





'''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()
A__       :     List[str]          =logging.get_logger(__name__)
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							tf.image.resize(lowerCAmelCase							,			(3_46, 3_46)						)
      _lowerCAmelCase							  =							tf.image.crop_to_bounding_box(lowerCAmelCase							,			(3_46 - 2_89) // 2							,			(3_46 - 2_89) // 2							,			2_89							,			2_89						)
      return image
def      UpperCamelCase__   (	):





      """simple docstring"""


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

      _lowerCAmelCase							  =							BertConfig()
      _lowerCAmelCase							  =							AlignConfig.from_text_vision_configs(
          text_config=lowerCAmelCase							,			vision_config=lowerCAmelCase							,			projection_dim=6_40						)
      return config
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase							  =							Image.open(requests.get(lowerCAmelCase							,			stream=lowerCAmelCase						).raw						)
      return im
def      UpperCamelCase__   (	):





      """simple docstring"""


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





      """simple docstring"""


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

      _lowerCAmelCase							  =							[]
      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:
            _lowerCAmelCase							  =							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")						)

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

    # BERT text encoder
      _lowerCAmelCase							  =							[]
      _lowerCAmelCase							  =							"""tf_bert_model/bert"""
      _lowerCAmelCase							  =							"""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:
                  _lowerCAmelCase							  =							item[1]
      return key_mapping
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      list(hf_params.keys()						)

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

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

            # Replace HF parameters with original TF model parameters
            hf_params[hf_key].copy_(lowerCAmelCase						)
@torch.no_grad()
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							64
      _lowerCAmelCase							  =							Tokenizer(lowerCAmelCase						)
      _lowerCAmelCase							  =							align.Align("""efficientnet-b7"""							,			"""bert-base"""							,			6_40							,			lowerCAmelCase							,			tok.get_vocab_size()						)
      original_model.compile()
      original_model.load_weights(lowerCAmelCase						)

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

      # Load HuggingFace model
      _lowerCAmelCase							  =							get_align_config()
      _lowerCAmelCase							  =							AlignModel(lowerCAmelCase						).eval()
      _lowerCAmelCase							  =							hf_model.state_dict()

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

      # Initialize processor
      _lowerCAmelCase							  =							get_processor()
      _lowerCAmelCase							  =							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():
            _lowerCAmelCase							  =							hf_model(**lowerCAmelCase						)

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

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

      _lowerCAmelCase							  =							original_model.image_encoder(lowerCAmelCase							,			training=lowerCAmelCase						)
      _lowerCAmelCase							  =							original_model.text_encoder(lowerCAmelCase							,			training=lowerCAmelCase						)

      _lowerCAmelCase							  =							tf.nn.la_normalize(lowerCAmelCase							,			axis=-1						)
      _lowerCAmelCase							  =							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__       :     Tuple          =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__       :     Optional[Any]          =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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






