import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


lowercase_  							=			logging.get_logger(__name__)  # pylint: disable=invalid-name


lowercase_  							=			"lfs-multipart-upload"







class      SCREAMING_SNAKE_CASE__					(  lowerCAmelCase_    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     ArgumentParser			):
           __snake_case       :				Dict 			=    parser.add_parser(
               """lfs-enable-largefiles"""			,			help=(
                   """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
               )			,			)
           enable_parser.add_argument("""path"""			,			type=UpperCamelCase_			,			help="""Local path to repository you want to configure."""			)
           enable_parser.set_defaults(func=lambda _lowerCAmelCase			: LfsEnableCommand(UpperCamelCase_			)			)

           __snake_case       :				str 			=    parser.add_parser(
               UpperCamelCase_			,			help=(
                   """Deprecated: use `huggingface-cli` instead. """
                   """Command will get called by git-lfs, do not call it directly."""
               )			,			)
           upload_parser.set_defaults(func=lambda _lowerCAmelCase			: LfsUploadCommand(UpperCamelCase_			)			)







class      SCREAMING_SNAKE_CASE__					:

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     str			):
           __snake_case       :				Any 			=    args





       def 			snake_case__    (							self					:     Optional[int]			):
           warnings.warn(
               """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""			)
           __snake_case       :				Optional[Any] 			=    os.path.abspath(self.args.path			)
           if not os.path.isdir(UpperCamelCase_			):
               print("""This does not look like a valid git repo."""			)
               exit(1			)
           subprocess.run(
               """git config lfs.customtransfer.multipart.path transformers-cli""".split()			,			check=UpperCamelCase_			,			cwd=UpperCamelCase_			)
           subprocess.run(
               f'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split()			,			check=UpperCamelCase_			,			cwd=UpperCamelCase_			,			)
           print("""Local repo set up for largefiles"""			)







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    __snake_case       :				Optional[int] 			=    json.dumps(_SCREAMING_SNAKE_CASE   ) + '''\n'''
    sys.stdout.write(_SCREAMING_SNAKE_CASE   )
    sys.stdout.flush()







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    json.loads(sys.stdin.readline().strip()   )

    if "terminate" in (msg.get("""type"""   ), msg.get("""event"""   )):
        # terminate message received
        return None

    if msg.get("""event"""   ) not in ("download", "upload"):
        logger.critical("""Received unexpected message"""   )
        sys.exit(1   )

    return msg







class      SCREAMING_SNAKE_CASE__					(  lowerCAmelCase_    ):

       def __init__(							self					:     Tuple			,			_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     int			):
           __snake_case       :				int 			=    filepath
           __snake_case       :				Optional[Any] 			=    seek_from
           __snake_case       :				Tuple 			=    read_limit
           __snake_case       :				Tuple 			=    0

       def __enter__(							self					:     int			):
           __snake_case       :				str 			=    open(self.filepath			,			"""rb"""			)
           self.f.seek(self.seek_from			)
           return self

       def __len__(							self					:     Optional[Any]			):
           __snake_case       :				Tuple 			=    os.fstat(self.f.fileno()			).st_size
           return min(self.read_limit			,			total_length - self.seek_from			)

       def 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     str=-1			):
           if self.n_seen >= self.read_limit:
               return b""
           __snake_case       :				str 			=    self.read_limit - self.n_seen
           __snake_case       :				Optional[int] 			=    self.f.read(remaining_amount if n < 0 else min(UpperCamelCase_			,			UpperCamelCase_			)			)
           self.n_seen += len(UpperCamelCase_			)
           return data

       def __iter__(							self					:     int			):
           yield self.read(n=4 * 10_24 * 10_24			)





       def __exit__(							self					:     Tuple			,			*_lowerCAmelCase					:     Tuple			):
           self.f.close()







class      SCREAMING_SNAKE_CASE__					:

       def __init__(							self					:     List[str]			,			_lowerCAmelCase					:     List[str]			):
           __snake_case       :				int 			=    args





       def 			snake_case__    (							self					:     List[Any]			):
           # Immediately after invoking a custom transfer process, git-lfs
           # sends initiation data to the process over stdin.
           # This tells the process useful information about the configuration.
           __snake_case       :				Union[str, Any] 			=    json.loads(sys.stdin.readline().strip()			)
           if not (init_msg.get("""event"""			) == "init" and init_msg.get("""operation"""			) == "upload"):
               write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}			)
               sys.exit(1			)

