




'''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_		:    Optional[Any]									=		logging.get_logger(__name__)





def 			__a			(     _UpperCamelCase:					int					)		->				int:

     """simple docstring"""




     _snake_case								=   tf.image.resize(_UpperCamelCase      ,		(346, 346)					)
     _snake_case								=   tf.image.crop_to_bounding_box(_UpperCamelCase      ,		(346 - 289) // 2      ,		(346 - 289) // 2      ,		289      ,		289					)
     return image





def 			__a			(     )		->				Any:

     """simple docstring"""




     _snake_case								=   EfficientNetConfig.from_pretrained("google/efficientnet-b7"					)
     _snake_case								=   289
     _snake_case								=   640
     _snake_case								=   {"0": "LABEL_0", "1": "LABEL_1"}
     _snake_case								=   {"LABEL_0": 0, "LABEL_1": 1}
     _snake_case								=   []

     _snake_case								=   BertConfig()
     _snake_case								=   AlignConfig.from_text_vision_configs(
         text_config=_UpperCamelCase      ,		vision_config=_UpperCamelCase      ,		projection_dim=640					)
     return config





def 			__a			(     )		->				List[str]:

     """simple docstring"""




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





def 			__a			(     )		->				Optional[int]:

     """simple docstring"""




     _snake_case								=   EfficientNetImageProcessor(
         do_center_crop=_UpperCamelCase      ,		rescale_factor=1 / 127.5      ,		rescale_offset=_UpperCamelCase      ,		do_normalize=_UpperCamelCase      ,		include_top=_UpperCamelCase      ,		resample=Image.BILINEAR      ,		)
     _snake_case								=   BertTokenizer.from_pretrained("bert-base-uncased"					)
     _snake_case								=   64
     _snake_case								=   AlignProcessor(image_processor=_UpperCamelCase      ,		tokenizer=_UpperCamelCase					)
     return processor





def 			__a			(     _UpperCamelCase:					Dict					)		->				Optional[Any]:

     """simple docstring"""




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

     _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:
          _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""")					)

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

    # BERT text encoder
     _snake_case								=   []
     _snake_case								=   "tf_bert_model/bert"
     _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:
               _snake_case								=   item[1]
     return key_mapping





def 			__a			(     _UpperCamelCase:					Optional[int]      ,		_UpperCamelCase:					List[Any]      ,		_UpperCamelCase:					Dict					)		->				Union[str, Any]:

     """simple docstring"""




     list(hf_params.keys()					)

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

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

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





@torch.no_grad()
def 			__a			(     _UpperCamelCase:					Any      ,		_UpperCamelCase:					Any      ,		_UpperCamelCase:					Dict      ,		_UpperCamelCase:					Union[str, Any]					)		->				Optional[int]:

     """simple docstring"""




     _snake_case								=   64
     _snake_case								=   Tokenizer(_UpperCamelCase					)
     _snake_case								=   align.Align("efficientnet-b7"      ,		"bert-base"      ,		640      ,		_UpperCamelCase      ,		tok.get_vocab_size()					)
     original_model.compile()
     original_model.load_weights(_UpperCamelCase					)

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

     # Load HuggingFace model
     _snake_case								=   get_align_config()
     _snake_case								=   AlignModel(_UpperCamelCase					).eval()
     _snake_case								=   hf_model.state_dict()

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

     # Initialize processor
     _snake_case								=   get_processor()
     _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():
          _snake_case								=   hf_model(**_UpperCamelCase					)

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

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

     _snake_case								=   original_model.image_encoder(_UpperCamelCase      ,		training=_UpperCamelCase					)
     _snake_case								=   original_model.text_encoder(_UpperCamelCase      ,		training=_UpperCamelCase					)

     _snake_case								=   tf.nn.la_normalize(_UpperCamelCase      ,		axis=-1					)
     _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__":
   UpperCamelCase_		:    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''')

   UpperCamelCase_		:    Tuple									=		parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








'''simple docstring'''





from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


UpperCamelCase_		:    Optional[int]									=		logging.get_logger(__name__)

UpperCamelCase_		:    str									=		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_		:    List[str]									=		{
    '''vocab_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''openai/clip-vit-base-patch32''': (
            '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_		:    List[Any]									=		{
    '''openai/clip-vit-base-patch32''': 77,
}

class      _a       (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					Any					       =		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:					Dict					       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:					int					       =		["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		CLIPTokenizer






     def __init__(				self      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,_SCREAMING_SNAKE_CASE="<|startoftext|>"      ,_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,**_SCREAMING_SNAKE_CASE      ,)							->    List[Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,tokenizer_file=_SCREAMING_SNAKE_CASE      ,unk_token=_SCREAMING_SNAKE_CASE      ,bos_token=_SCREAMING_SNAKE_CASE      ,eos_token=_SCREAMING_SNAKE_CASE      ,pad_token=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE      ,)

          if not isinstance(self.backend_tokenizer.pre_tokenizer      ,pre_tokenizers.Sequence					):
               raise ValueError(
                   "The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"
                   " heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"
                   " to be compatible with this version.The easiest way to do so is"
                   " `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"
                   " to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"
                   " transformers."					)

          self._wrap_decode_method_backend_tokenizer()






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   self.backend_tokenizer.decode

          def new_decode_method(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					):
               _snake_case							=   orig_decode_method(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)
               _snake_case							=   text.replace(self.backend_tokenizer.model.end_of_word_suffix      ," "					).strip()
               return text

          _snake_case							=   new_decode_method






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    List[int]:
          _snake_case							=   [self.bos_token_id]
          _snake_case							=   [self.eos_token_id]

          if token_ids_a is None:
               return bos_token + token_ids_a + eos_token
          return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    List[int]:
          _snake_case							=   [self.bos_token_id]
          _snake_case							=   [self.eos_token_id]

          if token_ids_a is None:
               return len(bos_token + token_ids_a + eos_token					) * [0]
          return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token					) * [0]






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    Tuple[str]:
          _snake_case							=   self._tokenizer.model.save(_SCREAMING_SNAKE_CASE      ,name=_SCREAMING_SNAKE_CASE					)
          return tuple(_SCREAMING_SNAKE_CASE					)


