



'''simple docstring'''





import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class __A      :



 '''simple docstring'''







 def __init__(self			,       A			,       A=12			,       A=7			,       A=True			,       A=True			,       A=True			,       A=99			,       A=32			,       A=32			,       A=5			,       A=4			,       A=37			,       A=0.1			,       A=0.1			,       A=512			,       A=0.02			,       A=0			,       A=None			,       )   ->					Optional[int]:


      """simple docstring"""



      _a     =  parent
      _a     =  batch_size
      _a     =  seq_length
      _a     =  is_training
      _a     =  use_input_mask
      _a     =  use_labels
      _a     =  vocab_size
      _a     =  hidden_size
      _a     =  projection_dim
      _a     =  num_hidden_layers
      _a     =  num_attention_heads
      _a     =  intermediate_size
      _a     =  dropout
      _a     =  attention_dropout
      _a     =  max_position_embeddings
      _a     =  initializer_range
      _a     =  scope
      _a     =  bos_token_id



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  ids_tensor([self.batch_size, self.seq_length]			,       self.vocab_size	)

      _a     =  None
      if self.use_input_mask:
           _a     =  random_attention_mask([self.batch_size, self.seq_length]	)

      if input_mask is not None:
           _a  ,   _a     =  input_mask.shape
           _a     =  np.random.randint(1			,       seq_length - 1			,       size=(batch_size,)	)
           for batch_idx, start_index in enumerate(A	):
                _a     =  1
                _a     =  0

      _a     =  self.get_config()

      return config, input_ids, input_mask



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      return BlipTextConfig(
          vocab_size=self.vocab_size			,       hidden_size=self.hidden_size			,       projection_dim=self.projection_dim			,       num_hidden_layers=self.num_hidden_layers			,       num_attention_heads=self.num_attention_heads			,       intermediate_size=self.intermediate_size			,       dropout=self.dropout			,       attention_dropout=self.attention_dropout			,       max_position_embeddings=self.max_position_embeddings			,       initializer_range=self.initializer_range			,       bos_token_id=self.bos_token_id			,       )



 def  a__  (self			,       A			,       A			,       A	)   ->					List[str]:


      """simple docstring"""



      _a     =  BlipTextModel(config=A	)
      model.to(A	)
      model.eval()
      with torch.no_grad():
           _a     =  model(A			,       attention_mask=A	)
           _a     =  model(A	)
      self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size)	)
      self.parent.assertEqual(result.pooler_output.shape			,       (self.batch_size, self.hidden_size)	)



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  self.prepare_config_and_inputs()
      _a  ,   _a  ,   _a     =  config_and_inputs
      _a     =  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class __A      (  A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	List[Any]										=	(BlipTextModel,) if is_torch_available() else ()
 __lowerCamelCase   :	List[str]										=	False
 __lowerCamelCase   :	List[Any]										=	False
 __lowerCamelCase   :	int										=	False



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  BlipTextModelTester(self	)
      _a     =  ConfigTester(self			,       config_class=A			,       hidden_size=37	)



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def  a__  (self	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      pass



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      pass



 @unittest.skip(reason='''Blip does not use inputs_embeds'''	)
 def  a__  (self	)   ->					int:


      """simple docstring"""



      pass



 @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''	)
 def  a__  (self	)   ->					str:


      """simple docstring"""



      pass



 @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''	)
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      pass



 @slow
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a     =  BlipTextModel.from_pretrained(A	)
           self.assertIsNotNone(A	)



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      super().test_pt_tf_model_equivalence(allow_missing_keys=A	)








'''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()
lowercase_			=  logging.get_logger(__name__)

lowercase_			=  "Hello world! cécé herlolip"




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a  ,   _a     =  os.path.split(__A)
     _a     =  DataaVecTextModel.from_pretrained(
         __A  ,     checkpoint_file=__A)
     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=514  ,     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:'''  ,     __A)

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

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

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


if __name__ == "__main__":
  lowercase_			=  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."
  )
  lowercase_			=  parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )



