





'''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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__       :     Tuple          ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Tuple          =['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[Any]          =[
          '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MPNetForMaskedLM''',
          '''MPNetForMultipleChoice''',
          '''MPNetForQuestionAnswering''',
          '''MPNetForSequenceClassification''',
          '''MPNetForTokenClassification''',
          '''MPNetLayer''',
          '''MPNetModel''',
          '''MPNetPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     str          =[
          '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFMPNetEmbeddings''',
          '''TFMPNetForMaskedLM''',
          '''TFMPNetForMultipleChoice''',
          '''TFMPNetForQuestionAnswering''',
          '''TFMPNetForSequenceClassification''',
          '''TFMPNetForTokenClassification''',
          '''TFMPNetMainLayer''',
          '''TFMPNetModel''',
          '''TFMPNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      A__       :     List[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






