



'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase		      =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase		      =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







 def __init__(				self    ,a_=None    ,a_=None    ,**a_				)						->  Tuple:
        _UpperCAmelCase    :		List[Any]     	=					None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""    ,a_    ,)
               _UpperCAmelCase    :		Tuple     	=					kwargs.pop("""feature_extractor"""				)

        _UpperCAmelCase    :		Any     	=					image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""				)
        if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""				)

        super().__init__(a_    ,a_				)







 def __call__(				self    ,a_    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = True    ,a_ = False    ,a_ = False    ,a_ = None    ,a_ = 0    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = False    ,a_ = False    ,a_ = False    ,a_ = False    ,a_ = True    ,a_ = None    ,**a_    ,)						->  BatchEncoding:
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""				)

        if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

        if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""				)

        # first, apply the image processor
        _UpperCAmelCase    :		Tuple     	=					self.image_processor(images=a_    ,return_tensors=a_				)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(a_    ,a_				):
                      _UpperCAmelCase    :		Union[str, Any]     	=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
               _UpperCAmelCase    :		List[str]     	=					features["""words"""]

        _UpperCAmelCase    :		int     	=					self.tokenizer(
            text=text if text is not None else features["""words"""]    ,text_pair=text_pair if text_pair is not None else None    ,boxes=boxes if boxes is not None else features["""boxes"""]    ,word_labels=a_    ,add_special_tokens=a_    ,padding=a_    ,truncation=a_    ,max_length=a_    ,stride=a_    ,pad_to_multiple_of=a_    ,return_token_type_ids=a_    ,return_attention_mask=a_    ,return_overflowing_tokens=a_    ,return_special_tokens_mask=a_    ,return_offsets_mapping=a_    ,return_length=a_    ,verbose=a_    ,return_tensors=a_    ,**a_    ,)

        # add pixel values
        _UpperCAmelCase    :		int     	=					features.pop("""pixel_values"""				)
        if return_overflowing_tokens is True:
               _UpperCAmelCase    :		Any     	=					self.get_overflowing_images(a_    ,encoded_inputs["""overflow_to_sample_mapping"""]				)
        _UpperCAmelCase    :		List[str]     	=					images

        return encoded_inputs







 def _snake_case					(				self    ,a_    ,a_				)						->  str:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        _UpperCAmelCase    :		Optional[int]     	=					[]
        for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]				)

        if len(a_				) != len(a_				):
               raise ValueError(
                   """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                   f''' {len(a_				)} and {len(a_				)}'''				)

        return images_with_overflow







 def _snake_case					(				self    ,*a_    ,**a_				)						->  Union[str, Any]:
        return self.tokenizer.batch_decode(*a_    ,**a_				)







 def _snake_case					(				self    ,*a_    ,**a_				)						->  Dict:
        return self.tokenizer.decode(*a_    ,**a_				)







 @property
 def _snake_case					(				self				)						->  Optional[int]:
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







 @property
 def _snake_case					(				self				)						->  Union[str, Any]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,a_    ,)
        return self.image_processor_class







 @property
 def _snake_case					(				self				)						->  List[str]:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,a_    ,)
        return self.image_processor









"""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()
SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						str:
 A__			=							tf.image.resize(lowercase_     ,					(3_46, 3_46)		)
 A__			=							tf.image.crop_to_bounding_box(lowercase_     ,					(3_46 - 2_89) // 2     ,					(3_46 - 2_89) // 2     ,					2_89     ,					2_89		)
 return image







def     _SCREAMING_SNAKE_CASE  (				)		->						Dict:
 A__			=							EfficientNetConfig.from_pretrained("google/efficientnet-b7"		)
 A__			=							2_89
 A__			=							6_40
 A__			=							{"0": "LABEL_0", "1": "LABEL_1"}
 A__			=							{"LABEL_0": 0, "LABEL_1": 1}
 A__			=							[]

 A__			=							BertConfig()
 A__			=							AlignConfig.from_text_vision_configs(
     text_config=lowercase_     ,					vision_config=lowercase_     ,					projection_dim=6_40		)
 return config







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







def     _SCREAMING_SNAKE_CASE  (				)		->						List[str]:
 A__			=							EfficientNetImageProcessor(
     do_center_crop=lowercase_     ,					rescale_factor=1 / 1_2_7.5     ,					rescale_offset=lowercase_     ,					do_normalize=lowercase_     ,					include_top=lowercase_     ,					resample=Image.BILINEAR     ,					)
 A__			=							BertTokenizer.from_pretrained("bert-base-uncased"		)
 A__			=							64
 A__			=							AlignProcessor(image_processor=lowercase_     ,					tokenizer=lowercase_		)
 return processor







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						int:
 # EfficientNet image encoder
 A__			=							[v.split("_"		)[0].split("block"		)[1] for v in original_param_names if v.startswith("block"		)]
 A__			=							list(set(lowercase_		)		)
 A__			=							sorted(lowercase_		)
 A__			=							len(lowercase_		)
 A__			=							{b: str(lowercase_		) for b, i in zip(lowercase_     ,					range(lowercase_		)		)}

 A__			=							[]
 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:
  A__			=							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""")		)

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

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







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Optional[Any]:
 list(hf_params.keys()		)

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

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

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







@torch.no_grad()
def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						int:
 A__			=							64
 A__			=							Tokenizer(lowercase_		)
 A__			=							align.Align("efficientnet-b7"     ,					"bert-base"     ,					6_40     ,					lowercase_     ,					tok.get_vocab_size()		)
 original_model.compile()
 original_model.load_weights(lowercase_		)

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

 # Load HuggingFace model
 A__			=							get_align_config()
 A__			=							AlignModel(lowercase_		).eval()
 A__			=							hf_model.state_dict()

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

 # Initialize processor
 A__			=							get_processor()
 A__			=							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():
  A__			=							hf_model(**lowercase_		)

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

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

 A__			=							original_model.image_encoder(lowercase_     ,					training=lowercase_		)
 A__			=							original_model.text_encoder(lowercase_     ,					training=lowercase_		)

 A__			=							tf.nn.la_normalize(lowercase_     ,					axis=-1		)
 A__			=							tf.nn.la_normalize(lowercase_     ,					axis=-1		)

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

 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__":
      SCREAMING_SNAKE_CASE							=			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")

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





