

'''simple docstring'''




import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_a			:	Optional[Any]								=  logging.get_logger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
		UpperCAmelCase  							=		tf.image.resize(SCREAMING_SNAKE_CASE    ,				(346, 346)  )
		UpperCAmelCase  							=		tf.image.crop_to_bounding_box(SCREAMING_SNAKE_CASE    ,				(346 - 289) // 2    ,				(346 - 289) // 2    ,				289    ,				289  )
		return image


def  lowerCamelCase__						(  ):
		UpperCAmelCase  							=		EfficientNetConfig.from_pretrained('google/efficientnet-b7'  )
		UpperCAmelCase  							=		289
		UpperCAmelCase  							=		640
		UpperCAmelCase  							=		{'0': 'LABEL_0', '1': 'LABEL_1'}
		UpperCAmelCase  							=		{'LABEL_0': 0, 'LABEL_1': 1}
		UpperCAmelCase  							=		[]

		UpperCAmelCase  							=		BertConfig()
		UpperCAmelCase  							=		AlignConfig.from_text_vision_configs(
		    text_config=SCREAMING_SNAKE_CASE    ,				vision_config=SCREAMING_SNAKE_CASE    ,				projection_dim=640  )
		return config


def  lowerCamelCase__						(  ):
		UpperCAmelCase  							=		'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCAmelCase  							=		Image.open(requests.get(SCREAMING_SNAKE_CASE    ,				stream=SCREAMING_SNAKE_CASE  ).raw  )
		return im


def  lowerCamelCase__						(  ):
		UpperCAmelCase  							=		EfficientNetImageProcessor(
		    do_center_crop=SCREAMING_SNAKE_CASE    ,				rescale_factor=1 / 127.5    ,				rescale_offset=SCREAMING_SNAKE_CASE    ,				do_normalize=SCREAMING_SNAKE_CASE    ,				include_top=SCREAMING_SNAKE_CASE    ,				resample=Image.BILINEAR    ,				)
		UpperCAmelCase  							=		BertTokenizer.from_pretrained('bert-base-uncased'  )
		UpperCAmelCase  							=		64
		UpperCAmelCase  							=		AlignProcessor(image_processor=SCREAMING_SNAKE_CASE    ,				tokenizer=SCREAMING_SNAKE_CASE  )
		return processor


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict  ):
		# EfficientNet image encoder
		UpperCAmelCase  							=		[v.split('_'  )[0].split('block'  )[1] for v in original_param_names if v.startswith('block'  )]
		UpperCAmelCase  							=		list(set(SCREAMING_SNAKE_CASE  )  )
		UpperCAmelCase  							=		sorted(SCREAMING_SNAKE_CASE  )
		UpperCAmelCase  							=		len(SCREAMING_SNAKE_CASE  )
		UpperCAmelCase  							=		{b: str(SCREAMING_SNAKE_CASE  ) for b, i in zip(SCREAMING_SNAKE_CASE    ,				range(SCREAMING_SNAKE_CASE  )  )}

		UpperCAmelCase  							=		[]
		rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')  )
		rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')  )
		rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')  )
		rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')  )
		rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')  )

		for b in block_names:
				UpperCAmelCase  							=		block_name_mapping[b]
				rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')  )
				rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')  )
				rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')  )
				rename_keys.append(
				    (f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')  )
				rename_keys.append(
				    (f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')  )
				rename_keys.append(
				    (f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')  )
				rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')  )
				rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')  )
				rename_keys.append(
				    (f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')  )
				rename_keys.append(
				    (f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')  )

				rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')  )
				rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')  )
				rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')  )
				rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')  )
				rename_keys.append(
				    (f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''')  )
				rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''')  )
				rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''')  )
				rename_keys.append(
				    (f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')  )
				rename_keys.append(
				    (f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')  )

		UpperCAmelCase  							=		{}
		for item in rename_keys:
				if item[0] in original_param_names:
						UpperCAmelCase  							=		'vision_model.' + item[1]

				# BERT text encoder
		UpperCAmelCase  							=		[]
		UpperCAmelCase  							=		'tf_bert_model/bert'
		UpperCAmelCase  							=		'text_model'
		for i in range(12  ):
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
				        f'''{new}.encoder.layer.{i}.attention.self.query.weight''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
				        f'''{new}.encoder.layer.{i}.attention.self.query.bias''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
				        f'''{new}.encoder.layer.{i}.attention.self.key.weight''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
				        f'''{new}.encoder.layer.{i}.attention.self.key.bias''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
				        f'''{new}.encoder.layer.{i}.attention.self.value.weight''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
				        f'''{new}.encoder.layer.{i}.attention.self.value.bias''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
				        f'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
				        f'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
				        f'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
				        f'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
				        f'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
				    )  )
				rename_keys.append(
				    (
				        f'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
				        f'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
				    )  )
				rename_keys.append(
				    (f'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', f'''{new}.encoder.layer.{i}.output.dense.weight''')  )
				rename_keys.append(
				    (f'''{old}/encoder/layer_._{i}/output/dense/bias:0''', f'''{new}.encoder.layer.{i}.output.dense.bias''')  )
				rename_keys.append(
				    (f'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', f'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')  )
				rename_keys.append(
				    (f'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', f'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')  )

