

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


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


lowerCamelCase_    =					'''lfs-multipart-upload'''







class  __lowerCamelCase				(	__snake_case	):







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase   )						->       int:
        snake_case_       						=      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=lowerCamelCase							,  help="""Local path to repository you want to configure."""   )
        enable_parser.set_defaults(func=lambda lowerCamelCase   : LfsEnableCommand(lowerCamelCase   )   )

        snake_case_       						=      parser.add_parser(
            lowerCamelCase							,  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(lowerCamelCase   )   )







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase   )						->       Dict:
        snake_case_       						=      args







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



def    UpperCamelCase(					lowercase_							)	->				List[Any]:





  '''simple docstring'''





  snake_case_       						=      json.dumps(lowercase_							) + """\n"""
  sys.stdout.write(lowercase_							)
  sys.stdout.flush()



def    UpperCamelCase(					)	->				Optional[Dict]:





  '''simple docstring'''





  snake_case_       						=      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  __lowerCamelCase				(	__snake_case	):







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[int]:
        snake_case_       						=      filepath
        snake_case_       						=      seek_from
        snake_case_       						=      read_limit
        snake_case_       						=      0







      def __enter__(     self   )						->       Tuple:
        snake_case_       						=      open(self.filepath							,  """rb"""   )
        self.f.seek(self.seek_from   )
        return self







      def __len__(     self   )						->       Optional[int]:
        snake_case_       						=      os.fstat(self.f.fileno()   ).st_size
        return min(self.read_limit							,  total_length - self.seek_from   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase=-1   )						->       str:
        if self.n_seen >= self.read_limit:
          return b""
        snake_case_       						=      self.read_limit - self.n_seen
        snake_case_       						=      self.f.read(remaining_amount if n < 0 else min(lowerCamelCase							,  lowerCamelCase   )   )
        self.n_seen += len(lowerCamelCase   )
        return data







      def __iter__(     self   )						->       Dict:
        yield self.read(n=4 * 1024 * 1024   )







      def __exit__(     self							,  *lowerCamelCase   )						->       Tuple:
        self.f.close()







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      args







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # 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_       						=      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_       						=      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_       						=      msg["""oid"""]
          snake_case_       						=      msg["""path"""]
          snake_case_       						=      msg["""action"""]["""href"""]
          snake_case_       						=      msg["""action"""]["""header"""]
          snake_case_       						=      int(header.pop("""chunk_size"""   )   )
          snake_case_       						=      list(header.values()   )

          snake_case_       						=      []
          for i, presigned_url in enumerate(lowerCamelCase   ):
            with FileSlice(lowerCamelCase							,  seek_from=i * chunk_size							,  read_limit=lowerCamelCase   ) as data:
              snake_case_       						=      requests.put(lowerCamelCase							,  data=lowerCamelCase   )
              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_       						=      requests.post(
              lowerCamelCase							,  json={
                  """oid""": oid,
                  """parts""": parts,
              }							,  )
          r.raise_for_status()

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




"""simple docstring"""
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :  Tuple										=					logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				List[nn.Module]        =       field(default_factory=A__			)
					lowercase__      :				list        =       field(default_factory=A__			)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = len(list(m.modules()      )      ) == 1 or isinstance(lowerCamelCase__ ,				nn.Convad      ) or isinstance(lowerCamelCase__ ,				nn.BatchNormad      )
									if has_not_submodules:
													if not isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
																	self.traced.append(lowerCamelCase__      )

					def __call__(			self ,				lowerCamelCase__      ):
									for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook      )      )
									self.module(lowerCamelCase__      )
									[x.remove() for x in self.handles]
									return self





					@property
					def 	snake_case__					(			self      ):
									# check the len of the state_dict keys to see if we have learnable params
									return list(filter(lambda lowerCamelCase__      : len(list(x.state_dict().keys()      )      ) > 0 ,				self.traced      )      )


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				nn.Module
					lowercase__      :				int        =       0
					lowercase__      :				List        =       field(default_factory=A__			)
					lowercase__      :				List        =       field(default_factory=A__			)

					def __call__(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = Tracker(self.dest      )(lowerCamelCase__      ).parametrized
									_lowerCamelCase					     = Tracker(self.src      )(lowerCamelCase__      ).parametrized