           # The transfer process should use the information it needs from the
           # initiation structure, and also perform any one-off setup tasks it
           # needs to do. It should then respond on stdout with a simple empty
           # confirmation structure, as follows:
           write_msg({}			)

           # After the initiation exchange, git-lfs will send any number of
           # transfer requests to the stdin of the transfer process, in a serial sequence.
           while True:
               __snake_case       :				List[Any] 			=    read_msg()
               if msg is None:
                   # When all transfers have been processed, git-lfs will send
                   # a terminate event to the stdin of the transfer process.
                   # On receiving this message the transfer process should
                   # clean up and terminate. No response is expected.
                   sys.exit(0			)

               __snake_case       :				List[Any] 			=    msg['''oid''']
               __snake_case       :				List[str] 			=    msg['''path''']
               __snake_case       :				int 			=    msg['''action''']['''href''']
               __snake_case       :				List[Any] 			=    msg['''action''']['''header''']
               __snake_case       :				Union[str, Any] 			=    int(header.pop("""chunk_size"""			)			)
               __snake_case       :				List[str] 			=    list(header.values()			)

               __snake_case       :				Dict 			=    []
               for i, presigned_url in enumerate(UpperCamelCase_			):
                   with FileSlice(UpperCamelCase_			,			seek_from=i * chunk_size			,			read_limit=UpperCamelCase_			) as data:
                       __snake_case       :				Optional[Any] 			=    requests.put(UpperCamelCase_			,			data=UpperCamelCase_			)
                       r.raise_for_status()
                       parts.append(
                           {
                               """etag""": r.headers.get("""etag"""			),
                               """partNumber""": i + 1,
                           }			)
                       # In order to support progress reporting while data is uploading / downloading,
                       # the transfer process should post messages to stdout
                       write_msg(
                           {
                               """event""": """progress""",
                               """oid""": oid,
                               """bytesSoFar""": (i + 1) * chunk_size,
                               """bytesSinceLast""": chunk_size,
                           }			)
                       # Not precise but that's ok.

               __snake_case       :				Optional[Any] 			=    requests.post(
                   UpperCamelCase_			,			json={
                       """oid""": oid,
                       """parts""": parts,
                   }			,			)
               r.raise_for_status()

               write_msg({"""event""": """complete""", """oid""": oid}			)





'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "model" in orig_key:
				lowerCAmelCase_   :       Union[str, Any]    							=orig_key.replace('''model.'''  ,					''''''  )
		if "norm1" in orig_key:
				lowerCAmelCase_   :       List[Any]    							=orig_key.replace('''norm1'''  ,					'''attention.output.LayerNorm'''  )
		if "norm2" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm2'''  ,					'''output.LayerNorm'''  )
		if "norm" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm'''  ,					'''LayerNorm'''  )
		if "transformer" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.split('''.'''  )[0].split('''_'''  )[-1]
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace(f'transformer_{layer_num}'  ,					f'encoder.layer.{layer_num}'  )
		if "mha.attn" in orig_key:
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace('''mha.attn'''  ,					'''attention.self'''  )
		if "mha" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mha'''  ,					'''attention'''  )
		if "W_q" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''W_q'''  ,					'''self.query'''  )
		if "W_k" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''W_k'''  ,					'''self.key'''  )
		if "W_v" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''W_v'''  ,					'''self.value'''  )
		if "ff.0" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''ff.0'''  ,					'''intermediate.dense'''  )
		if "ff.2" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.replace('''ff.2'''  ,					'''output.dense'''  )
		if "ff" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''ff'''  ,					'''output.dense'''  )
		if "mlm_class" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mlm.mlm_class'''  ,					'''cls.predictions.decoder'''  )
		if "mlm" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''mlm'''  ,					'''cls.predictions.transform'''  )
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''backbone.backbone.encoders'''  ,					'''encoder.layer'''  )
		if "cls" not in orig_key:
				lowerCAmelCase_   :       List[Any]    							='''mra.''' + orig_key

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowerCAmelCase_   :       Dict    							=val

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

		print(model.load_state_dict(_SCREAMING_SNAKE_CASE  )  )
		model.eval()
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )

		print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )


if __name__ == "__main__":
							__lowercase	=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The json file for Mra model config.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							__lowercase	=    parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






