
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							=       logging.get_logger(__name__)




def 					lowerCamelCase__					(    a__	:	Dict  )		->						Tuple:
       UpperCamelCase_											=							tf.image.resize(lowerCAmelCase_						,   (346, 346)  )
       UpperCamelCase_											=							tf.image.crop_to_bounding_box(lowerCAmelCase_						,   (346 - 289) // 2						,   (346 - 289) // 2						,   289						,   289  )
       return image




def 					lowerCamelCase__					(    )		->						Tuple:
       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=lowerCAmelCase_						,   vision_config=lowerCAmelCase_						,   projection_dim=640  )
       return config




def 					lowerCamelCase__					(    )		->						int:
       UpperCamelCase_											=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCamelCase_											=							Image.open(requests.get(lowerCAmelCase_						,   stream=lowerCAmelCase_  ).raw  )
       return im




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




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							[v.split("""_"""  )[0].split("""block"""  )[1] for v in original_param_names if v.startswith("""block"""  )]
       UpperCamelCase_											=							list(set(lowerCAmelCase_  )  )
       UpperCamelCase_											=							sorted(lowerCAmelCase_  )
       UpperCamelCase_											=							len(lowerCAmelCase_  )
       UpperCamelCase_											=							{b: str(lowerCAmelCase_  ) for b, i in zip(lowerCAmelCase_						,   range(lowerCAmelCase_  )  )}

       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__					(    a__	:	Optional[int]						,   a__	:	List[Any]						,   a__	:	Optional[int]  )		->						Optional[int]:
       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(lowerCAmelCase_  ).permute(3						,   2						,   0						,   1  )
              elif "embeddings" in key:
                     UpperCamelCase_											=							torch.from_numpy(lowerCAmelCase_  )
              elif "depthwise_kernel" in key:
                     UpperCamelCase_											=							torch.from_numpy(lowerCAmelCase_  ).permute(2						,   3						,   0						,   1  )
              elif "kernel" in key:
                     UpperCamelCase_											=							torch.from_numpy(np.transpose(lowerCAmelCase_  )  )
              elif "temperature" in key:
                     UpperCamelCase_											=							value
              elif "bn/gamma" or "bn/beta" in key:
                     UpperCamelCase_											=							torch.from_numpy(np.transpose(lowerCAmelCase_  )  ).squeeze()
              else:
                     UpperCamelCase_											=							torch.from_numpy(lowerCAmelCase_  )

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




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[str]						,   a__	:	Dict						,   a__	:	List[Any]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							64
       UpperCamelCase_											=							Tokenizer(lowerCAmelCase_  )
       UpperCamelCase_											=							align.Align("""efficientnet-b7"""						,   """bert-base"""						,   640						,   lowerCAmelCase_						,   tok.get_vocab_size()  )
       original_model.compile()
       original_model.load_weights(lowerCAmelCase_  )

       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(lowerCAmelCase_  ).eval()
       UpperCamelCase_											=							hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("""Converting parameters..."""  )
       UpperCamelCase_											=							rename_keys(lowerCAmelCase_  )
       replace_params(lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_  )

       # 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(**lowerCAmelCase_  )

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

       # Original model inference
       UpperCamelCase_											=							False
       UpperCamelCase_											=							EfficientNetImageProcessor(
           do_center_crop=lowerCAmelCase_						,   do_rescale=lowerCAmelCase_						,   do_normalize=lowerCAmelCase_						,   include_top=lowerCAmelCase_						,   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(lowerCAmelCase_						,   training=lowerCAmelCase_  )
       UpperCamelCase_											=							original_model.text_encoder(lowerCAmelCase_						,   training=lowerCAmelCase_  )

       UpperCamelCase_											=							tf.nn.la_normalize(lowerCAmelCase_						,   axis=-1  )
       UpperCamelCase_											=							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							=       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							=       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_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


       if n < 0:
              return 0
       # table[i] represents the number of ways to get to amount i
       _UpperCAmelCase    :		Union[str, Any]     	=					[0] * (n + 1)

       # There is exactly 1 way to get to zero(You pick no coins).
       _UpperCAmelCase    :		Optional[int]     	=					1

       # Pick all coins one by one and update table[] values
       # after the index greater than or equal to the value of the
       # picked coin
       for coin_val in s:
              for j in range(lowerCAmelCase_			,  n + 1						):
                     table[j] += table[j - coin_val]

       return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



