
'''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()
UpperCAmelCase            = logging.get_logger(__name__)

def   A			(							A_				:       Union[str, Any]							):
						snake_case							:  Optional[Any]			=     tf.image.resize(A_     ,				(346, 346)							)
						snake_case							:  Optional[Any]			=     tf.image.crop_to_bounding_box(A_     ,				(346 - 289) // 2     ,				(346 - 289) // 2     ,				289     ,				289							)
						return image

def   A			(							):
						snake_case							:  Tuple			=     EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''							)
						snake_case							:  Tuple			=     289
						snake_case							:  int			=     640
						snake_case							:  Optional[Any]			=     {'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
						snake_case							:  Tuple			=     {'''LABEL_0''': 0, '''LABEL_1''': 1}
						snake_case							:  Optional[Any]			=     []

						snake_case							:  Any			=     BertConfig()
						snake_case							:  List[str]			=     AlignConfig.from_text_vision_configs(
						    text_config=A_     ,				vision_config=A_     ,				projection_dim=640							)
						return config

def   A			(							):
						snake_case							:  Tuple			=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case							:  Optional[Any]			=     Image.open(requests.get(A_     ,				stream=A_							).raw							)
						return im

def   A			(							):
						snake_case							:  List[str]			=     EfficientNetImageProcessor(
						    do_center_crop=A_     ,				rescale_factor=1 / 127.5     ,				rescale_offset=A_     ,				do_normalize=A_     ,				include_top=A_     ,				resample=Image.BILINEAR     ,				)
						snake_case							:  Tuple			=     BertTokenizer.from_pretrained('''bert-base-uncased'''							)
						snake_case							:  List[str]			=     64
						snake_case							:  List[str]			=     AlignProcessor(image_processor=A_     ,				tokenizer=A_							)
						return processor

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

						snake_case							:  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:
												snake_case							:  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""")							)

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

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

def   A			(							A_				:       Union[str, Any]     ,				A_				:       List[str]     ,				A_				:       Any							):
						list(hf_params.keys()							)

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

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

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

@torch.no_grad()
def   A			(							A_				:       Union[str, Any]     ,				A_				:       Tuple     ,				A_				:       Optional[int]     ,				A_				:       List[str]							):
						snake_case							:  Dict			=     64
						snake_case							:  int			=     Tokenizer(A_							)
						snake_case							:  Dict			=     align.Align('''efficientnet-b7'''     ,				'''bert-base'''     ,				640     ,				A_     ,				tok.get_vocab_size()							)
						original_model.compile()
						original_model.load_weights(A_							)

						snake_case							:  List[Any]			=     original_model.trainable_variables
						snake_case							:  Optional[Any]			=     original_model.non_trainable_variables
						snake_case							:  List[Any]			=     {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												snake_case							:  Any			=     param.numpy()
						snake_case							:  List[str]			=     list(tf_params.keys()							)

						# Load HuggingFace model
						snake_case							:  int			=     get_align_config()
						snake_case							:  Any			=     AlignModel(A_							).eval()
						snake_case							:  Any			=     hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print('''Converting parameters...'''							)
						snake_case							:  Tuple			=     rename_keys(A_							)
						replace_params(A_     ,				A_     ,				A_							)

						# Initialize processor
						snake_case							:  int			=     get_processor()
						snake_case							:  Optional[Any]			=     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():
												snake_case							:  Optional[int]			=     hf_model(**A_							)

						snake_case							:  List[str]			=     outputs.image_embeds.detach().numpy()
						snake_case							:  int			=     outputs.text_embeds.detach().numpy()

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

						snake_case							:  int			=     original_model.image_encoder(A_     ,				training=A_							)
						snake_case							:  Any			=     original_model.text_encoder(A_     ,				training=A_							)

						snake_case							:  Any			=     tf.nn.la_normalize(A_     ,				axis=-1							)
						snake_case							:  List[str]			=     tf.nn.la_normalize(A_     ,				axis=-1							)

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

						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__":
						UpperCAmelCase            = 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")

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




from __future__ import annotations



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  assert n > 1 and a > 0 and greatest_common_divisor(_UpperCamelCase					,			_UpperCamelCase	) == 1
  ((_snake_case),	(_snake_case),	(_snake_case))		=				extended_gcd(_UpperCamelCase					,			_UpperCamelCase	)  # Implemented below
  _snake_case		=				(b * s) % n
  return x



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  ((_snake_case),	(_snake_case))		=				extended_euclid(_UpperCamelCase					,			_UpperCamelCase	)  # Implemented below
  if b < 0:
    _snake_case		=				(b % n + n) % n
  return b



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  _snake_case		=				invert_modulo(_UpperCamelCase					,			_UpperCamelCase	)
  _snake_case		=				(b * s) % n
  return x



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      tuple[int, int, int]:



  """simple docstring"""






  assert a >= 0 and b >= 0

  if b == 0:
    _snake_case,	_snake_case,	_snake_case		=				a, 1, 0
  else:
    ((_snake_case),	(_snake_case),	(_snake_case))		=				extended_gcd(_UpperCamelCase					,			a % b	)
    _snake_case		=				q
    _snake_case		=				p - q * (a // b)

  assert a % d == 0 and b % d == 0
  assert d == a * x + b * y

  return (d, x, y)



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      tuple[int, int]:



  """simple docstring"""






  if b == 0:
    return (1, 0)
  ((_snake_case),	(_snake_case))		=				extended_euclid(_UpperCamelCase					,			a % b	)
  _snake_case		=				a // b
  return (y, x - k * y)



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  if a < b:
    _snake_case,	_snake_case		=				b, a

  while a % b != 0:
    _snake_case,	_snake_case		=				b, a % b

  return b


if __name__ == "__main__":
 from doctest import testmod

 testmod(name='''modular_division''', verbose=True)
 testmod(name='''modular_division2''', verbose=True)
 testmod(name='''invert_modulo''', verbose=True)
 testmod(name='''extended_gcd''', verbose=True)
 testmod(name='''extended_euclid''', verbose=True)
 testmod(name='''greatest_common_divisor''', verbose=True)

