





"""simple docstring"""







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()
SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =					'Hello world! cécé herlolip'





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase					,  UpperCamelCase										=  os.path.split(_lowercase						)
    UpperCamelCase										=  DataaVecTextModel.from_pretrained(
        _lowercase ,checkpoint_file=_lowercase						)
    dataavec.eval()  # disable dropout
    UpperCamelCase										=  dataavec.models[0]
    UpperCamelCase										=  dataavec_model.encoder.sentence_encoder
    UpperCamelCase										=  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										=  dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,_lowercase						)

    UpperCamelCase										=  DataaVecTextForSequenceClassification(_lowercase						) if classification_head else DataaVecTextForMaskedLM(_lowercase						)
    model.eval()

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

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

        # self attention
        UpperCamelCase										=  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										=  dataavec_layer.self_attn.q_proj.weight
        UpperCamelCase										=  dataavec_layer.self_attn.q_proj.bias
        UpperCamelCase										=  dataavec_layer.self_attn.k_proj.weight
        UpperCamelCase										=  dataavec_layer.self_attn.k_proj.bias
        UpperCamelCase										=  dataavec_layer.self_attn.v_proj.weight
        UpperCamelCase										=  dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        UpperCamelCase										=  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										=  dataavec_layer.self_attn.out_proj.weight
        UpperCamelCase										=  dataavec_layer.self_attn.out_proj.bias
        UpperCamelCase										=  dataavec_layer.self_attn_layer_norm.weight
        UpperCamelCase										=  dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        UpperCamelCase										=  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										=  dataavec_layer.fca.weight
        UpperCamelCase										=  dataavec_layer.fca.bias

        # output
        UpperCamelCase										=  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										=  dataavec_layer.fca.weight
        UpperCamelCase										=  dataavec_layer.fca.bias
        UpperCamelCase										=  dataavec_layer.final_layer_norm.weight
        UpperCamelCase										=  dataavec_layer.final_layer_norm.bias
        # end of layer

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

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

    UpperCamelCase										=  model(_lowercase						)[0]
    if classification_head:
        UpperCamelCase										=  dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(_lowercase						)						)
    else:
        UpperCamelCase										=  dataavec_model(_lowercase						)[0]
    print(our_output.shape ,their_output.shape						)
    UpperCamelCase										=  torch.max(torch.abs(our_output - their_output						)						).item()
    print(f'max_absolute_diff = {max_absolute_diff}'						)  # ~ 1e-7
    UpperCamelCase										=  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'''						)

    pathlib.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(
      '--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_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )





"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=7					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=1_8					,				lowerCamelCase_=3_0					,				lowerCamelCase_=4_0_0					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=None					,				)							->      Optional[Any]:
       UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 1_8}
       UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  num_frames
       UpperCamelCase										=  image_size
       UpperCamelCase										=  min_resolution
       UpperCamelCase										=  max_resolution
       UpperCamelCase										=  do_resize
       UpperCamelCase										=  size
       UpperCamelCase										=  do_normalize
       UpperCamelCase										=  image_mean
       UpperCamelCase										=  image_std
       UpperCamelCase										=  crop_size







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }

@require_torch
@require_vision
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







   @property
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.image_processor_tester.prepare_image_processor_dict()







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_mean'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_std'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_normalize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_resize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_center_crop'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''size'''))







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 1_8})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 1_8, '''width''': 1_8})

       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict					,				size=4_2					,				crop_size=8_4)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 4_2})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 8_4, '''width''': 8_4})







   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PIL videos
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				Image.Image)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				numpify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				np.ndarray)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				torchify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				torch.Tensor)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)