
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        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      tempfile.mkdtemp()

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

				_snake_case          =      BlipProcessor(lowercase      ,  lowercase     )

				processor.save_pretrained(self.tmpdirname     )



	def 			A							(  self     :   Tuple      ,  **lowercase     :   Dict     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Tuple      ,  **lowercase     :   List[Any]     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				shutil.rmtree(self.tmpdirname     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


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

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

				return image_inputs



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


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

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

				_snake_case          =      BlipProcessor.from_pretrained(
				    self.tmpdirname      ,  bos_token='(BOS)'      ,  eos_token='(EOS)'      ,  do_normalize=lowercase      ,  padding_value=1.0     )

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

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



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.get_image_processor()
				_snake_case          =      self.get_tokenizer()

				_snake_case          =      BlipProcessor(tokenizer=lowercase      ,  image_processor=lowercase     )

				_snake_case          =      self.prepare_image_inputs()

				_snake_case          =      image_processor(lowercase      ,  return_tensors='np'     )
				_snake_case          =      processor(images=lowercase      ,  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     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.get_image_processor()
				_snake_case          =      self.get_tokenizer()

				_snake_case          =      BlipProcessor(tokenizer=lowercase      ,  image_processor=lowercase     )

				_snake_case          =      'lower newer'

				_snake_case          =      processor(text=lowercase     )

				_snake_case          =      tokenizer(lowercase      ,  return_token_type_ids=lowercase     )

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



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.get_image_processor()
				_snake_case          =      self.get_tokenizer()

				_snake_case          =      BlipProcessor(tokenizer=lowercase      ,  image_processor=lowercase     )

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

				_snake_case          =      processor(text=lowercase      ,  images=lowercase     )

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

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



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.get_image_processor()
				_snake_case          =      self.get_tokenizer()

				_snake_case          =      BlipProcessor(tokenizer=lowercase      ,  image_processor=lowercase     )

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

				_snake_case          =      processor.batch_decode(lowercase     )
				_snake_case          =      tokenizer.batch_decode(lowercase     )

				self.assertListEqual(lowercase      ,  lowercase     )





	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.get_image_processor()
				_snake_case          =      self.get_tokenizer()

				_snake_case          =      BlipProcessor(tokenizer=lowercase      ,  image_processor=lowercase     )

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

				_snake_case          =      processor(text=lowercase      ,  images=lowercase     )

				# 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 torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)