									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.src_skip ,				lowerCamelCase__      )      )
									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.dest_skip ,				lowerCamelCase__      )      )

									if len(lowerCamelCase__      ) != len(lowerCamelCase__      ):
													raise Exception(
													    F"""Numbers of operations are different. Source module has {len(lowerCamelCase__      )} operations while"""
													    F""" destination module has {len(lowerCamelCase__      )}."""      )

									for dest_m, src_m in zip(lowerCamelCase__ ,				lowerCamelCase__      ):
													dest_m.load_state_dict(src_m.state_dict()      )
													if self.verbose == 1:
																	print(F"""Transfered from={src_m} to={dest_m}"""      )



def 				lowerCAmelCase_(      lowercase_   :							nn.Module  ,      lowercase_   :							nn.Module							)		->				nn.Module:
				# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
				_lowerCamelCase					     = from_model.state_dict()
				_lowerCamelCase					     = our_model.state_dict()
				_lowerCamelCase					     = our_model.config
				_lowerCamelCase					     = []
				for stage_idx in range(len(config.hidden_sizes							)							):
								for block_id in range(config.depths[stage_idx]							):
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)							)
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

												all_keys.append((from_key, to_key)							)

				for from_key, to_key in all_keys:
								_lowerCamelCase					     = from_state_dict.pop(lowercase_							)

				our_model.load_state_dict(lowercase_							)
				return our_model



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							VanConfig  ,      lowercase_   :							str  ,      lowercase_   :							nn.Module  ,      lowercase_   :							Path  ,      lowercase_   :							bool = True  ,      )		->				Union[str, Any]:
				print(F"""Downloading weights for {name}..."""							)
				_lowerCamelCase					     = cached_download(lowercase_							)
				print(F"""Converting {name}..."""							)
				_lowerCamelCase					     = torch.load(lowercase_							)['''state_dict''']
				from_model.load_state_dict(lowercase_							)
				from_model.eval()
				with torch.no_grad():
								_lowerCamelCase					     = VanForImageClassification(lowercase_							).eval()
								_lowerCamelCase					     = ModuleTransfer(src=lowercase_  ,      dest=lowercase_							)
								_lowerCamelCase					     = torch.randn((1, 3, 2_24, 2_24)							)
								module_transfer(lowercase_							)
								_lowerCamelCase					     = copy_parameters(lowercase_  ,      lowercase_							)

				if not torch.allclose(from_model(lowercase_							)  ,      our_model(lowercase_							).logits							):
								raise ValueError('''The model logits don\'t match the original one.'''							)

				_lowerCamelCase					     = name
				print(lowercase_							)

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add model'''  ,      use_temp_dir=lowercase_  ,      )

								# we can use the convnext one
								_lowerCamelCase					     = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							)
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add image processor'''  ,      use_temp_dir=lowercase_  ,      )

								print(F"""Pushed {checkpoint_name}"""							)



def 				lowerCAmelCase_(      lowercase_   :							Path  ,      lowercase_   :							str = None  ,      lowercase_   :							bool = True							)		->				List[str]:
				_lowerCamelCase					     = '''imagenet-1k-id2label.json'''
				_lowerCamelCase					     = 10_00

				_lowerCamelCase					     = '''huggingface/label-files'''
				_lowerCamelCase					     = num_labels
				_lowerCamelCase					     = json.load(open(hf_hub_download(lowercase_  ,      lowercase_  ,      repo_type='''dataset'''							)  ,      '''r'''							)							)
				_lowerCamelCase					     = {int(lowercase_							): v for k, v in idalabel.items()}

				_lowerCamelCase					     = idalabel
				_lowerCamelCase					     = {v: k for k, v in idalabel.items()}

				_lowerCamelCase					     = partial(lowercase_  ,      num_labels=lowercase_  ,      idalabel=lowercase_  ,      labelaid=lowercase_							)

				_lowerCamelCase					     = {
				    '''van-tiny''': ImageNetPreTrainedConfig(
				        hidden_sizes=[32, 64, 1_60, 2_56]  ,      depths=[3, 3, 5, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-small''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[2, 2, 4, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-base''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 3, 12, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-large''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 5, 27, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': van_tiny,
				    '''van-small''': van_small,
				    '''van-base''': van_base,
				    '''van-large''': van_large,
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
				    ),
				    '''van-small''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
				    ),
				    '''van-base''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
				    ),
				    '''van-large''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
				    ),
				}

				if model_name:
								convert_weight_and_push(
								    lowercase_  ,      names_to_config[model_name]  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    lowercase_  ,      lowercase_  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  Any										=					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model-name''',
			    default=None,
			    type=str,
			    help=(
			        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
			        ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=Path,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--van_dir''',
			    required=True,
			    type=Path,
			    help=(
			        '''A path to VAN\'s original implementation directory. You can download from here:'''
			        ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
			    ),
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    default=True,
			    type=bool,
			    required=False,
			    help='''If True, push model and image processor to the hub.''',
			)

			__SCREAMING_SNAKE_CASE     :  int										=					parser.parse_args()
			__SCREAMING_SNAKE_CASE     :  Path										=					args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			__SCREAMING_SNAKE_CASE     :  List[str]										=					args.van_dir
			# append the path to the parents to maskformer dir
			sys.path.append(str(van_dir.parent))
			from van.models.van import van_base, van_large, van_small, van_tiny

			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
