
'''simple docstring'''
import argparse
import os
import sys
import urllib.request
import zipfile


lowercase__		    =	["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
lowercase__		    =	{
    "CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
    "SST": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8",
    "MRPC": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc",
    "QQP": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5",
    "STS": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5",
    "MNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce",
    "SNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df",
    "QNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601",
    "RTE": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb",
    "WNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf",
    "diagnostic": "https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D",
}

lowercase__		    =	"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
lowercase__		    =	"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Tuple:


		'''simple docstring'''


		print(F'Downloading and extracting {task}...'       )
		snake_case   :    List[Any]			       =		F'{task}.zip'
		urllib.request.urlretrieve(TASKaPATH[task]      ,    SCREAMING_SNAKE_CASE__       )
		with zipfile.ZipFile(SCREAMING_SNAKE_CASE__       ) as zip_ref:
				zip_ref.extractall(SCREAMING_SNAKE_CASE__       )
		os.remove(SCREAMING_SNAKE_CASE__       )
		print('''\tCompleted!'''       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


		print('''Processing MRPC...'''       )
		snake_case   :    str			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''MRPC'''       )
		if not os.path.isdir(SCREAMING_SNAKE_CASE__       ):
				os.mkdir(SCREAMING_SNAKE_CASE__       )
		if path_to_data:
				snake_case   :    Tuple			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''msr_paraphrase_train.txt'''       )
				snake_case   :    List[str]			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''msr_paraphrase_test.txt'''       )
		else:
				print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN       )
				snake_case   :    int			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''msr_paraphrase_train.txt'''       )
				snake_case   :    Dict			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''msr_paraphrase_test.txt'''       )
				urllib.request.urlretrieve(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )
				urllib.request.urlretrieve(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )
		if not os.path.isfile(SCREAMING_SNAKE_CASE__       ):
				raise ValueError(F'Train data not found at {mrpc_train_file}'       )
		if not os.path.isfile(SCREAMING_SNAKE_CASE__       ):
				raise ValueError(F'Test data not found at {mrpc_test_file}'       )
		urllib.request.urlretrieve(TASKaPATH['''MRPC''']      ,    os.path.join(SCREAMING_SNAKE_CASE__      ,    '''dev_ids.tsv'''       )       )

		snake_case   :    int			       =		[]
		with open(os.path.join(SCREAMING_SNAKE_CASE__      ,    '''dev_ids.tsv'''       )      ,    encoding='''utf8'''       ) as ids_fh:
				for row in ids_fh:
						dev_ids.append(row.strip().split('''\t'''       )       )

		with open(SCREAMING_SNAKE_CASE__      ,    encoding='''utf8'''       ) as data_fh, open(
		    os.path.join(SCREAMING_SNAKE_CASE__      ,    '''train.tsv'''       )      ,    '''w'''      ,    encoding='''utf8'''       ) as train_fh, open(os.path.join(SCREAMING_SNAKE_CASE__      ,    '''dev.tsv'''       )      ,    '''w'''      ,    encoding='''utf8'''       ) as dev_fh:
				snake_case   :    Optional[int]			       =		data_fh.readline()
				train_fh.write(SCREAMING_SNAKE_CASE__       )
				dev_fh.write(SCREAMING_SNAKE_CASE__       )
				for row in data_fh:
						snake_case       ,snake_case       ,snake_case       ,snake_case       ,snake_case   :    List[Any]			       =		row.strip().split('''\t'''       )
						if [ida, ida] in dev_ids:
								dev_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)       )
						else:
								train_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)       )

		with open(SCREAMING_SNAKE_CASE__      ,    encoding='''utf8'''       ) as data_fh, open(
		    os.path.join(SCREAMING_SNAKE_CASE__      ,    '''test.tsv'''       )      ,    '''w'''      ,    encoding='''utf8'''       ) as test_fh:
				snake_case   :    List[str]			       =		data_fh.readline()
				test_fh.write('''index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'''       )
				for idx, row in enumerate(SCREAMING_SNAKE_CASE__       ):
						snake_case       ,snake_case       ,snake_case       ,snake_case       ,snake_case   :    Dict			       =		row.strip().split('''\t'''       )
						test_fh.write('''%d\t%s\t%s\t%s\t%s\n''' % (idx, ida, ida, sa, sa)       )
		print('''\tCompleted!'''       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Optional[Any]:


		'''simple docstring'''


		print('''Downloading and extracting diagnostic...'''       )
		if not os.path.isdir(os.path.join(SCREAMING_SNAKE_CASE__      ,    '''diagnostic'''       )       ):
				os.mkdir(os.path.join(SCREAMING_SNAKE_CASE__      ,    '''diagnostic'''       )       )
		snake_case   :    Any			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''diagnostic'''      ,    '''diagnostic.tsv'''       )
		urllib.request.urlretrieve(TASKaPATH['''diagnostic''']      ,    SCREAMING_SNAKE_CASE__       )
		print('''\tCompleted!'''       )
		return




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[str]:


		'''simple docstring'''


		snake_case   :    int			       =		task_names.split(''','''       )
		if "all" in task_names:
				snake_case   :    int			       =		TASKS
		else:
				snake_case   :    List[str]			       =		[]
				for task_name in task_names:
						if task_name not in TASKS:
								raise ValueError(F'Task {task_name} not found!'       )
						tasks.append(SCREAMING_SNAKE_CASE__       )
		return tasks




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    List[Any]			       =		argparse.ArgumentParser()
		parser.add_argument('''--data_dir'''      ,    help='''directory to save data to'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''glue_data'''       )
		parser.add_argument(
		    '''--tasks'''      ,    help='''tasks to download data for as a comma separated string'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''all'''       )
		parser.add_argument(
		    '''--path_to_mrpc'''      ,    help='''path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default=''''''      ,    )
		snake_case   :    List[Any]			       =		parser.parse_args(SCREAMING_SNAKE_CASE__       )

		if not os.path.isdir(args.data_dir       ):
				os.mkdir(args.data_dir       )
		snake_case   :    Tuple			       =		get_tasks(args.tasks       )

		for task in tasks:
				if task == "MRPC":
						format_mrpc(args.data_dir      ,    args.path_to_mrpc       )
				elif task == "diagnostic":
						download_diagnostic(args.data_dir       )
				else:
						download_and_extract(SCREAMING_SNAKE_CASE__      ,    args.data_dir       )


if __name__ == "__main__":
						sys.exit(main(sys.argv[1:]))








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_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= FNetConfig.from_json_file(lowerCAmelCase_			)
     print(F'Building PyTorch model from configuration: {config}'			)
     lowerCAmelCase__		= FNetForPreTraining(lowerCAmelCase_			)

     lowerCAmelCase__		= restore_checkpoint(lowerCAmelCase_   ,				lowerCAmelCase_			)
     lowerCAmelCase__		= checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     lowerCAmelCase__		= fnet_pretraining_model.state_dict()

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

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

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["intermediate"]["kernel"]			).T
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["intermediate"]["bias"]			)

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["output"]["kernel"]			).T
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["output"]["bias"]			)

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'encoder_{layer}']["output_layer_norm"]["scale"]			)
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'encoder_{layer}']["output_layer_norm"]["bias"]			)

     # Pooler Layers
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]			)

     # Masked LM Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]			)
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]			)

     # Seq Relationship Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]			)
     lowerCAmelCase__		= 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__":
     UpperCamelCase								=     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.')
     UpperCamelCase								=     parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
