'''simple docstring'''






import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
    raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
    raise Exception('requires fairseq < v2')

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

SCREAMING_SNAKE_CASE_	   =			'Hello, World!'
SCREAMING_SNAKE_CASE_	   =			'en_XX'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str   ,    _lowercase    :				bool     )							->	Optional[Any]:
  __UpperCAmelCase:					Tuple		= Path("""data_bin"""     )
  __UpperCAmelCase:					Dict		= FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(_lowercase     ).parent     )   ,    checkpoint_file=Path(_lowercase     ).name   ,    _name="""xmod_base"""   ,    arch="""xmod_base"""   ,    task="""multilingual_masked_lm"""   ,    data_name_or_path=str(_lowercase     )   ,    bpe="""sentencepiece"""   ,    sentencepiece_model=str(Path(_lowercase     ).parent / """sentencepiece.bpe.model"""     )   ,    src_dict=str(data_dir / """dict.txt"""     )   ,    )
  xmod.eval()  # disable dropout
  print(_lowercase     )

  __UpperCAmelCase:					Optional[Any]		= xmod.model.encoder.sentence_encoder
  __UpperCAmelCase:					List[Any]		= XmodConfig(
      vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings   ,    hidden_size=xmod.cfg.model.encoder_embed_dim   ,    num_hidden_layers=xmod.cfg.model.encoder_layers   ,    num_attention_heads=xmod.cfg.model.encoder_attention_heads   ,    intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim   ,    max_position_embeddings=5_1_4   ,    type_vocab_size=1   ,    layer_norm_eps=1E-5   ,    pre_norm=xmod.cfg.model.encoder_normalize_before   ,    adapter_reduction_factor=getattr(xmod.cfg.model   ,    """bottleneck"""   ,    2     )   ,    adapter_layer_norm=xmod.cfg.model.adapter_layer_norm   ,    adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm   ,    ln_before_adapter=xmod.cfg.model.ln_before_adapter   ,    languages=xmod.cfg.model.languages   ,    )
  if classification_head:
    __UpperCAmelCase:					int		= xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our X-MOD config:"""   ,    _lowercase     )

  __UpperCAmelCase:					int		= XmodForSequenceClassification(_lowercase     ) if classification_head else XmodForMaskedLM(_lowercase     )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  __UpperCAmelCase:					str		= xmod_sent_encoder.embed_tokens.weight
  __UpperCAmelCase:					Optional[Any]		= xmod_sent_encoder.embed_positions.weight
  __UpperCAmelCase:					str		= torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c xmod doesn't use them.

  __UpperCAmelCase:					str		= xmod_sent_encoder.layernorm_embedding.weight
  __UpperCAmelCase:					List[Any]		= xmod_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers     ):
    # Encoder: start of layer
    __UpperCAmelCase:					List[Any]		= model.roberta.encoder.layer[i]
    __UpperCAmelCase:					List[str]		= xmod_sent_encoder.layers[i]

    # self attention
    __UpperCAmelCase:					List[str]		= layer.attention.self
    if not (
        xmod_layer.self_attn.k_proj.weight.data.shape
        == xmod_layer.self_attn.q_proj.weight.data.shape
        == xmod_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)     )
    ):
      raise AssertionError("""Dimensions of self-attention weights do not match."""     )

    __UpperCAmelCase:					Union[str, Any]		= xmod_layer.self_attn.q_proj.weight
    __UpperCAmelCase:					Any		= xmod_layer.self_attn.q_proj.bias
    __UpperCAmelCase:					Optional[Any]		= xmod_layer.self_attn.k_proj.weight
    __UpperCAmelCase:					int		= xmod_layer.self_attn.k_proj.bias
    __UpperCAmelCase:					Dict		= xmod_layer.self_attn.v_proj.weight
    __UpperCAmelCase:					List[str]		= xmod_layer.self_attn.v_proj.bias

    # self-attention output
    __UpperCAmelCase:					str		= layer.attention.output
    if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
      raise AssertionError("""Dimensions of self-attention output weights do not match."""     )
    __UpperCAmelCase:					Dict		= xmod_layer.self_attn.out_proj.weight
    __UpperCAmelCase:					List[Any]		= xmod_layer.self_attn.out_proj.bias
    __UpperCAmelCase:					Union[str, Any]		= xmod_layer.self_attn_layer_norm.weight
    __UpperCAmelCase:					Optional[int]		= xmod_layer.self_attn_layer_norm.bias

    # intermediate
    __UpperCAmelCase:					Optional[Any]		= layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of intermediate weights do not match."""     )
    __UpperCAmelCase:					Optional[Any]		= xmod_layer.fca.weight
    __UpperCAmelCase:					str		= xmod_layer.fca.bias

