



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_  :Tuple     = {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_  :str     = [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_  :int     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













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,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )







@require_vision
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		tempfile.mkdtemp()

             _lowerCamelCase				:  List[str]						=		BlipImageProcessor()
             _lowerCamelCase				:  Union[str, Any]						=		GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''')
             _lowerCamelCase				:  Any						=		BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''')

             _lowerCamelCase				:  Any						=		InstructBlipProcessor(a__   ,				a__   ,				a__)

             processor.save_pretrained(self.tmpdirname)


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             return AutoProcessor.from_pretrained(self.tmpdirname   ,				**a__).qformer_tokenizer


      def 					__snake_case      (		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta)]

             _lowerCamelCase				:  List[str]						=		[Image.fromarray(np.moveaxis(a__   ,				0   ,				-1)) for x in image_inputs]

             return image_inputs


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()   ,				qformer_tokenizer=self.get_qformer_tokenizer()   ,				)
             processor.save_pretrained(self.tmpdirname)

             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)''')
             _lowerCamelCase				:  str						=		self.get_image_processor(do_normalize=a__   ,				padding_value=1.0)

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor.from_pretrained(
                 self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=a__   ,				padding_value=1.0)

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

             self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor_add_kwargs.to_json_string())
             self.assertIsInstance(processor.image_processor   ,				a__)
             self.assertIsInstance(processor.qformer_tokenizer   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Any						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Dict						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		self.prepare_image_inputs()

             _lowerCamelCase				:  List[str]						=		image_processor(a__   ,				return_tensors='''np''')
             _lowerCamelCase				:  List[Any]						=		processor(images=a__   ,				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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  int						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''

             _lowerCamelCase				:  Any						=		processor(text=a__)

             _lowerCamelCase				:  Optional[int]						=		tokenizer(a__   ,				return_token_type_ids=a__)
             _lowerCamelCase				:  Optional[Any]						=		qformer_tokenizer(a__   ,				return_token_type_ids=a__)

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

             for key in encoded_tokens_qformer.keys():
                    self.assertListEqual(encoded_tokens_qformer[key]   ,				encoded_processor['''qformer_''' + key])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Optional[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  List[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''
             _lowerCamelCase				:  Tuple						=		self.prepare_image_inputs()

             _lowerCamelCase				:  Tuple						=		processor(text=a__   ,				images=a__)

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

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


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.get_image_processor()
             _lowerCamelCase				:  List[Any]						=		self.get_tokenizer()
             _lowerCamelCase				:  List[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase				:  Tuple						=		processor.batch_decode(a__)
             _lowerCamelCase				:  str						=		tokenizer.batch_decode(a__)

             self.assertListEqual(a__   ,				a__)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  str						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  str						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  str						=		'''lower newer'''
             _lowerCamelCase				:  str						=		self.prepare_image_inputs()

             _lowerCamelCase				:  List[Any]						=		processor(text=a__   ,				images=a__)

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

