

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()
_lowerCamelCase							:  Union[str, Any]     =				logging.get_logger(__name__)

_lowerCamelCase							:  Dict     =				"Hello world! cécé herlolip"

def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     str							,					UpperCAmelCase				:     bool     )    ->		Optional[Any]:
  UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					os.path.split(UpperCAmelCase     )
  UpperCAmelCase			:				Optional[int]										=					DataaVecTextModel.from_pretrained(
      UpperCAmelCase							,					checkpoint_file=UpperCAmelCase     )
  dataavec.eval()  # disable dropout
  UpperCAmelCase			:				Any										=					dataavec.models[0]
  UpperCAmelCase			:				Tuple										=					dataavec_model.encoder.sentence_encoder
  UpperCAmelCase			:				List[str]										=					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:
    UpperCAmelCase			:				List[Any]										=					dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
  print('''Our BERT config:'''							,					UpperCAmelCase     )

  UpperCAmelCase			:				Any										=					DataaVecTextForSequenceClassification(UpperCAmelCase     ) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase     )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  UpperCAmelCase			:				Tuple										=					dataavec_sent_encoder.embed_tokens.weight
  UpperCAmelCase			:				int										=					dataavec_sent_encoder.embed_positions.weight
  UpperCAmelCase			:				Union[str, Any]										=					torch.zeros_like(
      model.dataavec_text.embeddings.token_type_embeddings.weight     )  # just zero them out b/c data2vec doesn't use them.
  UpperCAmelCase			:				List[Any]										=					dataavec_sent_encoder.layernorm_embedding.weight
  UpperCAmelCase			:				Any										=					dataavec_sent_encoder.layernorm_embedding.bias

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

    # self attention
    UpperCAmelCase			:				BertSelfAttention										=					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)     )}'''
    )

    UpperCAmelCase			:				Tuple										=					dataavec_layer.self_attn.q_proj.weight
    UpperCAmelCase			:				Any										=					dataavec_layer.self_attn.q_proj.bias
    UpperCAmelCase			:				Tuple										=					dataavec_layer.self_attn.k_proj.weight
    UpperCAmelCase			:				List[str]										=					dataavec_layer.self_attn.k_proj.bias
    UpperCAmelCase			:				Union[str, Any]										=					dataavec_layer.self_attn.v_proj.weight
    UpperCAmelCase			:				Optional[Any]										=					dataavec_layer.self_attn.v_proj.bias

    # self-attention output
    UpperCAmelCase			:				BertSelfOutput										=					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}'''
    UpperCAmelCase			:				List[str]										=					dataavec_layer.self_attn.out_proj.weight
    UpperCAmelCase			:				Dict										=					dataavec_layer.self_attn.out_proj.bias
    UpperCAmelCase			:				Any										=					dataavec_layer.self_attn_layer_norm.weight
    UpperCAmelCase			:				List[str]										=					dataavec_layer.self_attn_layer_norm.bias

    # intermediate
    UpperCAmelCase			:				BertIntermediate										=					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}'''
    UpperCAmelCase			:				List[str]										=					dataavec_layer.fca.weight
    UpperCAmelCase			:				int										=					dataavec_layer.fca.bias

    # output
    UpperCAmelCase			:				BertOutput										=					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}'''
    UpperCAmelCase			:				List[Any]										=					dataavec_layer.fca.weight
    UpperCAmelCase			:				Union[str, Any]										=					dataavec_layer.fca.bias
    UpperCAmelCase			:				List[Any]										=					dataavec_layer.final_layer_norm.weight
    UpperCAmelCase			:				Union[str, Any]										=					dataavec_layer.final_layer_norm.bias
    # end of layer

