

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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Optional[int]					=			tempfile.mkdtemp()

     UpperCAmelCase	:							Dict					=			BlipImageProcessor()
     UpperCAmelCase	:							Dict					=			GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''						)

     UpperCAmelCase	:							Any					=			BlipaProcessor(__A,		__A						)

     processor.save_pretrained(self.tmpdirname						)



   def 	__magic_name__   (							self							:					Dict,		**__A							:					str						):
     return AutoProcessor.from_pretrained(self.tmpdirname,		**__A						).tokenizer



   def 	__magic_name__   (							self							:					Tuple,		**__A							:					int						):
     return AutoProcessor.from_pretrained(self.tmpdirname,		**__A						).image_processor



   def 	__magic_name__   (							self							:					int						):
     shutil.rmtree(self.tmpdirname						)



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

     UpperCAmelCase	:							Union[str, Any]					=			[Image.fromarray(np.moveaxis(__A,		0,		-1						)						) for x in image_inputs]

     return image_inputs



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							List[Any]					=			BlipaProcessor(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	:							Union[str, Any]					=			self.get_image_processor(do_normalize=__A,		padding_value=1.0						)

     UpperCAmelCase	:							Dict					=			BlipaProcessor.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						)



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Tuple					=			self.get_image_processor()
     UpperCAmelCase	:							List[str]					=			self.get_tokenizer()

     UpperCAmelCase	:							Any					=			BlipaProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							Tuple					=			self.prepare_image_inputs()

     UpperCAmelCase	:							str					=			image_processor(__A,		return_tensors='''np'''						)
     UpperCAmelCase	:							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 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[Any]					=			self.get_image_processor()
     UpperCAmelCase	:							str					=			self.get_tokenizer()

     UpperCAmelCase	:							Union[str, Any]					=			BlipaProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							Optional[int]					=			'''lower newer'''

     UpperCAmelCase	:							Any					=			processor(text=__A						)

     UpperCAmelCase	:							List[Any]					=			tokenizer(__A,		return_token_type_ids=__A						)

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



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							int					=			self.get_image_processor()
     UpperCAmelCase	:							Optional[int]					=			self.get_tokenizer()

     UpperCAmelCase	:							Optional[Any]					=			BlipaProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							str					=			'''lower newer'''
     UpperCAmelCase	:							Optional[int]					=			self.prepare_image_inputs()

     UpperCAmelCase	:							Any					=			processor(text=__A,		images=__A						)

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

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



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Tuple					=			self.get_image_processor()
     UpperCAmelCase	:							Any					=			self.get_tokenizer()

     UpperCAmelCase	:							List[str]					=			BlipaProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							Union[str, Any]					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase	:							Any					=			processor.batch_decode(__A						)
     UpperCAmelCase	:							Tuple					=			tokenizer.batch_decode(__A						)

     self.assertListEqual(__A,		__A						)





   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Dict					=			self.get_image_processor()
     UpperCAmelCase	:							List[str]					=			self.get_tokenizer()

     UpperCAmelCase	:							Dict					=			BlipaProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							List[str]					=			'''lower newer'''
     UpperCAmelCase	:							str					=			self.prepare_image_inputs()

     UpperCAmelCase	:							Tuple					=			processor(text=__A,		images=__A						)

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase    :       str        =							{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       List[Any]        =							["XLNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Optional[Any]        =							["XLNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Dict        =							[
         "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "XLNetForMultipleChoice",
         "XLNetForQuestionAnswering",
         "XLNetForQuestionAnsweringSimple",
         "XLNetForSequenceClassification",
         "XLNetForTokenClassification",
         "XLNetLMHeadModel",
         "XLNetModel",
         "XLNetPreTrainedModel",
         "load_tf_weights_in_xlnet",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Union[str, Any]        =							[
         "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFXLNetForMultipleChoice",
         "TFXLNetForQuestionAnsweringSimple",
         "TFXLNetForSequenceClassification",
         "TFXLNetForTokenClassification",
         "TFXLNetLMHeadModel",
         "TFXLNetMainLayer",
         "TFXLNetModel",
         "TFXLNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet import XLNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet_fast import XLNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlnet import (
              XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLNetForMultipleChoice,
              XLNetForQuestionAnswering,
              XLNetForQuestionAnsweringSimple,
              XLNetForSequenceClassification,
              XLNetForTokenClassification,
              XLNetLMHeadModel,
              XLNetModel,
              XLNetPreTrainedModel,
              load_tf_weights_in_xlnet,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlnet import (
              TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLNetForMultipleChoice,
              TFXLNetForQuestionAnsweringSimple,
              TFXLNetForSequenceClassification,
              TFXLNetForTokenClassification,
              TFXLNetLMHeadModel,
              TFXLNetMainLayer,
              TFXLNetModel,
              TFXLNetPreTrainedModel,
          )

else:
     import sys

     _lowerCamelCase    :       Optional[Any]        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