		rename_keys.append((f'''{old}/embeddings/word_embeddings/weight:0''', f'''{new}.embeddings.word_embeddings.weight''')  )
		rename_keys.append(
		    (f'''{old}/embeddings/position_embeddings/embeddings:0''', f'''{new}.embeddings.position_embeddings.weight''')  )
		rename_keys.append(
		    (f'''{old}/embeddings/token_type_embeddings/embeddings:0''', f'''{new}.embeddings.token_type_embeddings.weight''')  )
		rename_keys.append((f'''{old}/embeddings/LayerNorm/gamma:0''', f'''{new}.embeddings.LayerNorm.weight''')  )
		rename_keys.append((f'''{old}/embeddings/LayerNorm/beta:0''', f'''{new}.embeddings.LayerNorm.bias''')  )

		rename_keys.append((f'''{old}/pooler/dense/kernel:0''', f'''{new}.pooler.dense.weight''')  )
		rename_keys.append((f'''{old}/pooler/dense/bias:0''', f'''{new}.pooler.dense.bias''')  )
		rename_keys.append(('dense/kernel:0', 'text_projection.weight')  )
		rename_keys.append(('dense/bias:0', 'text_projection.bias')  )
		rename_keys.append(('dense/bias:0', 'text_projection.bias')  )
		rename_keys.append(('temperature:0', 'temperature')  )

		for item in rename_keys:
				if item[0] in original_param_names:
						UpperCAmelCase  							=		item[1]
		return key_mapping


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				Optional[Any]  ):
		list(hf_params.keys()  )

		for key, value in tf_params.items():
				if key not in key_mapping:
						continue

				UpperCAmelCase  							=		key_mapping[key]
				if "_conv" in key and "kernel" in key:
						UpperCAmelCase  							=		torch.from_numpy(SCREAMING_SNAKE_CASE  ).permute(3    ,				2    ,				0    ,				1  )
				elif "embeddings" in key:
						UpperCAmelCase  							=		torch.from_numpy(SCREAMING_SNAKE_CASE  )
				elif "depthwise_kernel" in key:
						UpperCAmelCase  							=		torch.from_numpy(SCREAMING_SNAKE_CASE  ).permute(2    ,				3    ,				0    ,				1  )
				elif "kernel" in key:
						UpperCAmelCase  							=		torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE  )  )
				elif "temperature" in key:
						UpperCAmelCase  							=		value
				elif "bn/gamma" or "bn/beta" in key:
						UpperCAmelCase  							=		torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE  )  ).squeeze()
				else:
						UpperCAmelCase  							=		torch.from_numpy(SCREAMING_SNAKE_CASE  )

				# Replace HF parameters with original TF model parameters
				hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE  )


@torch.no_grad()
def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				int  ):
		UpperCAmelCase  							=		64
		UpperCAmelCase  							=		Tokenizer(SCREAMING_SNAKE_CASE  )
		UpperCAmelCase  							=		align.Align('efficientnet-b7'    ,				'bert-base'    ,				640    ,				SCREAMING_SNAKE_CASE    ,				tok.get_vocab_size()  )
		original_model.compile()
		original_model.load_weights(SCREAMING_SNAKE_CASE  )

		UpperCAmelCase  							=		original_model.trainable_variables
		UpperCAmelCase  							=		original_model.non_trainable_variables
		UpperCAmelCase  							=		{param.name: param.numpy() for param in tf_params}
		for param in tf_non_train_params:
				UpperCAmelCase  							=		param.numpy()
		UpperCAmelCase  							=		list(tf_params.keys()  )

		# Load HuggingFace model
		UpperCAmelCase  							=		get_align_config()
		UpperCAmelCase  							=		AlignModel(SCREAMING_SNAKE_CASE  ).eval()
		UpperCAmelCase  							=		hf_model.state_dict()

		# Create src-to-dst parameter name mapping dictionary
		print('Converting parameters...'  )
		UpperCAmelCase  							=		rename_keys(SCREAMING_SNAKE_CASE  )
		replace_params(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )

		# Initialize processor
		UpperCAmelCase  							=		get_processor()
		UpperCAmelCase  							=		processor(
		    images=prepare_img()    ,				text='A picture of a cat'    ,				padding='max_length'    ,				max_length=64    ,				return_tensors='pt'  )

		# HF model inference
		hf_model.eval()
		with torch.no_grad():
				UpperCAmelCase  							=		hf_model(**SCREAMING_SNAKE_CASE  )

