



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()
A_		:  Optional[int]										=  logging.get_logger(__name__)

A_		:  List[Any]										=  'Hello world! cécé herlolip'


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Optional[Any]       ):






 '''simple docstring'''

 a							=		os.path.split(_UpperCAmelCase       )
 a							=		DataaVecTextModel.from_pretrained(
     _UpperCAmelCase			,       checkpoint_file=_UpperCAmelCase       )
 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=5_14			,       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:"			,       _UpperCAmelCase       )

 a							=		DataaVecTextForSequenceClassification(_UpperCAmelCase       ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase       )
 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(_UpperCAmelCase       ).unsqueeze(0       )  # batch of size 1

 a							=		model(_UpperCAmelCase       )[0]
 if classification_head:
  a							=		dataavec.model.classification_heads['mnli'](dataavec.extract_features(_UpperCAmelCase       )       )
 else:
  a							=		dataavec_model(_UpperCAmelCase       )[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(_UpperCAmelCase			,       _UpperCAmelCase			,       atol=1E-3       )
 print("Do both models output the same tensors?"			,       "🔥" if success else "💩"       )
 if not success:
  raise Exception("Something went wRoNg"       )

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


if __name__ == "__main__":
     A_		:  str										=  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.'''
     )
     A_		:  Any										=  parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )








"""simple docstring"""




import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCamelCase_	:		int         =   pathlib.Path.cwd().joinpath('project_euler')
lowerCamelCase_	:		str         =   pathlib.Path.cwd().joinpath(
    'scripts', 'project_euler_answers.json'
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
   lowerCamelCase_	:		dict[str, str]         =   json.load(file_handle)





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    importlib.util.spec_from_file_location(file_path.name					,   str(_UpperCAmelCase   )   )
  A_    :			int									=    importlib.util.module_from_spec(_UpperCAmelCase   )  # type: ignore
  spec.loader.exec_module(_UpperCAmelCase   )  # type: ignore
  return module





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Tuple									=    []
  for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
    if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'   ):
      continue
    for file_path in problem_dir_path.iterdir():
      if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')   ):
        continue
      solution_file_paths.append(_UpperCAmelCase   )
  return solution_file_paths





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  with open(os.environ['GITHUB_EVENT_PATH']   ) as file:
    A_    :			str									=    json.load(_UpperCAmelCase   )
  return event["pull_request"]["url"] + "/files"





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Any									=    []
  A_    :			Optional[Any]									=    {
      'Accept': 'application/vnd.github.v3+json',
      'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
  }
  A_    :			Union[str, Any]									=    requests.get(get_files_url()					,   headers=_UpperCAmelCase   ).json()
  for file in files:
    A_    :			Optional[int]									=    pathlib.Path.cwd().joinpath(file['filename']   )
    if (
        filepath.suffix != ".py"
        or filepath.name.startswith(('_', 'test')   )
        or not filepath.name.startswith('sol'   )
    ):
      continue
    solution_file_paths.append(_UpperCAmelCase   )
  return solution_file_paths





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  if os.environ.get('CI'   ) and os.environ.get('GITHUB_EVENT_NAME'   ) == "pull_request":
    # Return only if there are any, otherwise default to all solutions
    if filepaths := added_solution_file_path():
      return filepaths
  return all_solution_file_paths()





@pytest.mark.parametrize(
    'solution_path'					,   collect_solution_file_paths()					,   ids=lambda _UpperCAmelCase   : f"""{path.parent.name}/{path.name}"""					,   )
def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			str									=    solution_path.parent.name[8:].zfill(3   )
  A_    :			str									=    PROBLEM_ANSWERS[problem_number]
  A_    :			List[Any]									=    convert_path_to_module(_UpperCAmelCase   )
  A_    :			Optional[int]									=    str(solution_module.solution()   )  # type: ignore
  A_    :			Any									=    hashlib.shaaaa(answer.encode()   ).hexdigest()
  assert (
      answer == expected
  ), f"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""