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





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				tf.image.resize(lowerCamelCase,     (346, 346)							)
 lowerCamelCase		:     int          =				tf.image.crop_to_bounding_box(lowerCamelCase,     (346 - 289) // 2,     (346 - 289) // 2,     289,     289							)
 return image





def    _a (						):
 lowerCamelCase		:     List[Any]          =				EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""							)
 lowerCamelCase		:     Optional[int]          =				289
 lowerCamelCase		:     int          =				640
 lowerCamelCase		:     List[str]          =				{"""0""": """LABEL_0""", """1""": """LABEL_1"""}
 lowerCamelCase		:     List[Any]          =				{"""LABEL_0""": 0, """LABEL_1""": 1}
 lowerCamelCase		:     Any          =				[]

 lowerCamelCase		:     Any          =				BertConfig()
 lowerCamelCase		:     List[str]          =				AlignConfig.from_text_vision_configs(
     text_config=lowerCamelCase,     vision_config=lowerCamelCase,     projection_dim=640							)
 return config





def    _a (						):
 lowerCamelCase		:     Optional[Any]          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowerCamelCase		:     Optional[int]          =				Image.open(requests.get(lowerCamelCase,     stream=lowerCamelCase							).raw							)
 return im





def    _a (						):
 lowerCamelCase		:     Any          =				EfficientNetImageProcessor(
     do_center_crop=lowerCamelCase,     rescale_factor=1 / 1_2_7.5,     rescale_offset=lowerCamelCase,     do_normalize=lowerCamelCase,     include_top=lowerCamelCase,     resample=Image.BILINEAR,     )
 lowerCamelCase		:     Dict          =				BertTokenizer.from_pretrained("""bert-base-uncased"""							)
 lowerCamelCase		:     Any          =				64
 lowerCamelCase		:     Optional[int]          =				AlignProcessor(image_processor=lowerCamelCase,     tokenizer=lowerCamelCase							)
 return processor





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Union[str, Any]          =				[v.split("""_"""							)[0].split("""block"""							)[1] for v in original_param_names if v.startswith("""block"""							)]
 lowerCamelCase		:     str          =				list(set(lowerCamelCase							)							)
 lowerCamelCase		:     Tuple          =				sorted(lowerCamelCase							)
 lowerCamelCase		:     Any          =				len(lowerCamelCase							)
 lowerCamelCase		:     Tuple          =				{b: str(lowerCamelCase							) for b, i in zip(lowerCamelCase,     range(lowerCamelCase							)							)}

 lowerCamelCase		:     List[str]          =				[]
 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		:     int          =				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		:     List[Any]          =				{}
 for item in rename_keys:
  if item[0] in original_param_names:
   lowerCamelCase		:     Tuple          =				"""vision_model.""" + item[1]

    # BERT text encoder
 lowerCamelCase		:     Union[str, Any]          =				[]
 lowerCamelCase		:     Optional[int]          =				"""tf_bert_model/bert"""
 lowerCamelCase		:     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:
   lowerCamelCase		:     Tuple          =				item[1]
 return key_mapping





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 list(hf_params.keys()							)

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

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

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





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     str          =				64
 lowerCamelCase		:     Optional[int]          =				Tokenizer(lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				align.Align("""efficientnet-b7""",     """bert-base""",     640,     lowerCamelCase,     tok.get_vocab_size()							)
 original_model.compile()
 original_model.load_weights(lowerCamelCase							)

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

 # Load HuggingFace model
 lowerCamelCase		:     Optional[int]          =				get_align_config()
 lowerCamelCase		:     Union[str, Any]          =				AlignModel(lowerCamelCase							).eval()
 lowerCamelCase		:     Union[str, Any]          =				hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print("""Converting parameters..."""							)
 lowerCamelCase		:     Optional[int]          =				rename_keys(lowerCamelCase							)
 replace_params(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							)

 # Initialize processor
 lowerCamelCase		:     Optional[Any]          =				get_processor()
 lowerCamelCase		:     Optional[Any]          =				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		:     Any          =				hf_model(**lowerCamelCase							)

 lowerCamelCase		:     Dict          =				outputs.image_embeds.detach().numpy()
 lowerCamelCase		:     int          =				outputs.text_embeds.detach().numpy()

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

 lowerCamelCase		:     List[Any]          =				original_model.image_encoder(lowerCamelCase,     training=lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				original_model.text_encoder(lowerCamelCase,     training=lowerCamelCase							)

 lowerCamelCase		:     Dict          =				tf.nn.la_normalize(lowerCamelCase,     axis=-1							)
 lowerCamelCase		:     Dict          =				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__":
      _lowerCamelCase        =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""")

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






import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # Initialise PyTorch model
 lowerCamelCase		:     List[str]          =				FNetConfig.from_json_file(lowerCamelCase							)
 print(F'''Building PyTorch model from configuration: {config}'''							)
 lowerCamelCase		:     Any          =				FNetForPreTraining(lowerCamelCase							)

 lowerCamelCase		:     List[Any]          =				restore_checkpoint(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				checkpoint_dict["""target"""]

 # Embeddings
 # Position IDs
 lowerCamelCase		:     Optional[Any]          =				fnet_pretraining_model.state_dict()

 lowerCamelCase		:     str          =				state_dict["""fnet.embeddings.position_ids"""]
 lowerCamelCase		:     List[str]          =				{"""fnet.embeddings.position_ids""": position_ids}
 # Embedding Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     List[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]							).T
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]							)
 lowerCamelCase		:     str          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]							)

 # Encoder Layers
 for layer in range(config.num_hidden_layers							):
  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Any          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]							)

  lowerCamelCase		:     int          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]							).T
  lowerCamelCase		:     List[str]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]							)

  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]							).T
  lowerCamelCase		:     str          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]							)

  lowerCamelCase		:     Tuple          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Optional[int]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]							)

 # Pooler Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]							).T
 lowerCamelCase		:     List[Any]          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]							)

 # Masked LM Layers
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""kernel"""]							).T
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""bias"""]							)
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""bias"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""predictions_output"""]["""output_bias"""]							)
 lowerCamelCase		:     Any          =				torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]							)

 # Seq Relationship Layers
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_kernel"""]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_bias"""]							)

 # Load State Dict
 fnet_pretraining_model.load_state_dict(lowerCamelCase							)

 # Save PreTrained
 print(F'''Saving pretrained model to {save_path}'''							)
 fnet_pretraining_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--fnet_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained FNet model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      _lowerCamelCase        =parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




