"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)  # pylint: disable=invalid-name


_SCREAMING_SNAKE_CASE	:		List[str]   =   '''lfs-multipart-upload'''






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					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=a_ ,				help='Local path to repository you want to configure.'	)
      enable_parser.set_defaults(func=lambda _SCREAMING_SNAKE_CASE	: LfsEnableCommand(a_	)	)

      __lowerCamelCase    		=					parser.add_parser(
          a_ ,				help=(
              'Deprecated: use `huggingface-cli` instead. '
              'Command will get called by git-lfs, do not call it directly.'
          ) ,				)
      upload_parser.set_defaults(func=lambda _SCREAMING_SNAKE_CASE	: LfsUploadCommand(a_	)	)






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					args







   def    _A				(	self	):


      '''simple docstring'''

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



def       A__    (	lowercase_     :	Dict						)  ->							Dict:
   __lowerCamelCase    		=					json.dumps(__a						) + '\n'
   sys.stdout.write(__a						)
   sys.stdout.flush()



def       A__    (	)  ->							Any:
   __lowerCamelCase    		=					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							(			UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					filepath
      __lowerCamelCase    		=					seek_from
      __lowerCamelCase    		=					read_limit
      __lowerCamelCase    		=					0







   def __enter__(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					open(self.filepath ,				'rb'	)
      self.f.seek(self.seek_from	)
      return self







   def __len__(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					os.fstat(self.f.fileno()	).st_size
      return min(self.read_limit ,				total_length - self.seek_from	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE=-1	):


      '''simple docstring'''

      if self.n_seen >= self.read_limit:
         return b""
      __lowerCamelCase    		=					self.read_limit - self.n_seen
      __lowerCamelCase    		=					self.f.read(remaining_amount if n < 0 else min(a_ ,				a_	)	)
      self.n_seen += len(a_	)
      return data







   def __iter__(	self	):


      '''simple docstring'''

      yield self.read(n=4 * 1024 * 1024	)







   def __exit__(	self ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      self.f.close()






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					args







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					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:
         __lowerCamelCase    		=					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	)

         __lowerCamelCase    		=					msg['oid']
         __lowerCamelCase    		=					msg['path']
         __lowerCamelCase    		=					msg['action']['href']
         __lowerCamelCase    		=					msg['action']['header']
         __lowerCamelCase    		=					int(header.pop('chunk_size'	)	)
         __lowerCamelCase    		=					list(header.values()	)

         __lowerCamelCase    		=					[]
         for i, presigned_url in enumerate(a_	):
            with FileSlice(a_ ,				seek_from=i * chunk_size ,				read_limit=a_	) as data:
               __lowerCamelCase    		=					requests.put(a_ ,				data=a_	)
               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.

         __lowerCamelCase    		=					requests.post(
             a_ ,				json={
                 'oid': oid,
                 'parts': parts,
             } ,				)
         r.raise_for_status()

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











import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
	raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				dataavec.eval()  # disable dropout
				a__					   =		dataavec.models[0]
				a__					   =		dataavec_model.encoder.sentence_encoder
				a__					   =		DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings			,		hidden_size=dataavec_model.args.encoder_embed_dim			,		num_hidden_layers=dataavec_model.args.encoder_layers			,		num_attention_heads=dataavec_model.args.encoder_attention_heads			,		intermediate_size=dataavec_model.args.encoder_ffn_embed_dim			,		max_position_embeddings=514			,		type_vocab_size=1			,		layer_norm_eps=1e-5			,		)
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				a__					   =		dataavec_sent_encoder.embed_tokens.weight
				a__					   =		dataavec_sent_encoder.embed_positions.weight
				a__					   =		torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
				a__					   =		dataavec_sent_encoder.layernorm_embedding.weight
				a__					   =		dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers		):
								# Encoder: start of layer
								a__					   =		model.dataavec_text.encoder.layer[i]
								a__					   =		dataavec_sent_encoder.layers[i]

								# self attention
								a__					   =		layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)

								a__					   =		dataavec_layer.self_attn.q_proj.weight
								a__					   =		dataavec_layer.self_attn.q_proj.bias
								a__					   =		dataavec_layer.self_attn.k_proj.weight
								a__					   =		dataavec_layer.self_attn.k_proj.bias
								a__					   =		dataavec_layer.self_attn.v_proj.weight
								a__					   =		dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								a__					   =		layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								a__					   =		dataavec_layer.self_attn.out_proj.weight
								a__					   =		dataavec_layer.self_attn.out_proj.bias
								a__					   =		dataavec_layer.self_attn_layer_norm.weight
								a__					   =		dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								a__					   =		layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias

								# output
								a__					   =		layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias
								a__					   =		dataavec_layer.final_layer_norm.weight
								a__					   =		dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""].dense.weight
								a__					   =		dataavec.model.classification_heads["""mnli"""].dense.bias
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.bias
				else:
								# LM Head
								a__					   =		dataavec_model.encoder.lm_head.dense.weight
								a__					   =		dataavec_model.encoder.lm_head.dense.bias
								a__					   =		dataavec_model.encoder.lm_head.layer_norm.weight
								a__					   =		dataavec_model.encoder.lm_head.layer_norm.bias
								a__					   =		dataavec_model.encoder.lm_head.weight
								a__					   =		dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				a__					   =		dataavec.encode(__a		).unsqueeze(0		)  # batch of size 1

				a__					   =		model(__a		)[0]
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__a		)		)
				else:
								a__					   =		dataavec_model(__a		)[0]
				print(our_output.shape			,		their_output.shape		)
				a__					   =		torch.max(torch.abs(our_output - their_output		)		).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
				a__					   =		torch.allclose(__a			,		__a			,		atol=1e-3		)
				print("""Do both models output the same tensors?"""			,		"""🔥""" if success else """💩"""		)
				if not success:
								raise Exception("""Something went wRoNg"""		)

				pathlib.Path(__a		).mkdir(parents=__a			,		exist_ok=__a		)
				print(F'''Saving model to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__a		)


if __name__ == "__main__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)

