UpperCAmelCase__					)

						# Verify outputs
						UpperCamelCase__         = outputs.logits_per_video
						UpperCamelCase__         = logits_per_video.softmax(dim=1					)
						print("""Probs:"""						,      UpperCAmelCase__					)
						# kinetics-400
						if model_name == "xclip-base-patch32":
												UpperCamelCase__         = torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]					)
						elif model_name == "xclip-base-patch32-16-frames":
												UpperCamelCase__         = torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]					)
						elif model_name == "xclip-base-patch16":
												UpperCamelCase__         = torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]					)
						elif model_name == "xclip-base-patch16-16-frames":
												UpperCamelCase__         = torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]					)
						elif model_name == "xclip-large-patch14":
												UpperCamelCase__         = torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]					)
						elif model_name == "xclip-large-patch14-16-frames":
												UpperCamelCase__         = torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]					)
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												UpperCamelCase__         = torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]					)
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												UpperCamelCase__         = torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]					)
						elif model_name == "xclip-large-patch14-kinetics-600":
												UpperCamelCase__         = torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]					)
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												UpperCamelCase__         = torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]					)
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												UpperCamelCase__         = torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]					)
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												UpperCamelCase__         = torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]					)
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												UpperCamelCase__         = torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]					)
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												UpperCamelCase__         = torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]					)
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												UpperCamelCase__         = torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]					)
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												UpperCamelCase__         = torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]					)
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												UpperCamelCase__         = torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]					)
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												UpperCamelCase__         = torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]					)
						else:
												raise ValueError(f"Model name {model_name} not supported"					)
						assert torch.allclose(UpperCAmelCase__						,      UpperCAmelCase__						,      atol=1e-3					)
						print("""Looks ok!"""					)

						if pytorch_dump_folder_path is not None:
												print(f"Saving model {model_name} to {pytorch_dump_folder_path}"					)
												model.save_pretrained(UpperCAmelCase__					)

						if push_to_hub:
												print("""Pushing model, processor and slow tokenizer files to the hub..."""					)
												model.push_to_hub(UpperCAmelCase__						,      organization="""nielsr"""					)
												processor.push_to_hub(UpperCAmelCase__						,      organization="""nielsr"""					)
												slow_tokenizer.push_to_hub(UpperCAmelCase__						,      organization="""nielsr"""					)


if __name__ == "__main__":
					lowerCamelCase_				: Any    	=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--model_name''',
					    default='''xclip-base-patch32''',
					    type=str,
					    help='''Name of the model.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)

					lowerCamelCase_				: Optional[Any]    	=							parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)














'''simple docstring'''


import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


lowerCamelCase_				: Union[str, Any]    	=							re.compile(r'''\b(a|an|the)\b''', re.UNICODE)

lowerCamelCase_				: int    	=							None







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0."""					)
						parser.add_argument("""data_file"""						,      metavar="""data.json"""						,      help="""Input data JSON file."""					)
						parser.add_argument("""pred_file"""						,      metavar="""pred.json"""						,      help="""Model predictions."""					)
						parser.add_argument(
						    """--out-file"""						,      """-o"""						,      metavar="""eval.json"""						,      help="""Write accuracy metrics to file (default is stdout)."""					)
						parser.add_argument(
						    """--na-prob-file"""						,      """-n"""						,      metavar="""na_prob.json"""						,      help="""Model estimates of probability of no answer."""					)
						parser.add_argument(
						    """--na-prob-thresh"""						,      """-t"""						,      type=_A						,      default=1.0						,      help="""Predict \"\" if no-answer probability exceeds this (default = 1.0)."""						,      )
						parser.add_argument(
						    """--out-image-dir"""						,      """-p"""						,      metavar="""out_images"""						,      default=_A						,      help="""Save precision-recall curves to directory."""					)
						parser.add_argument("""--verbose"""						,      """-v"""						,      action="""store_true"""					)
						if len(sys.argv					) == 1:
												parser.print_help()
												sys.exit(1					)
						return parser.parse_args()







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = bool(qa["""answers"""]["""text"""]					)
						return qid_to_has_ans







def 	__magic_name__( _A					):
						'''simple docstring'''





						def remove_articles(_A					):
												return ARTICLES_REGEX.sub(""" """						,      _A					)

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(_A					)					)					)					)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if not s:
												return []
						return normalize_answer(_A					).split()







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						return int(normalize_answer(_A					) == normalize_answer(_A					)					)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = collections.Counter(_A					) & collections.Counter(_A					)
						UpperCamelCase__         = sum(common.values()					)
						if len(_A					) == 0 or len(_A					) == 0:
												# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
												return int(gold_toks == pred_toks					)
						if num_same == 0:
												return 0
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = (2 * precision * recall) / (precision + recall)
						return fa







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = qa["""id"""]
																								UpperCamelCase__         = [t for t in qa["""answers"""]["""text"""] if normalize_answer(_A					)]
																								if not gold_answers:
																														# For unanswerable questions, only correct answer is empty string
																														UpperCamelCase__         = [""""""]
																								if qid not in preds:
																														print(f"Missing prediction for {qid}"					)
																														continue
																								UpperCamelCase__         = preds[qid]
																								# Take max over all gold answers
																								UpperCamelCase__         = max(compute_exact(_A						,      _A					) for a in gold_answers					)
																								UpperCamelCase__         = max(compute_fa(_A						,      _A					) for a in gold_answers					)
						return exact_scores, fa_scores