		UpperCAmelCase  							=		outputs.image_embeds.detach().numpy()
		UpperCAmelCase  							=		outputs.text_embeds.detach().numpy()

		# Original model inference
		UpperCAmelCase  							=		False
		UpperCAmelCase  							=		EfficientNetImageProcessor(
		    do_center_crop=SCREAMING_SNAKE_CASE    ,				do_rescale=SCREAMING_SNAKE_CASE    ,				do_normalize=SCREAMING_SNAKE_CASE    ,				include_top=SCREAMING_SNAKE_CASE    ,				resample=Image.BILINEAR    ,				)
		UpperCAmelCase  							=		tf_image_processor(images=prepare_img()    ,				return_tensors='tf'    ,				data_format='channels_last'  )['pixel_values']
		UpperCAmelCase  							=		tok(tf.constant(['A picture of a cat']  )  )

		UpperCAmelCase  							=		original_model.image_encoder(SCREAMING_SNAKE_CASE    ,				training=SCREAMING_SNAKE_CASE  )
		UpperCAmelCase  							=		original_model.text_encoder(SCREAMING_SNAKE_CASE    ,				training=SCREAMING_SNAKE_CASE  )

		UpperCAmelCase  							=		tf.nn.la_normalize(SCREAMING_SNAKE_CASE    ,				axis=-1  )
		UpperCAmelCase  							=		tf.nn.la_normalize(SCREAMING_SNAKE_CASE    ,				axis=-1  )

		# Check whether original and HF model outputs match  -> np.allclose
		if not np.allclose(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				atol=1E-3  ):
				raise ValueError('The predicted image features are not the same.'  )
		if not np.allclose(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				atol=1E-3  ):
				raise ValueError('The predicted text features are not the same.'  )
		print('Model outputs match!'  )

		if save_model:
				# Create folder to save model
				if not os.path.isdir(SCREAMING_SNAKE_CASE  ):
						os.mkdir(SCREAMING_SNAKE_CASE  )
				# Save converted model and image processor
				hf_model.save_pretrained(SCREAMING_SNAKE_CASE  )
				processor.save_pretrained(SCREAMING_SNAKE_CASE  )

		if push_to_hub:
				# Push model and image processor to hub
				print('Pushing converted ALIGN to the hub...'  )
				processor.push_to_hub('align-base'  )
				hf_model.push_to_hub('align-base'  )


if __name__ == "__main__":
		_a			:	Tuple								=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_path',
		    default='./weights/model-weights',
		    type=str,
		    help='Path to the pretrained TF ALIGN checkpoint.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='hf_model',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--save_model', action='store_true', help='Save model to local')
		parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

		_a			:	Any								=  parser.parse_args()
		convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





'''simple docstring'''




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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]  ):

  def remove_articles(SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
    return re.sub(R'\b(a|an|the)\b'    ,				' '    ,				SCREAMING_SNAKE_CASE  )

  def white_space_fix(SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
    return " ".join(text.split()  )

  def remove_punc(SCREAMING_SNAKE_CASE :				List[Any]  ):
    UpperCAmelCase  							=		set(string.punctuation  )
    return "".join(ch for ch in text if ch not in exclude  )

  def lower(SCREAMING_SNAKE_CASE :				Any  ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE  )  )  )  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		normalize_answer(SCREAMING_SNAKE_CASE  ).split()
  UpperCAmelCase  							=		normalize_answer(SCREAMING_SNAKE_CASE  ).split()
  UpperCAmelCase  							=		Counter(SCREAMING_SNAKE_CASE  ) & Counter(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		sum(common.values()  )
  if num_same == 0:
    return 0
  UpperCAmelCase  							=		1.0 * num_same / len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		1.0 * num_same / len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		(2 * precision * recall) / (precision + recall)
  return fa


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				int  ):
  return normalize_answer(SCREAMING_SNAKE_CASE  ) == normalize_answer(SCREAMING_SNAKE_CASE  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		[]
  for ground_truth in ground_truths:
    UpperCAmelCase  							=		metric_fn(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
    scores_for_ground_truths.append(SCREAMING_SNAKE_CASE  )
  return max(SCREAMING_SNAKE_CASE  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  UpperCAmelCase  							=		UpperCAmelCase  							=		UpperCAmelCase  							=		0
  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(SCREAMING_SNAKE_CASE    ,				file=sys.stderr  )
          continue
        UpperCAmelCase  							=		[x['text'] for x in qa['answers']]
        UpperCAmelCase  							=		predictions[qa['id']]
        exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
        fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		100.0 * exact_match / total
  UpperCAmelCase  							=		100.0 * fa / total

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


if __name__ == "__main__":
  _a			:	Tuple								=  '1.1'
  _a			:	int								=  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			:	List[Any]								=  parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    _a			:	Optional[int]								=  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			:	Optional[Any]								=  dataset_json['data']
  with open(args.prediction_file) as prediction_file:
    _a			:	Optional[Any]								=  json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))


