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


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				List[Any]       						= tf.image.resize(snake_case	,     (3_46, 3_46)   )
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= tf.image.crop_to_bounding_box(snake_case	,     (3_46 - 2_89) // 2	,     (3_46 - 2_89) // 2	,     2_89	,     2_89   )
   return image


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''   )
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= 2_89
   SCREAMING_SNAKE_CASE__			:				Dict       						= 6_40
   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
   SCREAMING_SNAKE_CASE__			:				Any       						= {'''LABEL_0''': 0, '''LABEL_1''': 1}
   SCREAMING_SNAKE_CASE__			:				int       						= []

   SCREAMING_SNAKE_CASE__			:				str       						= BertConfig()
   SCREAMING_SNAKE_CASE__			:				str       						= AlignConfig.from_text_vision_configs(
       text_config=snake_case	,     vision_config=snake_case	,     projection_dim=6_40   )
   return config


def 				UpperCamelCase	(  ):




   '''simple docstring'''

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


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= EfficientNetImageProcessor(
       do_center_crop=snake_case	,     rescale_factor=1 / 127.5	,     rescale_offset=snake_case	,     do_normalize=snake_case	,     include_top=snake_case	,     resample=Image.BILINEAR	,     )
   SCREAMING_SNAKE_CASE__			:				str       						= BertTokenizer.from_pretrained('''bert-base-uncased'''   )
   SCREAMING_SNAKE_CASE__			:				Dict       						= 64
   SCREAMING_SNAKE_CASE__			:				str       						= AlignProcessor(image_processor=snake_case	,     tokenizer=snake_case   )
   return processor


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

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

   SCREAMING_SNAKE_CASE__			:				Union[str, 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:
      SCREAMING_SNAKE_CASE__			:				Tuple       						= 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__			:				Tuple       						= {}
   for item in rename_keys:
      if item[0] in original_param_names:
         SCREAMING_SNAKE_CASE__			:				Any       						= '''vision_model.''' + item[1]

    # BERT text encoder
   SCREAMING_SNAKE_CASE__			:				int       						= []
   SCREAMING_SNAKE_CASE__			:				str       						= '''tf_bert_model/bert'''
   SCREAMING_SNAKE_CASE__			:				Dict       						= '''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__			:				Tuple       						= item[1]
   return key_mapping


def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   list(hf_params.keys()   )

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

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

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


@torch.no_grad()
def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				List[str]       						= 64
   SCREAMING_SNAKE_CASE__			:				int       						= Tokenizer(snake_case   )
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= align.Align('''efficientnet-b7'''	,     '''bert-base'''	,     6_40	,     snake_case	,     tok.get_vocab_size()   )
   original_model.compile()
   original_model.load_weights(snake_case   )

   SCREAMING_SNAKE_CASE__			:				Dict       						= original_model.trainable_variables
   SCREAMING_SNAKE_CASE__			:				Dict       						= original_model.non_trainable_variables
   SCREAMING_SNAKE_CASE__			:				str       						= {param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      SCREAMING_SNAKE_CASE__			:				Tuple       						= param.numpy()
   SCREAMING_SNAKE_CASE__			:				Dict       						= list(tf_params.keys()   )

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

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

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

   SCREAMING_SNAKE_CASE__			:				str       						= outputs.image_embeds.detach().numpy()
   SCREAMING_SNAKE_CASE__			:				Tuple       						= outputs.text_embeds.detach().numpy()

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

   SCREAMING_SNAKE_CASE__			:				int       						= original_model.image_encoder(snake_case	,     training=snake_case   )
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= original_model.text_encoder(snake_case	,     training=snake_case   )

   SCREAMING_SNAKE_CASE__			:				Tuple       						= tf.nn.la_normalize(snake_case	,     axis=-1   )
   SCREAMING_SNAKE_CASE__			:				Any       						= tf.nn.la_normalize(snake_case	,     axis=-1   )

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

   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__":
       lowercase__			=	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''')

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







'''simple docstring'''





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__			=	{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase__			=	[
           '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ConvNextV2ForImageClassification''',
           '''ConvNextV2Model''',
           '''ConvNextV2PreTrainedModel''',
           '''ConvNextV2Backbone''',
       ]


if TYPE_CHECKING:
       from .configuration_convnextva import (
           CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           ConvNextVaConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnextva import (
                  CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextVaBackbone,
                  ConvNextVaForImageClassification,
                  ConvNextVaModel,
                  ConvNextVaPreTrainedModel,
              )

else:
       import sys

       lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)

