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 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	tempfile.mkdtemp()

       lowerCAmelCase_:							Any       =	BlipImageProcessor()
       lowerCAmelCase_:							List[str]       =	BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )

       lowerCAmelCase_:							Optional[int]       =	BlipProcessor(lowerCamelCase__       ,						lowerCamelCase__ )

       processor.save_pretrained(self.tmpdirname )




    def      _a    (    self       ,						**lowerCamelCase__ ):
       return AutoProcessor.from_pretrained(self.tmpdirname       ,						**lowerCamelCase__ ).tokenizer




    def      _a    (    self       ,						**lowerCamelCase__ ):
       return AutoProcessor.from_pretrained(self.tmpdirname       ,						**lowerCamelCase__ ).image_processor




    def      _a    (    self ):
       shutil.rmtree(self.tmpdirname )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	[np.random.randint(255       ,						size=(3, 30, 400)       ,						dtype=np.uinta )]

       lowerCAmelCase_:							Dict       =	[Image.fromarray(np.moveaxis(lowerCamelCase__       ,						0       ,						-1 ) ) for x in image_inputs]

       return image_inputs




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	BlipProcessor(tokenizer=self.get_tokenizer()       ,						image_processor=self.get_image_processor() )
       processor.save_pretrained(self.tmpdirname )

       lowerCAmelCase_:							List[str]       =	self.get_tokenizer(bos_token="(BOS)"       ,						eos_token="(EOS)" )
       lowerCAmelCase_:							Optional[Any]       =	self.get_image_processor(do_normalize=lowerCamelCase__       ,						padding_value=1.0 )

       lowerCAmelCase_:							Optional[Any]       =	BlipProcessor.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      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.get_image_processor()
       lowerCAmelCase_:							str       =	self.get_tokenizer()

       lowerCAmelCase_:							str       =	BlipProcessor(tokenizer=lowerCamelCase__       ,						image_processor=lowerCamelCase__ )

       lowerCAmelCase_:							int       =	self.prepare_image_inputs()

       lowerCAmelCase_:							int       =	image_processor(lowerCamelCase__       ,						return_tensors="np" )
       lowerCAmelCase_:							Optional[Any]       =	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      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.get_image_processor()
       lowerCAmelCase_:							List[str]       =	self.get_tokenizer()

       lowerCAmelCase_:							Dict       =	BlipProcessor(tokenizer=lowerCamelCase__       ,						image_processor=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[Any]       =	"lower newer"

       lowerCAmelCase_:							Tuple       =	processor(text=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[Any]       =	tokenizer(lowerCamelCase__       ,						return_token_type_ids=lowerCamelCase__ )

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




    def      _a    (    self ):
       lowerCAmelCase_:							Dict       =	self.get_image_processor()
       lowerCAmelCase_:							Union[str, Any]       =	self.get_tokenizer()

       lowerCAmelCase_:							str       =	BlipProcessor(tokenizer=lowerCamelCase__       ,						image_processor=lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	"lower newer"
       lowerCAmelCase_:							List[str]       =	self.prepare_image_inputs()

       lowerCAmelCase_:							Any       =	processor(text=lowerCamelCase__       ,						images=lowerCamelCase__ )

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

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




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.get_image_processor()
       lowerCAmelCase_:							Tuple       =	self.get_tokenizer()

       lowerCAmelCase_:							Optional[int]       =	BlipProcessor(tokenizer=lowerCamelCase__       ,						image_processor=lowerCamelCase__ )

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

       lowerCAmelCase_:							Dict       =	processor.batch_decode(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	tokenizer.batch_decode(lowerCamelCase__ )

       self.assertListEqual(lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.get_image_processor()
       lowerCAmelCase_:							List[Any]       =	self.get_tokenizer()

       lowerCAmelCase_:							List[Any]       =	BlipProcessor(tokenizer=lowerCamelCase__       ,						image_processor=lowerCamelCase__ )

       lowerCAmelCase_:							Union[str, Any]       =	"lower newer"
       lowerCAmelCase_:							Union[str, Any]       =	self.prepare_image_inputs()

       lowerCAmelCase_:							List[str]       =	processor(text=lowerCamelCase__       ,						images=lowerCamelCase__ )

       # 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_tf_available,
    is_torch_available,
    is_vision_available,
)


a  :      List[str]            =    {
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    ["""EfficientFormerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[str]            =    [
     """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """EfficientFormerForImageClassification""",
     """EfficientFormerForImageClassificationWithTeacher""",
     """EfficientFormerModel""",
     """EfficientFormerPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[Any]            =    [
     """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFEfficientFormerForImageClassification""",
     """TFEfficientFormerForImageClassificationWithTeacher""",
     """TFEfficientFormerModel""",
     """TFEfficientFormerPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientformer import EfficientFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
      EfficientFormerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
      TFEfficientFormerPreTrainedModel,
  )

else:
 import sys

 a  :      Tuple            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)