




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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class 							__lowercase   (			unittest.TestCase    ):



     def 				__magic_name__		(  self   :							List[Any]       ):
       a_		    =  tempfile.mkdtemp()

       a_		    =  CLIPImageProcessor()
       a_		    =  BertTokenizer.from_pretrained(
           '''hf-internal-testing/tiny-random-BertModel'''      ,		model_input_names=['''input_ids''', '''attention_mask''']       )

       a_		    =  GitProcessor(lowerCAmelCase__      ,		lowerCAmelCase__       )

       processor.save_pretrained(self.tmpdirname       )



     def 				__magic_name__		(  self   :							List[str]      ,		**lowercase__   :							Optional[Any]       ):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,		**lowerCAmelCase__       ).tokenizer



     def 				__magic_name__		(  self   :							Any      ,		**lowercase__   :							str       ):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,		**lowerCAmelCase__       ).image_processor



     def 				__magic_name__		(  self   :							Optional[int]       ):
       shutil.rmtree(self.tmpdirname       )



     def 				__magic_name__		(  self   :							List[Any]       ):
       a_		    =  [np.random.randint(2_5_5      ,		size=(3, 3_0, 4_0_0)      ,		dtype=np.uinta       )]

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

       return image_inputs



     def 				__magic_name__		(  self   :							str       ):
       a_		    =  GitProcessor(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=lowerCAmelCase__      ,		padding_value=1.0       )

       a_		    =  GitProcessor.from_pretrained(
           self.tmpdirname      ,		bos_token='''(BOS)'''      ,		eos_token='''(EOS)'''      ,		do_normalize=lowerCAmelCase__      ,		padding_value=1.0       )

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

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



     def 				__magic_name__		(  self   :							List[Any]       ):
       a_		    =  self.get_image_processor()
       a_		    =  self.get_tokenizer()

       a_		    =  GitProcessor(tokenizer=lowerCAmelCase__      ,		image_processor=lowerCAmelCase__       )

       a_		    =  self.prepare_image_inputs()

       a_		    =  image_processor(lowerCAmelCase__      ,		return_tensors='''np'''       )
       a_		    =  processor(images=lowerCAmelCase__      ,		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 				__magic_name__		(  self   :							List[Any]       ):
       a_		    =  self.get_image_processor()
       a_		    =  self.get_tokenizer()

       a_		    =  GitProcessor(tokenizer=lowerCAmelCase__      ,		image_processor=lowerCAmelCase__       )

       a_		    =  '''lower newer'''

       a_		    =  processor(text=lowerCAmelCase__       )

       a_		    =  tokenizer(lowerCAmelCase__      ,		return_token_type_ids=lowerCAmelCase__       )

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



     def 				__magic_name__		(  self   :							List[Any]       ):
       a_		    =  self.get_image_processor()
       a_		    =  self.get_tokenizer()

       a_		    =  GitProcessor(tokenizer=lowerCAmelCase__      ,		image_processor=lowerCAmelCase__       )

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

       a_		    =  processor(text=lowerCAmelCase__      ,		images=lowerCAmelCase__       )

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

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



     def 				__magic_name__		(  self   :							Tuple       ):
       a_		    =  self.get_image_processor()
       a_		    =  self.get_tokenizer()

       a_		    =  GitProcessor(tokenizer=lowerCAmelCase__      ,		image_processor=lowerCAmelCase__       )

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

       a_		    =  processor.batch_decode(lowerCAmelCase__       )
       a_		    =  tokenizer.batch_decode(lowerCAmelCase__       )

       self.assertListEqual(lowerCAmelCase__      ,		lowerCAmelCase__       )



     def 				__magic_name__		(  self   :							int       ):
       a_		    =  self.get_image_processor()
       a_		    =  self.get_tokenizer()

       a_		    =  GitProcessor(tokenizer=lowerCAmelCase__      ,		image_processor=lowerCAmelCase__       )

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

       a_		    =  processor(text=lowerCAmelCase__      ,		images=lowerCAmelCase__       )

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__							=			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["XLMProphetNetTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMProphetNetDecoder",
          "XLMProphetNetEncoder",
          "XLMProphetNetForCausalLM",
          "XLMProphetNetForConditionalGeneration",
          "XLMProphetNetModel",
          "XLMProphetNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase__							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



