





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 			__UpperCamelCase				(			lowercase__	:       str		,    lowercase__	:       str		,    lowercase__	:       bool		)	->      str:
    '''simple docstring'''





    lowerCAmelCase_					:	Dict						=   os.path.split(lowercase__		)
    lowerCAmelCase_					:	Optional[Any]						=   DataaVecTextModel.from_pretrained(
        lowercase__		,    checkpoint_file=lowercase__		)
    dataavec.eval()  # disable dropout
    lowerCAmelCase_					:	str						=   dataavec.models[0]
    lowerCAmelCase_					:	Optional[int]						=   dataavec_model.encoder.sentence_encoder
    lowerCAmelCase_					:	Optional[int]						=   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:
        lowerCAmelCase_					:	List[str]						=   dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
    print("""Our BERT config:"""		,    lowercase__		)

    lowerCAmelCase_					:	Optional[Any]						=   DataaVecTextForSequenceClassification(lowercase__		) if classification_head else DataaVecTextForMaskedLM(lowercase__		)
    model.eval()

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

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

        # self attention
        lowerCAmelCase_					:	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)		)}'
        )

        lowerCAmelCase_					:	Union[str, Any]						=   dataavec_layer.self_attn.q_proj.weight
        lowerCAmelCase_					:	int						=   dataavec_layer.self_attn.q_proj.bias
        lowerCAmelCase_					:	List[str]						=   dataavec_layer.self_attn.k_proj.weight
        lowerCAmelCase_					:	int						=   dataavec_layer.self_attn.k_proj.bias
        lowerCAmelCase_					:	Dict						=   dataavec_layer.self_attn.v_proj.weight
        lowerCAmelCase_					:	Union[str, Any]						=   dataavec_layer.self_attn.v_proj.bias

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

        # intermediate
        lowerCAmelCase_					:	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}'
        lowerCAmelCase_					:	Optional[int]						=   dataavec_layer.fca.weight
        lowerCAmelCase_					:	Optional[Any]						=   dataavec_layer.fca.bias

        # output
        lowerCAmelCase_					:	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}'
        lowerCAmelCase_					:	int						=   dataavec_layer.fca.weight
        lowerCAmelCase_					:	Optional[Any]						=   dataavec_layer.fca.bias
        lowerCAmelCase_					:	str						=   dataavec_layer.final_layer_norm.weight
        lowerCAmelCase_					:	List[str]						=   dataavec_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        lowerCAmelCase_					:	int						=   dataavec.model.classification_heads["""mnli"""].dense.weight
        lowerCAmelCase_					:	Dict						=   dataavec.model.classification_heads["""mnli"""].dense.bias
        lowerCAmelCase_					:	Dict						=   dataavec.model.classification_heads["""mnli"""].out_proj.weight
        lowerCAmelCase_					:	Any						=   dataavec.model.classification_heads["""mnli"""].out_proj.bias
    else:
        # LM Head
        lowerCAmelCase_					:	Dict						=   dataavec_model.encoder.lm_head.dense.weight
        lowerCAmelCase_					:	Tuple						=   dataavec_model.encoder.lm_head.dense.bias
        lowerCAmelCase_					:	Dict						=   dataavec_model.encoder.lm_head.layer_norm.weight
        lowerCAmelCase_					:	Tuple						=   dataavec_model.encoder.lm_head.layer_norm.bias
        lowerCAmelCase_					:	Union[str, Any]						=   dataavec_model.encoder.lm_head.weight
        lowerCAmelCase_					:	int						=   dataavec_model.encoder.lm_head.bias

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

    lowerCAmelCase_					:	Optional[Any]						=   model(lowercase__		)[0]
    if classification_head:
        lowerCAmelCase_					:	List[Any]						=   dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(lowercase__		)		)
    else:
        lowerCAmelCase_					:	Any						=   dataavec_model(lowercase__		)[0]
    print(our_output.shape		,    their_output.shape		)
    lowerCAmelCase_					:	Union[str, Any]						=   torch.max(torch.abs(our_output - their_output		)		).item()
    print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
    lowerCAmelCase_					:	str						=   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__":
  __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
  )









import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )

@require_vision
class 		__a							(	unittest.TestCase      ):



       def  A							(     self	:   List[str]			):
           lowerCAmelCase_					:	Dict						=   tempfile.mkdtemp()

           # fmt: off
           lowerCAmelCase_					:	Dict						=   ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
           # fmt: on
           lowerCAmelCase_					:	Any						=   os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES["""vocab_file"""]			)

           with open(self.vocab_file					,			"""w"""					,			encoding="""utf-8"""			) as fp:
               fp.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)

           lowerCAmelCase_					:	int						=   {
               """image_mean""": FLAVA_IMAGE_MEAN,
               """image_std""": FLAVA_IMAGE_STD,
               """do_normalize""": True,
               """do_resize""": True,
               """size""": 2_24,
               """do_center_crop""": True,
               """crop_size""": 2_24,
               """input_size_patches""": 14,
               """total_mask_patches""": 75,
               """mask_group_max_patches""": None,
               """mask_group_min_patches""": 16,
               """mask_group_min_aspect_ratio""": 0.3,
               """mask_group_max_aspect_ratio""": None,
               """codebook_do_resize""": True,
               """codebook_size""": 1_12,
               """codebook_do_center_crop""": True,
               """codebook_crop_size""": 1_12,
               """codebook_do_map_pixels""": True,
               """codebook_do_normalize""": True,
               """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
               """codebook_image_std""": FLAVA_CODEBOOK_STD,
           }

