
"""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()
snake_case					  =						logging.get_logger(__name__)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      tf.image.resize(SCREAMING_SNAKE_CASE_,	(3_4_6, 3_4_6)     )
					SCREAMING_SNAKE_CASE           =      tf.image.crop_to_bounding_box(SCREAMING_SNAKE_CASE_,	(3_4_6 - 2_8_9) // 2,	(3_4_6 - 2_8_9) // 2,	2_8_9,	2_8_9     )
					return image

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      EfficientNetConfig.from_pretrained('google/efficientnet-b7'     )
					SCREAMING_SNAKE_CASE           =      2_8_9
					SCREAMING_SNAKE_CASE           =      6_4_0
					SCREAMING_SNAKE_CASE           =      {'0': 'LABEL_0', '1': 'LABEL_1'}
					SCREAMING_SNAKE_CASE           =      {'LABEL_0': 0, 'LABEL_1': 1}
					SCREAMING_SNAKE_CASE           =      []

					SCREAMING_SNAKE_CASE           =      BertConfig()
					SCREAMING_SNAKE_CASE           =      AlignConfig.from_text_vision_configs(
					    text_config=SCREAMING_SNAKE_CASE_,	vision_config=SCREAMING_SNAKE_CASE_,	projection_dim=6_4_0     )
					return config

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

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      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,	)
					SCREAMING_SNAKE_CASE           =      BertTokenizer.from_pretrained('bert-base-uncased'     )
					SCREAMING_SNAKE_CASE           =      6_4
					SCREAMING_SNAKE_CASE           =      AlignProcessor(image_processor=SCREAMING_SNAKE_CASE_,	tokenizer=SCREAMING_SNAKE_CASE_     )
					return processor

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [v.split('_'     )[0].split('block'     )[1] for v in original_param_names if v.startswith('block'     )]
					SCREAMING_SNAKE_CASE           =      list(set(SCREAMING_SNAKE_CASE_     )     )
					SCREAMING_SNAKE_CASE           =      sorted(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      {b: str(SCREAMING_SNAKE_CASE_     ) for b, i in zip(SCREAMING_SNAKE_CASE_,	range(SCREAMING_SNAKE_CASE_     )     )}

					SCREAMING_SNAKE_CASE           =      []
					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:
										SCREAMING_SNAKE_CASE           =      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''')     )

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

    # BERT text encoder
					SCREAMING_SNAKE_CASE           =      []
					SCREAMING_SNAKE_CASE           =      'tf_bert_model/bert'
					SCREAMING_SNAKE_CASE           =      'text_model'
					for i in range(1_2     ):
										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:
															SCREAMING_SNAKE_CASE           =      item[1]
					return key_mapping

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					list(hf_params.keys()     )

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

										SCREAMING_SNAKE_CASE           =      key_mapping[key]
										if "_conv" in key and "kernel" in key:
															SCREAMING_SNAKE_CASE           =      torch.from_numpy(SCREAMING_SNAKE_CASE_     ).permute(3,	2,	0,	1     )
										elif "embeddings" in key:
															SCREAMING_SNAKE_CASE           =      torch.from_numpy(SCREAMING_SNAKE_CASE_     )
										elif "depthwise_kernel" in key:
															SCREAMING_SNAKE_CASE           =      torch.from_numpy(SCREAMING_SNAKE_CASE_     ).permute(2,	3,	0,	1     )
										elif "kernel" in key:
															SCREAMING_SNAKE_CASE           =      torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE_     )     )
										elif "temperature" in key:
															SCREAMING_SNAKE_CASE           =      value
										elif "bn/gamma" or "bn/beta" in key:
															SCREAMING_SNAKE_CASE           =      torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE_     )     ).squeeze()
										else:
															SCREAMING_SNAKE_CASE           =      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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      6_4
					SCREAMING_SNAKE_CASE           =      Tokenizer(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      align.Align('efficientnet-b7',	'bert-base',	6_4_0,	SCREAMING_SNAKE_CASE_,	tok.get_vocab_size()     )
					original_model.compile()
					original_model.load_weights(SCREAMING_SNAKE_CASE_     )

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

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

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

					# Initialize processor
					SCREAMING_SNAKE_CASE           =      get_processor()
					SCREAMING_SNAKE_CASE           =      processor(
					    images=prepare_img(),	text='A picture of a cat',	padding='max_length',	max_length=6_4,	return_tensors='pt'     )

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

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

					# Original model inference
					SCREAMING_SNAKE_CASE           =      False
					SCREAMING_SNAKE_CASE           =      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,	)
					SCREAMING_SNAKE_CASE           =      tf_image_processor(images=prepare_img(),	return_tensors='tf',	data_format='channels_last'     )['pixel_values']
					SCREAMING_SNAKE_CASE           =      tok(tf.constant(['A picture of a cat']     )     )

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

					SCREAMING_SNAKE_CASE           =      tf.nn.la_normalize(SCREAMING_SNAKE_CASE_,	axis=-1     )
					SCREAMING_SNAKE_CASE           =      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__":
	snake_case					  =						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')

	snake_case					  =						parser.parse_args()
	convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


'''simple docstring'''





def 							snake_case				(		UpperCAmelCase						)->      list:







      """simple docstring"""
      __A													=      1.3
      __A													=      len(UpperCAmelCase						)
      __A													=      False

      while not completed:
            # Update the gap value for a next comb
            __A													=      int(gap / shrink_factor						)
            if gap <= 1:
                  __A													=      True

            __A													=      0
            while index + gap < len(UpperCAmelCase						):
                  if data[index] > data[index + gap]:
                        # Swap values
                        __A						,   __A													=      data[index + gap], data[index]
                        __A													=      False
                  index += 1

      return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a__		: Optional[Any]  							=							input("Enter numbers separated by a comma:\n").strip()
 a__		: Union[str, Any]  							=							[int(item) for item in user_input.split(",")]
 print(comb_sort(unsorted))






