


'''simple docstring'''
import json
import os
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 AlignProcessor, EfficientNetImageProcessor
@require_vision
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= tempfile.mkdtemp()

							UpperCamelCase__   :Any												= [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							UpperCamelCase__   :List[Any]												= os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['''vocab_file''']							)
							with open(self.vocab_file    ,   '''w'''    ,   encoding='''utf-8'''							) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

							UpperCamelCase__   :List[str]												= {
							    '''do_resize''': True,
							    '''size''': 20,
							    '''do_center_crop''': True,
							    '''crop_size''': 18,
							    '''do_normalize''': True,
							    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
							    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
							}
							UpperCamelCase__   :int												= os.path.join(self.tmpdirname    ,   SCREAMING_SNAKE_CASE__							)
							with open(self.image_processor_file    ,   '''w'''    ,   encoding='''utf-8'''							) as fp:
												json.dump(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							)




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return BertTokenizer.from_pretrained(self.tmpdirname    ,   **SCREAMING_SNAKE_CASE__							)




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return BertTokenizerFast.from_pretrained(self.tmpdirname    ,   **SCREAMING_SNAKE_CASE__							)




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname    ,   **SCREAMING_SNAKE_CASE__							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							shutil.rmtree(self.tmpdirname							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= [np.random.randint(255    ,   size=(3, 30, 400)    ,   dtype=np.uinta							)]
							UpperCamelCase__   :Any												= [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__    ,   0    ,   -1							)							) for x in image_inputs]
							return image_inputs




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.get_tokenizer()
							UpperCamelCase__   :Optional[Any]												= self.get_rust_tokenizer()
							UpperCamelCase__   :List[str]												= self.get_image_processor()

							UpperCamelCase__   :Union[str, Any]												= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__    ,   image_processor=SCREAMING_SNAKE_CASE__							)
							processor_slow.save_pretrained(self.tmpdirname							)
							UpperCamelCase__   :Dict												= AlignProcessor.from_pretrained(self.tmpdirname    ,   use_fast=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :Any												= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__    ,   image_processor=SCREAMING_SNAKE_CASE__							)
							processor_fast.save_pretrained(self.tmpdirname							)
							UpperCamelCase__   :str												= AlignProcessor.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    ,   SCREAMING_SNAKE_CASE__							)
							self.assertIsInstance(processor_fast.tokenizer    ,   SCREAMING_SNAKE_CASE__							)

							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    ,   SCREAMING_SNAKE_CASE__							)
							self.assertIsInstance(processor_fast.image_processor    ,   SCREAMING_SNAKE_CASE__							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= AlignProcessor(tokenizer=self.get_tokenizer()    ,   image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							UpperCamelCase__   :List[Any]												= self.get_tokenizer(bos_token='''(BOS)'''    ,   eos_token='''(EOS)'''							)
							UpperCamelCase__   :List[str]												= self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__    ,   padding_value=1.0							)

							UpperCamelCase__   :str												= AlignProcessor.from_pretrained(
							    self.tmpdirname    ,   bos_token='''(BOS)'''    ,   eos_token='''(EOS)'''    ,   do_normalize=SCREAMING_SNAKE_CASE__    ,   padding_value=1.0							)

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

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= self.get_image_processor()
							UpperCamelCase__   :int												= self.get_tokenizer()

							UpperCamelCase__   :Any												= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__    ,   image_processor=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :int												= self.prepare_image_inputs()

							UpperCamelCase__   :int												= image_processor(SCREAMING_SNAKE_CASE__    ,   return_tensors='''np'''							)
							UpperCamelCase__   :List[Any]												= processor(images=SCREAMING_SNAKE_CASE__    ,   return_tensors='''np'''							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= self.get_image_processor()
							UpperCamelCase__   :List[Any]												= self.get_tokenizer()

							UpperCamelCase__   :str												= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__    ,   image_processor=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :int												= '''lower newer'''

							UpperCamelCase__   :List[Any]												= processor(text=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :List[Any]												= tokenizer(SCREAMING_SNAKE_CASE__    ,   padding='''max_length'''    ,   max_length=64							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= self.get_image_processor()
							UpperCamelCase__   :Any												= self.get_tokenizer()

							UpperCamelCase__   :Optional[Any]												= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__    ,   image_processor=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :Any												= '''lower newer'''
							UpperCamelCase__   :Dict												= self.prepare_image_inputs()

							UpperCamelCase__   :Dict												= processor(text=SCREAMING_SNAKE_CASE__    ,   images=SCREAMING_SNAKE_CASE__							)

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

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.get_image_processor()
							UpperCamelCase__   :List[str]												= self.get_tokenizer()

							UpperCamelCase__   :Optional[int]												= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__    ,   image_processor=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :str												= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase__   :Any												= processor.batch_decode(SCREAMING_SNAKE_CASE__							)
							UpperCamelCase__   :Optional[int]												= tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)

							self.assertListEqual(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.get_image_processor()
							UpperCamelCase__   :Tuple												= self.get_tokenizer()

							UpperCamelCase__   :List[Any]												= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__    ,   image_processor=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :Optional[Any]												= '''lower newer'''
							UpperCamelCase__   :List[Any]												= self.prepare_image_inputs()

							UpperCamelCase__   :Any												= processor(text=SCREAMING_SNAKE_CASE__    ,   images=SCREAMING_SNAKE_CASE__							)

							self.assertListEqual(list(inputs.keys()							)    ,   processor.model_input_names							)
'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
_A			:      str					      =				logging.get_logger(__name__)

_A			:      str					      =				[
    ['''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      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 if k == "embeddings.weight":
  return "shared.weight"

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

 if k.startswith("""encoder"""      ):
  __lowerCAmelCase											=						k.replace(""".attn"""						,		""".self_attn"""      )
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""final_layer_norm"""      )
 elif k.startswith("""decoder"""      ):
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""encoder_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm3"""						,		"""final_layer_norm"""      )
 return k







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """model.encoder.layernorm_embedding.weight""",
     """model.encoder.layernorm_embedding.bias""",
     """model.decoder.layernorm_embedding.weight""",
     """model.decoder.layernorm_embedding.bias""",
 ]
 for k in keys:
  __lowerCAmelCase											=						sd.pop(snake_case_      )
  __lowerCAmelCase											=						k.replace("""layernorm_embedding"""						,		"""layer_norm"""      )
  assert new_k not in sd
  __lowerCAmelCase											=						v


_A			:      int					      =				['''START''']







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Any						,		snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						model["""model"""]
 __lowerCAmelCase											=						BlenderbotConfig.from_json_file(snake_case_      )
 __lowerCAmelCase											=						BlenderbotForConditionalGeneration(snake_case_      )
 __lowerCAmelCase											=						m.model.state_dict().keys()
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						{}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

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


if __name__ == "__main__":
 _A			:      Optional[Any]					      =				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'''
 )
 _A			:      Tuple					      =				parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






