

'''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()
lowerCamelCase				:   Union[str, Any]      				=  logging.get_logger(__name__)


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]       )   ->    Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=tf.image.resize(_UpperCamelCase							,				(3_46, 3_46)       )
     _SCREAMING_SNAKE_CASE												=tf.image.crop_to_bounding_box(_UpperCamelCase							,				(3_46 - 2_89) // 2							,				(3_46 - 2_89) // 2							,				2_89							,				2_89       )
     return image


def  _lowerCAmelCase      (		)   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=EfficientNetConfig.from_pretrained('google/efficientnet-b7'       )
     _SCREAMING_SNAKE_CASE												=2_89
     _SCREAMING_SNAKE_CASE												=6_40
     _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=_UpperCamelCase							,				vision_config=_UpperCamelCase							,				projection_dim=6_40       )
     return config


def  _lowerCAmelCase      (		)   ->    List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												='http://images.cocodataset.org/val2017/000000039769.jpg'
     _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_UpperCamelCase							,				stream=_UpperCamelCase       ).raw       )
     return im


def  _lowerCAmelCase      (		)   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=EfficientNetImageProcessor(
         do_center_crop=_UpperCamelCase							,				rescale_factor=1 / 1_27.5							,				rescale_offset=_UpperCamelCase							,				do_normalize=_UpperCamelCase							,				include_top=_UpperCamelCase							,				resample=Image.BILINEAR							,				)
     _SCREAMING_SNAKE_CASE												=BertTokenizer.from_pretrained('bert-base-uncased'       )
     _SCREAMING_SNAKE_CASE												=64
     _SCREAMING_SNAKE_CASE												=AlignProcessor(image_processor=_UpperCamelCase							,				tokenizer=_UpperCamelCase       )
     return processor


def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]       )   ->    Optional[int]:
     """simple docstring"""




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

     _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(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:
               _SCREAMING_SNAKE_CASE												=item[1]
     return key_mapping


def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  int							,				_UpperCamelCase		:  Any       )   ->    Dict:
     """simple docstring"""




     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(_UpperCamelCase       ).permute(3							,				2							,				0							,				1       )
          elif "embeddings" in key:
               _SCREAMING_SNAKE_CASE												=torch.from_numpy(_UpperCamelCase       )
          elif "depthwise_kernel" in key:
               _SCREAMING_SNAKE_CASE												=torch.from_numpy(_UpperCamelCase       ).permute(2							,				3							,				0							,				1       )
          elif "kernel" in key:
               _SCREAMING_SNAKE_CASE												=torch.from_numpy(np.transpose(_UpperCamelCase       )       )
          elif "temperature" in key:
               _SCREAMING_SNAKE_CASE												=value
          elif "bn/gamma" or "bn/beta" in key:
               _SCREAMING_SNAKE_CASE												=torch.from_numpy(np.transpose(_UpperCamelCase       )       ).squeeze()
          else:
               _SCREAMING_SNAKE_CASE												=torch.from_numpy(_UpperCamelCase       )

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


@torch.no_grad()
def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  int       )   ->    List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=64
     _SCREAMING_SNAKE_CASE												=Tokenizer(_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=align.Align('efficientnet-b7'							,				'bert-base'							,				6_40							,				_UpperCamelCase							,				tok.get_vocab_size()       )
     original_model.compile()
     original_model.load_weights(_UpperCamelCase       )

     _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(_UpperCamelCase       ).eval()
     _SCREAMING_SNAKE_CASE												=hf_model.state_dict()

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

     # 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=64							,				return_tensors='pt'       )

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          _SCREAMING_SNAKE_CASE												=hf_model(**_UpperCamelCase       )

     _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=_UpperCamelCase							,				do_rescale=_UpperCamelCase							,				do_normalize=_UpperCamelCase							,				include_top=_UpperCamelCase							,				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(_UpperCamelCase							,				training=_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=original_model.text_encoder(_UpperCamelCase							,				training=_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=tf.nn.la_normalize(_UpperCamelCase							,				axis=-1       )
     _SCREAMING_SNAKE_CASE												=tf.nn.la_normalize(_UpperCamelCase							,				axis=-1       )

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

     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__":
     lowerCamelCase				:   List[str]      				=  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")

     lowerCamelCase				:   str      				=  parser.parse_args()
     convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



'''simple docstring'''
lowerCamelCase				:   int      				=  {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

lowerCamelCase				:   Union[str, Any]      				=  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowerCamelCase				:   int      				=  [0 for x in range(len(graph))]
lowerCamelCase				:   Optional[Any]      				=  []


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     while stack:
          _SCREAMING_SNAKE_CASE												=stack.pop()
          print(_UpperCamelCase							,				clothes[current_clothing]       )
          order += 1


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Dict       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )

     stack.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  List[str]       )   ->    Optional[int]:
     """simple docstring"""




     for v in range(len(_UpperCamelCase       )       ):
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)