           lowerCAmelCase_					:	Union[str, Any]						=   os.path.join(self.tmpdirname					,			UpperCAmelCase			)
           with open(self.image_processor_file					,			"""w"""					,			encoding="""utf-8"""			) as fp:
               json.dump(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   Any					,			**UpperCAmelCase	:   Union[str, Any]			):
           return BertTokenizer.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   List[str]					,			**UpperCAmelCase	:   str			):
           return BertTokenizerFast.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Any					,			**UpperCAmelCase	:   int			):
           return FlavaImageProcessor.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Optional[int]			):
           shutil.rmtree(self.tmpdirname			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Union[str, Any]						=   [np.random.randint(2_55					,			size=(3, 30, 4_00)					,			dtype=np.uinta			)]

           lowerCAmelCase_					:	int						=   [Image.fromarray(np.moveaxis(UpperCAmelCase					,			0					,			-1			)			) for x in image_inputs]

           return image_inputs



       def  A							(     self	:   Tuple			):
           lowerCAmelCase_					:	str						=   self.get_tokenizer()
           lowerCAmelCase_					:	Dict						=   self.get_rust_tokenizer()
           lowerCAmelCase_					:	Optional[int]						=   self.get_image_processor()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)
           processor_slow.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	List[str]						=   FlavaProcessor.from_pretrained(self.tmpdirname					,			use_fast=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)
           processor_fast.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	List[str]						=   FlavaProcessor.from_pretrained(self.tmpdirname			)

           self.assertEqual(processor_slow.tokenizer.get_vocab()					,			tokenizer_slow.get_vocab()			)
           self.assertEqual(processor_fast.tokenizer.get_vocab()					,			tokenizer_fast.get_vocab()			)
           self.assertEqual(tokenizer_slow.get_vocab()					,			tokenizer_fast.get_vocab()			)
           self.assertIsInstance(processor_slow.tokenizer					,			UpperCAmelCase			)
           self.assertIsInstance(processor_fast.tokenizer					,			UpperCAmelCase			)

           self.assertEqual(processor_slow.image_processor.to_json_string()					,			image_processor.to_json_string()			)
           self.assertEqual(processor_fast.image_processor.to_json_string()					,			image_processor.to_json_string()			)
           self.assertIsInstance(processor_slow.image_processor					,			UpperCAmelCase			)
           self.assertIsInstance(processor_fast.image_processor					,			UpperCAmelCase			)



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	Any						=   FlavaProcessor(tokenizer=self.get_tokenizer()					,			image_processor=self.get_image_processor()			)
           processor.save_pretrained(self.tmpdirname			)

           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer(bos_token="""(BOS)"""					,			eos_token="""(EOS)"""			)
           lowerCAmelCase_					:	Optional[Any]						=   self.get_image_processor(do_normalize=UpperCAmelCase					,			padding_value=1.0			)

           lowerCAmelCase_					:	Any						=   FlavaProcessor.from_pretrained(
               self.tmpdirname					,			bos_token="""(BOS)"""					,			eos_token="""(EOS)"""					,			do_normalize=UpperCAmelCase					,			padding_value=1.0			)

           self.assertEqual(processor.tokenizer.get_vocab()					,			tokenizer_add_kwargs.get_vocab()			)
           self.assertIsInstance(processor.tokenizer					,			UpperCAmelCase			)

           self.assertEqual(processor.image_processor.to_json_string()					,			image_processor_add_kwargs.to_json_string()			)
           self.assertIsInstance(processor.image_processor					,			UpperCAmelCase			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	Tuple						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	List[str]						=   image_processor(UpperCAmelCase					,			return_tensors="""np"""			)
           lowerCAmelCase_					:	Tuple						=   processor(images=UpperCAmelCase					,			return_tensors="""np"""			)

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()					,			input_processor[key].sum()					,			delta=1e-2			)

           # With rest of the args
           random.seed(12_34			)
           lowerCAmelCase_					:	Optional[Any]						=   image_processor(
               UpperCAmelCase					,			return_image_mask=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_tensors="""np"""			)
           random.seed(12_34			)
           lowerCAmelCase_					:	Dict						=   processor(
               images=UpperCAmelCase					,			return_image_mask=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_tensors="""np"""			)

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()					,			input_processor[key].sum()					,			delta=1e-2			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	List[Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	str						=   """lower newer"""

           lowerCAmelCase_					:	Dict						=   processor(text=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   tokenizer(UpperCAmelCase			)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]					,			encoded_processor[key]			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	Optional[int]						=   self.get_image_processor()
           lowerCAmelCase_					:	List[Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	str						=   """lower newer"""
           lowerCAmelCase_					:	Any						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	Optional[int]						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase			)

           self.assertListEqual(list(inputs.keys()			)					,			["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]			)

           # add extra args
           lowerCAmelCase_					:	str						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_image_mask=UpperCAmelCase			)

           self.assertListEqual(
               list(inputs.keys()			)					,			[
                   """input_ids""",
                   """token_type_ids""",
                   """attention_mask""",
                   """pixel_values""",
                   """codebook_pixel_values""",
                   """bool_masked_pos""",
               ]					,			)

           # test if it raises when no input is passed
           with pytest.raises(UpperCAmelCase			):
               processor()



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	str						=   self.get_image_processor()
           lowerCAmelCase_					:	int						=   self.get_tokenizer()

           lowerCAmelCase_					:	Union[str, Any]						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[Any]						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowerCAmelCase_					:	List[str]						=   processor.batch_decode(UpperCAmelCase			)
           lowerCAmelCase_					:	Tuple						=   tokenizer.batch_decode(UpperCAmelCase			)

           self.assertListEqual(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	int						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	List[str]						=   """lower newer"""
           lowerCAmelCase_					:	Dict						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	Optional[int]						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase			)

           self.assertListEqual(list(inputs.keys()			)					,			processor.model_input_names			)


