



'''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_				:					str							=      logging.get_logger(__name__)




def        snake_case_   (	lowerCAmelCase_						)->						Tuple:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					tf.image.resize(lowerCAmelCase_			,  (346, 346)						)
       _UpperCAmelCase    :		str     	=					tf.image.crop_to_bounding_box(lowerCAmelCase_			,  (346 - 289) // 2			,  (346 - 289) // 2			,  289			,  289						)
       return image




def        snake_case_   (	)->						Tuple:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[int]     	=					EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""						)
       _UpperCAmelCase    :		int     	=					289
       _UpperCAmelCase    :		Any     	=					640
       _UpperCAmelCase    :		Any     	=					{"""0""": """LABEL_0""", """1""": """LABEL_1"""}
       _UpperCAmelCase    :		List[str]     	=					{"""LABEL_0""": 0, """LABEL_1""": 1}
       _UpperCAmelCase    :		Optional[Any]     	=					[]

       _UpperCAmelCase    :		int     	=					BertConfig()
       _UpperCAmelCase    :		Union[str, Any]     	=					AlignConfig.from_text_vision_configs(
           text_config=lowerCAmelCase_			,  vision_config=lowerCAmelCase_			,  projection_dim=640						)
       return config




def        snake_case_   (	)->						int:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase    :		int     	=					Image.open(requests.get(lowerCAmelCase_			,  stream=lowerCAmelCase_						).raw						)
       return im




def        snake_case_   (	)->						Dict:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[Any]     	=					EfficientNetImageProcessor(
           do_center_crop=lowerCAmelCase_			,  rescale_factor=1 / 1_2_7.5			,  rescale_offset=lowerCAmelCase_			,  do_normalize=lowerCAmelCase_			,  include_top=lowerCAmelCase_			,  resample=Image.BILINEAR			,  )
       _UpperCAmelCase    :		Union[str, Any]     	=					BertTokenizer.from_pretrained("""bert-base-uncased"""						)
       _UpperCAmelCase    :		Union[str, Any]     	=					64
       _UpperCAmelCase    :		int     	=					AlignProcessor(image_processor=lowerCAmelCase_			,  tokenizer=lowerCAmelCase_						)
       return processor




def        snake_case_   (	lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		Union[str, Any]     	=					[v.split("""_"""						)[0].split("""block"""						)[1] for v in original_param_names if v.startswith("""block"""						)]
       _UpperCAmelCase    :		Optional[Any]     	=					list(set(lowerCAmelCase_						)						)
       _UpperCAmelCase    :		List[str]     	=					sorted(lowerCAmelCase_						)
       _UpperCAmelCase    :		Optional[Any]     	=					len(lowerCAmelCase_						)
       _UpperCAmelCase    :		Tuple     	=					{b: str(lowerCAmelCase_						) for b, i in zip(lowerCAmelCase_			,  range(lowerCAmelCase_						)						)}

       _UpperCAmelCase    :		List[Any]     	=					[]
       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    :		List[Any]     	=					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    :		List[str]     	=					{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     _UpperCAmelCase    :		Any     	=					"""vision_model.""" + item[1]

    # BERT text encoder
       _UpperCAmelCase    :		Union[str, Any]     	=					[]
       _UpperCAmelCase    :		Tuple     	=					"""tf_bert_model/bert"""
       _UpperCAmelCase    :		List[Any]     	=					"""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    :		Optional[Any]     	=					item[1]
       return key_mapping




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       list(hf_params.keys()						)

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

              _UpperCAmelCase    :		Optional[int]     	=					key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     _UpperCAmelCase    :		Optional[int]     	=					torch.from_numpy(lowerCAmelCase_						).permute(3			,  2			,  0			,  1						)
              elif "embeddings" in key:
                     _UpperCAmelCase    :		Dict     	=					torch.from_numpy(lowerCAmelCase_						)
              elif "depthwise_kernel" in key:
                     _UpperCAmelCase    :		str     	=					torch.from_numpy(lowerCAmelCase_						).permute(2			,  3			,  0			,  1						)
              elif "kernel" in key:
                     _UpperCAmelCase    :		str     	=					torch.from_numpy(np.transpose(lowerCAmelCase_						)						)
              elif "temperature" in key:
                     _UpperCAmelCase    :		Union[str, Any]     	=					value
              elif "bn/gamma" or "bn/beta" in key:
                     _UpperCAmelCase    :		Optional[Any]     	=					torch.from_numpy(np.transpose(lowerCAmelCase_						)						).squeeze()
              else:
                     _UpperCAmelCase    :		int     	=					torch.from_numpy(lowerCAmelCase_						)