  if classification_head:
    UpperCAmelCase			:				Optional[Any]										=					dataavec.model.classification_heads['''mnli'''].dense.weight
    UpperCAmelCase			:				List[Any]										=					dataavec.model.classification_heads['''mnli'''].dense.bias
    UpperCAmelCase			:				Any										=					dataavec.model.classification_heads['''mnli'''].out_proj.weight
    UpperCAmelCase			:				List[Any]										=					dataavec.model.classification_heads['''mnli'''].out_proj.bias
  else:
    # LM Head
    UpperCAmelCase			:				Any										=					dataavec_model.encoder.lm_head.dense.weight
    UpperCAmelCase			:				Optional[int]										=					dataavec_model.encoder.lm_head.dense.bias
    UpperCAmelCase			:				List[str]										=					dataavec_model.encoder.lm_head.layer_norm.weight
    UpperCAmelCase			:				List[Any]										=					dataavec_model.encoder.lm_head.layer_norm.bias
    UpperCAmelCase			:				int										=					dataavec_model.encoder.lm_head.weight
    UpperCAmelCase			:				Any										=					dataavec_model.encoder.lm_head.bias

  # Let's check that we get the same results.
  UpperCAmelCase			:				torch.Tensor										=					dataavec.encode(UpperCAmelCase     ).unsqueeze(0     )  # batch of size 1

  UpperCAmelCase			:				Optional[Any]										=					model(UpperCAmelCase     )[0]
  if classification_head:
    UpperCAmelCase			:				Any										=					dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCAmelCase     )     )
  else:
    UpperCAmelCase			:				Union[str, Any]										=					dataavec_model(UpperCAmelCase     )[0]
  print(our_output.shape							,					their_output.shape     )
  UpperCAmelCase			:				str										=					torch.max(torch.abs(our_output - their_output     )     ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''     )  # ~ 1e-7
  UpperCAmelCase			:				int										=					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__":
     _lowerCamelCase							:  Optional[Any]     =				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."
     )
     _lowerCamelCase							:  Tuple     =				parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )








import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling

def      a__	(						UpperCAmelCase				:     Dict     )    ->		Any:
  UpperCAmelCase			:				List[str]										=					'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

  with open(UpperCAmelCase							,					'''wb'''     ) as f:
    with requests.get(UpperCAmelCase							,					stream=UpperCAmelCase     ) as req:
      for chunk in req.iter_content(chunk_size=2_048     ):
        f.write(UpperCAmelCase     )

def      a__	(						)    ->		VivitConfig:
  UpperCAmelCase			:				Any										=					VivitConfig()

  UpperCAmelCase			:				Any										=					400
  UpperCAmelCase			:				int										=					'''huggingface/label-files'''
  UpperCAmelCase			:				int										=					'''kinetics400-id2label.json'''

  UpperCAmelCase			:				Tuple										=					json.load(open(hf_hub_download(UpperCAmelCase							,					UpperCAmelCase							,					repo_type='''dataset'''     )							,					'''r'''     )     )
  UpperCAmelCase			:				Dict										=					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
  UpperCAmelCase			:				Optional[Any]										=					idalabel
  UpperCAmelCase			:				Tuple										=					{v: k for k, v in idalabel.items()}
  return config

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				int										=					hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''							,					filename='''eating_spaghetti_32_frames.npy'''							,					repo_type='''dataset'''     )
  UpperCAmelCase			:				Tuple										=					np.load(UpperCAmelCase     )
  return list(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  if np.ndim(UpperCAmelCase     ) == 2:
    return transform_attention_bias(UpperCAmelCase     )

  elif np.ndim(UpperCAmelCase     ) == 3:
    return transform_attention_kernel(UpperCAmelCase     )

  else:
    raise Exception(f'''Invalid number of dimesions: {np.ndim(UpperCAmelCase     )}'''     )

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		List[Any]:
  return current.flatten()

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Optional[Any]:
  return np.reshape(UpperCAmelCase							,					(current.shape[0], current.shape[1] * current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  return np.reshape(UpperCAmelCase							,					(current.shape[0] * current.shape[1], current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Union[str, Any]     )    ->		Dict:
  UpperCAmelCase			:				Any										=					state_dict['''optimizer''']['''target''']['''Transformer'''][f'''encoderblock_{i}''']