    # output
    __UpperCAmelCase:					str		= layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""     )
    __UpperCAmelCase:					Optional[int]		= xmod_layer.fca.weight
    __UpperCAmelCase:					int		= xmod_layer.fca.bias
    __UpperCAmelCase:					Optional[Any]		= xmod_layer.final_layer_norm.weight
    __UpperCAmelCase:					Any		= xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      __UpperCAmelCase:					Optional[int]		= xmod_layer.adapter_layer_norm.weight
      __UpperCAmelCase:					Optional[int]		= xmod_layer.adapter_layer_norm.bias

    if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
      raise AssertionError("""Lists of language adapters do not match."""     )
    for lang_code, adapter in xmod_layer.adapter_modules.items():
      __UpperCAmelCase:					List[Any]		= bert_output.adapter_modules[lang_code]
      __UpperCAmelCase:					Optional[Any]		= xmod_layer.adapter_modules[lang_code]
      __UpperCAmelCase:					Dict		= from_adapter.fca.weight
      __UpperCAmelCase:					Optional[int]		= from_adapter.fca.bias
      __UpperCAmelCase:					Optional[Any]		= from_adapter.fca.weight
      __UpperCAmelCase:					Tuple		= from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    __UpperCAmelCase:					Dict		= xmod_sent_encoder.layer_norm.weight
    __UpperCAmelCase:					Optional[int]		= xmod_sent_encoder.layer_norm.bias

  if classification_head:
    __UpperCAmelCase:					Optional[int]		= xmod.model.classification_heads["""mnli"""].dense.weight
    __UpperCAmelCase:					List[Any]		= xmod.model.classification_heads["""mnli"""].dense.bias
    __UpperCAmelCase:					Union[str, Any]		= xmod.model.classification_heads["""mnli"""].out_proj.weight
    __UpperCAmelCase:					Optional[Any]		= xmod.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    __UpperCAmelCase:					Tuple		= xmod.model.encoder.lm_head.dense.weight
    __UpperCAmelCase:					int		= xmod.model.encoder.lm_head.dense.bias
    __UpperCAmelCase:					Tuple		= xmod.model.encoder.lm_head.layer_norm.weight
    __UpperCAmelCase:					Any		= xmod.model.encoder.lm_head.layer_norm.bias
    __UpperCAmelCase:					List[Any]		= xmod.model.encoder.lm_head.weight
    __UpperCAmelCase:					List[Any]		= xmod.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  __UpperCAmelCase:					Optional[Any]		= xmod.encode(_lowercase     ).unsqueeze(0     )  # batch of size 1
  model.roberta.set_default_language(_lowercase     )

  __UpperCAmelCase:					Dict		= model(_lowercase     )[0]
  if classification_head:
    __UpperCAmelCase:					List[str]		= xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase     )     )
  else:
    __UpperCAmelCase:					Union[str, Any]		= xmod.model(_lowercase   ,    lang_id=[SAMPLE_LANGUAGE]     )[0]
  print(our_output.shape   ,    their_output.shape     )
  __UpperCAmelCase:					Optional[int]		= torch.max(torch.abs(our_output - their_output     )     ).item()
  print(F'''max_absolute_diff = {max_absolute_diff}'''     )  # ~ 1e-7
  __UpperCAmelCase:					Optional[int]		= torch.allclose(_lowercase   ,    _lowercase   ,    atol=1E-3     )
  print("""Do both models output the same tensors?"""   ,    """🔥""" if success else """💩"""     )
  if not success:
    raise Exception("""Something went wRoNg"""     )

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--xmod_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.'
    )
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )
'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""timesformer"""







  def __init__(							self   ,    snake_case_=224   ,    snake_case_=16   ,    snake_case_=3   ,    snake_case_=8   ,    snake_case_=768   ,    snake_case_=12   ,    snake_case_=12   ,    snake_case_=3072   ,    snake_case_="gelu"   ,    snake_case_=0.0   ,    snake_case_=0.0   ,    snake_case_=0.0_2   ,    snake_case_=1e-6   ,    snake_case_=True   ,    snake_case_="divided_space_time"   ,    snake_case_=0   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)

    __UpperCAmelCase:					Tuple		= image_size
    __UpperCAmelCase:					List[Any]		= patch_size
    __UpperCAmelCase:					Optional[Any]		= num_channels
    __UpperCAmelCase:					int		= num_frames

    __UpperCAmelCase:					List[str]		= hidden_size
    __UpperCAmelCase:					List[str]		= num_hidden_layers
    __UpperCAmelCase:					Dict		= num_attention_heads
    __UpperCAmelCase:					List[str]		= intermediate_size
    __UpperCAmelCase:					Union[str, Any]		= hidden_act
    __UpperCAmelCase:					Dict		= hidden_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Union[str, Any]		= initializer_range
    __UpperCAmelCase:					List[Any]		= layer_norm_eps
    __UpperCAmelCase:					int		= qkv_bias

    __UpperCAmelCase:					str		= attention_type
    __UpperCAmelCase:					Tuple		= drop_path_rate