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




@torch.no_grad()
def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[int]     	=					64
       _UpperCAmelCase    :		Optional[int]     	=					Tokenizer(lowerCAmelCase_						)
       _UpperCAmelCase    :		List[Any]     	=					align.Align("""efficientnet-b7"""			,  """bert-base"""			,  640			,  lowerCAmelCase_			,  tok.get_vocab_size()						)
       original_model.compile()
       original_model.load_weights(lowerCAmelCase_						)

       _UpperCAmelCase    :		Optional[int]     	=					original_model.trainable_variables
       _UpperCAmelCase    :		int     	=					original_model.non_trainable_variables
       _UpperCAmelCase    :		Dict     	=					{param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              _UpperCAmelCase    :		Dict     	=					param.numpy()
       _UpperCAmelCase    :		Union[str, Any]     	=					list(tf_params.keys()						)

       # Load HuggingFace model
       _UpperCAmelCase    :		Any     	=					get_align_config()
       _UpperCAmelCase    :		List[str]     	=					AlignModel(lowerCAmelCase_						).eval()
       _UpperCAmelCase    :		List[str]     	=					hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("""Converting parameters..."""						)
       _UpperCAmelCase    :		List[str]     	=					rename_keys(lowerCAmelCase_						)
       replace_params(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)

       # Initialize processor
       _UpperCAmelCase    :		Union[str, Any]     	=					get_processor()
       _UpperCAmelCase    :		List[str]     	=					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    :		Union[str, Any]     	=					hf_model(**lowerCAmelCase_						)

       _UpperCAmelCase    :		List[Any]     	=					outputs.image_embeds.detach().numpy()
       _UpperCAmelCase    :		Any     	=					outputs.text_embeds.detach().numpy()

       # Original model inference
       _UpperCAmelCase    :		Dict     	=					False
       _UpperCAmelCase    :		Any     	=					EfficientNetImageProcessor(
           do_center_crop=lowerCAmelCase_			,  do_rescale=lowerCAmelCase_			,  do_normalize=lowerCAmelCase_			,  include_top=lowerCAmelCase_			,  resample=Image.BILINEAR			,  )
       _UpperCAmelCase    :		int     	=					tf_image_processor(images=prepare_img()			,  return_tensors="""tf"""			,  data_format="""channels_last"""						)["""pixel_values"""]
       _UpperCAmelCase    :		Optional[int]     	=					tok(tf.constant(["""A picture of a cat"""]						)						)

       _UpperCAmelCase    :		Union[str, Any]     	=					original_model.image_encoder(lowerCAmelCase_			,  training=lowerCAmelCase_						)
       _UpperCAmelCase    :		Tuple     	=					original_model.text_encoder(lowerCAmelCase_			,  training=lowerCAmelCase_						)

       _UpperCAmelCase    :		str     	=					tf.nn.la_normalize(lowerCAmelCase_			,  axis=-1						)
       _UpperCAmelCase    :		Any     	=					tf.nn.la_normalize(lowerCAmelCase_			,  axis=-1						)

       # Check whether original and HF model outputs match  -> np.allclose
       if not np.allclose(lowerCAmelCase_			,  lowerCAmelCase_			,  atol=1e-3						):
              raise ValueError("""The predicted image features are not the same."""						)
       if not np.allclose(lowerCAmelCase_			,  lowerCAmelCase_			,  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(lowerCAmelCase_						):
                     os.mkdir(lowerCAmelCase_						)
              # Save converted model and image processor
              hf_model.save_pretrained(lowerCAmelCase_						)
              processor.save_pretrained(lowerCAmelCase_						)

       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_				:					Union[str, Any]							=      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)









UpperCamelCase__						=		[
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]






def    snake_case   (				A		,  A		,  A		,  A     )				->				bool:




	'''simple docstring'''


	return 0 <= row < rows and 0 <= col < cols






def    snake_case   (				A		,  A		,  A		,  A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				len(A     )
	lowerCamelCase_				:						Any				  =				len(mat[0]     )
	if is_safe(A		,  A		,  A		,  A     ) and (row, col) not in seen and mat[row][col] == 1:
		seen.add((row, col)     )
		return (
		    1
		    + depth_first_search(row + 1		,  A		,  A		,  A     )
		    + depth_first_search(row - 1		,  A		,  A		,  A     )
		    + depth_first_search(A		,  col + 1		,  A		,  A     )
		    + depth_first_search(A		,  col - 1		,  A		,  A     )
		)
	else:
		return 0






def    snake_case   (				A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						set				  =				set()

	lowerCamelCase_				:						Optional[Any]				  =				0
	for row, line in enumerate(A     ):
		for col, item in enumerate(A     ):
			if item == 1 and (row, col) not in seen:
				# Maximizing the area
				lowerCamelCase_				:						Optional[Any]				  =				max(A		,  depth_first_search(A		,  A		,  A		,  A     )     )
	return max_area


if __name__ == "__main__":
							import doctest

							print(find_max_area(matrix))  # Output -> 6
							doctest.testmod()
