






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Optional[int]:




       """simple docstring"""


       a						    = tempfile.mkdtemp()

       a						    = BlipImageProcessor()
       a						    = BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''    )

       a						    = BlipProcessor(__UpperCAmelCase					,   __UpperCAmelCase    )

       processor.save_pretrained(self.tmpdirname    )







      def  __lowerCAmelCase   ( self :		Optional[int]					,   **__UpperCAmelCase :		int    )       ->Tuple:




       """simple docstring"""


       return AutoProcessor.from_pretrained(self.tmpdirname					,   **__UpperCAmelCase    ).tokenizer







      def  __lowerCAmelCase   ( self :		Dict					,   **__UpperCAmelCase :		Dict    )       ->Optional[Any]:




       """simple docstring"""


       return AutoProcessor.from_pretrained(self.tmpdirname					,   **__UpperCAmelCase    ).image_processor







      def  __lowerCAmelCase   ( self :		List[str]    )       ->List[str]:




       """simple docstring"""


       shutil.rmtree(self.tmpdirname    )







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Dict:




       """simple docstring"""


       a						    = [np.random.randint(255					,   size=(3, 30, 400)					,   dtype=np.uinta    )]

       a						    = [Image.fromarray(np.moveaxis(__UpperCAmelCase					,   0					,   -1    )    ) for x in image_inputs]

       return image_inputs







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->List[str]:




       """simple docstring"""


       a						    = BlipProcessor(tokenizer=self.get_tokenizer()					,   image_processor=self.get_image_processor()    )
       processor.save_pretrained(self.tmpdirname    )

       a						    = self.get_tokenizer(bos_token='''(BOS)'''					,   eos_token='''(EOS)'''    )
       a						    = self.get_image_processor(do_normalize=__UpperCAmelCase					,   padding_value=1.0    )

       a						    = BlipProcessor.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  __lowerCAmelCase   ( self :		int    )       ->str:




       """simple docstring"""


       a						    = self.get_image_processor()
       a						    = self.get_tokenizer()

       a						    = BlipProcessor(tokenizer=__UpperCAmelCase					,   image_processor=__UpperCAmelCase    )

       a						    = self.prepare_image_inputs()

       a						    = image_processor(__UpperCAmelCase					,   return_tensors='''np'''    )
       a						    = 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    )







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




       """simple docstring"""


       a						    = self.get_image_processor()
       a						    = self.get_tokenizer()

       a						    = BlipProcessor(tokenizer=__UpperCAmelCase					,   image_processor=__UpperCAmelCase    )

       a						    = '''lower newer'''

       a						    = processor(text=__UpperCAmelCase    )

       a						    = tokenizer(__UpperCAmelCase					,   return_token_type_ids=__UpperCAmelCase    )

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







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->str:




       """simple docstring"""


       a						    = self.get_image_processor()
       a						    = self.get_tokenizer()

       a						    = BlipProcessor(tokenizer=__UpperCAmelCase					,   image_processor=__UpperCAmelCase    )

       a						    = '''lower newer'''
       a						    = self.prepare_image_inputs()

       a						    = processor(text=__UpperCAmelCase					,   images=__UpperCAmelCase    )

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

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







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




       """simple docstring"""


       a						    = self.get_image_processor()
       a						    = self.get_tokenizer()

       a						    = BlipProcessor(tokenizer=__UpperCAmelCase					,   image_processor=__UpperCAmelCase    )

       a						    = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       a						    = processor.batch_decode(__UpperCAmelCase    )
       a						    = tokenizer.batch_decode(__UpperCAmelCase    )

       self.assertListEqual(__UpperCAmelCase					,   __UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Optional[int]:




       """simple docstring"""


       a						    = self.get_image_processor()
       a						    = self.get_tokenizer()

       a						    = BlipProcessor(tokenizer=__UpperCAmelCase					,   image_processor=__UpperCAmelCase    )

       a						    = '''lower newer'''
       a						    = self.prepare_image_inputs()

       a						    = processor(text=__UpperCAmelCase					,   images=__UpperCAmelCase    )

       # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
       self.assertListEqual(list(inputs.keys()    )					,   ['''pixel_values''', '''input_ids''', '''attention_mask''']    )










import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

UpperCAmelCase__							  =						[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]
def 		_a       ( a					:Optional[Any]					)				->     List[Any]:
 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  a						    = k.replace(a      ,				a					)

 if k.startswith('''encoder'''					):
  a						    = k.replace('''.attn'''      ,				'''.self_attn'''					)
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''final_layer_norm'''					)
 elif k.startswith('''decoder'''					):
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''encoder_attn_layer_norm'''					)
  a						    = k.replace('''norm3'''      ,				'''final_layer_norm'''					)
 return k
def 		_a       ( a					:Dict					)				->     Tuple:
 a						    = [
     '''model.encoder.layernorm_embedding.weight''',
     '''model.encoder.layernorm_embedding.bias''',
     '''model.decoder.layernorm_embedding.weight''',
     '''model.decoder.layernorm_embedding.bias''',
 ]
 for k in keys:
  a						    = sd.pop(a					)
  a						    = k.replace('''layernorm_embedding'''      ,				'''layer_norm'''					)
  assert new_k not in sd
  a						    = v


UpperCAmelCase__							  =						["START"]
@torch.no_grad()
def 		_a       ( a					:Dict      ,				a					:str      ,				a					:int					)				->     int:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 a						    = model['''model''']
 a						    = BlenderbotConfig.from_json_file(a					)
 a						    = BlenderbotForConditionalGeneration(a					)
 a						    = m.model.state_dict().keys()
 a						    = []
 a						    = {}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  a						    = rename_state_dict_key(a					)
  if new_k not in valid_keys:
   failures.append([k, new_k]					)
  else:
   a						    = v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(a					)
 m.model.load_state_dict(a      ,				strict=a					)
 m.half()
 m.save_pretrained(a					)


if __name__ == "__main__":
     UpperCAmelCase__							  =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
     parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
     parser.add_argument(
         "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
     )
     UpperCAmelCase__							  =						parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