  UpperCAmelCase			:				Dict										=					f'''encoder.layer.{i}.'''
  UpperCAmelCase			:				Optional[int]										=					{
      prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
      prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']     ),
      prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
      prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']     ),
      prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
      prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
      prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
      prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
      prefix
      + '''attention.attention.query.bias''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']     ),
      prefix
      + '''attention.attention.query.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']     ),
      prefix
      + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']     ),
      prefix
      + '''attention.attention.key.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']     ),
      prefix
      + '''attention.attention.value.bias''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']     ),
      prefix
      + '''attention.attention.value.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']     ),
      prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
      prefix
      + '''attention.output.dense.weight''': transform_attention_output_weight(
          state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']     ),
  }

  return new_state

def      a__	(						UpperCAmelCase				:     Any     )    ->		List[str]:
  return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]     )

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict=False     )    ->		Dict:
  UpperCAmelCase			:				int										=					get_n_layers(UpperCAmelCase     )

  UpperCAmelCase			:				List[str]										=					OrderedDict()

  UpperCAmelCase			:				int										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
  UpperCAmelCase			:				Tuple										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

  UpperCAmelCase			:				Union[str, Any]										=					np.transpose(
      state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']							,					(4, 3, 0, 1, 2)     )
  UpperCAmelCase			:				List[str]										=					state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

  UpperCAmelCase			:				Optional[int]										=					state_dict['''optimizer''']['''target''']['''cls''']
  UpperCAmelCase			:				str										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
      '''pos_embedding'''
  ]

  for i in range(UpperCAmelCase     ):
    new_state.update(transform_state_encoder_block(UpperCAmelCase							,					UpperCAmelCase     )     )

  if classification_head:
    UpperCAmelCase			:				List[Any]										=					{'''vivit.''' + k: v for k, v in new_state.items()}
    UpperCAmelCase			:				str										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']     )
    UpperCAmelCase			:				Dict										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']     )

  return {k: torch.tensor(UpperCAmelCase     ) for k, v in new_state.items()}

def      a__	(						)    ->		VivitImageProcessor:
  UpperCAmelCase			:				Optional[int]										=					VivitImageProcessor()

  assert extractor.do_resize is True
  assert extractor.size == {"shortest_edge": 256}
  assert extractor.do_center_crop is True
  assert extractor.crop_size == {"width": 224, "height": 224}
  assert extractor.resample == PILImageResampling.BILINEAR

  # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
  # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
  # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
  assert extractor.do_normalize is False
  assert extractor.do_rescale is True
  assert extractor.rescale_factor == 1 / 255

  # zero-centering = True in original implementation
  assert extractor.do_zero_centering is True

  return extractor

def      a__	(						UpperCAmelCase				:     str     )    ->		Tuple:
  UpperCAmelCase			:				Union[str, Any]										=					'''checkpoint'''

  if not os.path.exists(UpperCAmelCase     ):
    download_checkpoint(UpperCAmelCase     )

  UpperCAmelCase			:				Tuple										=					restore_checkpoint(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Any										=					transform_state(UpperCAmelCase							,					classification_head=UpperCAmelCase     )

  UpperCAmelCase			:				Union[str, Any]										=					get_vivit_config()

  assert config.image_size == 224
  assert config.num_frames == 32

  UpperCAmelCase			:				Optional[int]										=					VivitForVideoClassification(UpperCAmelCase     )
  model.load_state_dict(UpperCAmelCase     )
  model.eval()

  UpperCAmelCase			:				List[Any]										=					get_processor()

  UpperCAmelCase			:				str										=					prepare_video()
  UpperCAmelCase			:				Union[str, Any]										=					extractor(UpperCAmelCase							,					return_tensors='''pt'''     )

  UpperCAmelCase			:				Optional[Any]										=					model(**UpperCAmelCase     )

  UpperCAmelCase			:				str										=					torch.Size([1, 400]     )
  UpperCAmelCase			:				int										=					torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]     )

  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, :5]							,					UpperCAmelCase							,					atol=1E-4     ), outputs.logits[0, :5]

  model.save_pretrained(UpperCAmelCase     )
  extractor.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
     _lowerCamelCase							:  Optional[Any]     =				argparse.ArgumentParser()

     parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

     _lowerCamelCase							:  str     =				parser.parse_args()
     convert(args.output_model_name)