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for qid, s in scores.items():
												UpperCamelCase__         = na_probs[qid] > na_prob_thresh
												if pred_na:
																		UpperCamelCase__         = float(not qid_to_has_ans[qid]					)
												else:
																		UpperCamelCase__         = s
						return new_scores







def 	__magic_name__( _A						,      _A						,      _A=None					):
						'''simple docstring'''





						if not qid_list:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores.values()					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores.values()					) / total),
												        ("""total""", total),
												    ]					)
						else:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores[k] for k in qid_list					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores[k] for k in qid_list					) / total),
												        ("""total""", total),
												    ]					)







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						for k in new_eval:
												UpperCamelCase__         = new_eval[k]







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						plt.step(_A						,      _A						,      color="""b"""						,      alpha=0.2						,      where="""post"""					)
						plt.fill_between(_A						,      _A						,      step="""post"""						,      alpha=0.2						,      color="""b"""					)
						plt.xlabel("""Recall"""					)
						plt.ylabel("""Precision"""					)
						plt.xlim([0.0, 1.0_5]					)
						plt.ylim([0.0, 1.0_5]					)
						plt.title(_A					)
						plt.savefig(_A					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A=None						,      _A=None					):
						'''simple docstring'''





						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = 1.0
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = [1.0]
						UpperCamelCase__         = [0.0]
						UpperCamelCase__         = 0.0
						for i, qid in enumerate(_A					):
												if qid_to_has_ans[qid]:
																		true_pos += scores[qid]
												UpperCamelCase__         = true_pos / float(i + 1					)
												UpperCamelCase__         = true_pos / float(_A					)
												if i == len(_A					) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
																		# i.e., if we can put a threshold after this point
																		avg_prec += cur_p * (cur_r - recalls[-1])
																		precisions.append(_A					)
																		recalls.append(_A					)
						if out_image:
												plot_pr_curve(_A						,      _A						,      _A						,      _A					)
						return {"ap": 1_0_0.0 * avg_prec}







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if out_image_dir and not os.path.exists(_A					):
												os.makedirs(_A					)
						UpperCamelCase__         = sum(1 for v in qid_to_has_ans.values() if v					)
						if num_true_pos == 0:
												return
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_exact.png"""					)						,      title="""Precision-Recall curve for Exact Match score"""						,      )
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_f1.png"""					)						,      title="""Precision-Recall curve for F1 score"""						,      )
						UpperCamelCase__         = {k: float(_A					) for k, v in qid_to_has_ans.items()}
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_oracle.png"""					)						,      title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"""						,      )
						merge_eval(_A						,      _A						,      """pr_exact"""					)
						merge_eval(_A						,      _A						,      """pr_f1"""					)
						merge_eval(_A						,      _A						,      """pr_oracle"""					)







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if not qid_list:
												return
						UpperCamelCase__         = [na_probs[k] for k in qid_list]
						UpperCamelCase__         = np.ones_like(_A					) / float(len(_A					)					)
						plt.hist(_A						,      weights=_A						,      bins=20						,      range=(0.0, 1.0)					)
						plt.xlabel("""Model probability of no-answer"""					)
						plt.ylabel("""Proportion of dataset"""					)
						plt.title(f"Histogram of no-answer probability: {name}"					)
						plt.savefig(os.path.join(_A						,      f"na_prob_hist_{name}.png"					)					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]					)
						UpperCamelCase__         = num_no_ans
						UpperCamelCase__         = cur_score
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						for i, qid in enumerate(_A					):
												if qid not in scores:
																		continue
												if qid_to_has_ans[qid]:
																		UpperCamelCase__         = scores[qid]
												else:
																		if preds[qid]:
																								UpperCamelCase__         = -1
																		else:
																								UpperCamelCase__         = 0
												cur_score += diff
												if cur_score > best_score:
																		UpperCamelCase__         = cur_score
																		UpperCamelCase__         = na_probs[qid]
						return 1_0_0.0 * best_score / len(_A					), best_thresh







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__         = best_exact
						UpperCamelCase__         = exact_thresh
						UpperCamelCase__         = best_fa
						UpperCamelCase__         = fa_thresh







def 	__magic_name__( ):
						'''simple docstring'''





						with open(OPTS.data_file					) as f:
												UpperCamelCase__         = json.load(_A					)
												UpperCamelCase__         = dataset_json["""data"""]
						with open(OPTS.pred_file					) as f:
												UpperCamelCase__         = json.load(_A					)
						if OPTS.na_prob_file:
												with open(OPTS.na_prob_file					) as f:
																		UpperCamelCase__         = json.load(_A					)
						else:
												UpperCamelCase__         = {k: 0.0 for k in preds}
						UpperCamelCase__         = make_qid_to_has_ans(_A					)  # maps qid to True/False
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if v]
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if not v]
						UpperCamelCase__    ,   UpperCamelCase__         = get_raw_scores(_A						,      _A					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = make_eval_dict(_A						,      _A					)
						if has_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """HasAns"""					)
						if no_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """NoAns"""					)
						if OPTS.na_prob_file:
												find_all_best_thresh(_A						,      _A						,      _A						,      _A						,      _A						,      _A					)
						if OPTS.na_prob_file and OPTS.out_image_dir:
												run_precision_recall_analysis(_A						,      _A						,      _A						,      _A						,      _A						,      OPTS.out_image_dir					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """hasAns"""					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """noAns"""					)
						if OPTS.out_file:
												with open(OPTS.out_file						,      """w"""					) as f:
																		json.dump(_A						,      _A					)
						else:
												print(json.dumps(_A						,      indent=2					)					)


if __name__ == "__main__":
					lowerCamelCase_				: str    	=							parse_args()
					if OPTS.out_image_dir:
										import matplotlib

										matplotlib.use('''Agg''')
										import matplotlib.pyplot as plt
					main()
