
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   tempfile.mkdtemp()

   lowercase__	: str     		=   [
       """[UNK]""",
       """[CLS]""",
       """[SEP]""",
       """[PAD]""",
       """[MASK]""",
       """want""",
       """##want""",
       """##ed""",
       """wa""",
       """un""",
       """runn""",
       """##ing""",
       """,""",
       """low""",
       """lowest""",
   ]
   lowercase__	: str     		=   os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["""vocab_file"""])
   with open(self.vocab_file       ,	"""w"""       ,	encoding="""utf-8""") as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))

   lowercase__	: Optional[Any]     		=   {
       """do_resize""": True,
       """size""": 20,
       """do_center_crop""": True,
       """crop_size""": 18,
       """do_normalize""": True,
       """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
       """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
   }
   lowercase__	: int     		=   os.path.join(self.tmpdirname       ,	SCREAMING_SNAKE_CASE_)
   with open(self.image_processor_file       ,	"""w"""       ,	encoding="""utf-8""") as fp:
     json.dump(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return BertTokenizer.from_pretrained(self.tmpdirname       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return BertTokenizerFast.from_pretrained(self.tmpdirname       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   shutil.rmtree(self.tmpdirname)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   [np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta)]
   lowercase__	: str     		=   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_       ,	0       ,	-1)) for x in image_inputs]
   return image_inputs


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.get_tokenizer()
   lowercase__	: Optional[int]     		=   self.get_rust_tokenizer()
   lowercase__	: Optional[int]     		=   self.get_image_processor()

   lowercase__	: Optional[int]     		=   AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,	image_processor=SCREAMING_SNAKE_CASE_)
   processor_slow.save_pretrained(self.tmpdirname)
   lowercase__	: List[str]     		=   AlignProcessor.from_pretrained(self.tmpdirname       ,	use_fast=SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,	image_processor=SCREAMING_SNAKE_CASE_)
   processor_fast.save_pretrained(self.tmpdirname)
   lowercase__	: Optional[Any]     		=   AlignProcessor.from_pretrained(self.tmpdirname)

   self.assertEqual(processor_slow.tokenizer.get_vocab()       ,	tokenizer_slow.get_vocab())
   self.assertEqual(processor_fast.tokenizer.get_vocab()       ,	tokenizer_fast.get_vocab())
   self.assertEqual(tokenizer_slow.get_vocab()       ,	tokenizer_fast.get_vocab())
   self.assertIsInstance(processor_slow.tokenizer       ,	SCREAMING_SNAKE_CASE_)
   self.assertIsInstance(processor_fast.tokenizer       ,	SCREAMING_SNAKE_CASE_)

   self.assertEqual(processor_slow.image_processor.to_json_string()       ,	image_processor.to_json_string())
   self.assertEqual(processor_fast.image_processor.to_json_string()       ,	image_processor.to_json_string())
   self.assertIsInstance(processor_slow.image_processor       ,	SCREAMING_SNAKE_CASE_)
   self.assertIsInstance(processor_fast.image_processor       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   AlignProcessor(tokenizer=self.get_tokenizer()       ,	image_processor=self.get_image_processor())
   processor.save_pretrained(self.tmpdirname)

   lowercase__	: int     		=   self.get_tokenizer(bos_token="""(BOS)"""       ,	eos_token="""(EOS)""")
   lowercase__	: Optional[int]     		=   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_       ,	padding_value=1.0)

   lowercase__	: Dict     		=   AlignProcessor.from_pretrained(
       self.tmpdirname       ,	bos_token="""(BOS)"""       ,	eos_token="""(EOS)"""       ,	do_normalize=SCREAMING_SNAKE_CASE_       ,	padding_value=1.0)

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

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


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: Dict     		=   AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,	image_processor=SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   self.prepare_image_inputs()

   lowercase__	: List[str]     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""np""")
   lowercase__	: Any     		=   processor(images=SCREAMING_SNAKE_CASE_       ,	return_tensors="""np""")

   for key in input_image_proc.keys():
     self.assertAlmostEqual(input_image_proc[key].sum()       ,	input_processor[key].sum()       ,	delta=1E-2)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.get_image_processor()
   lowercase__	: List[str]     		=   self.get_tokenizer()

   lowercase__	: Optional[int]     		=   AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,	image_processor=SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   """lower newer"""

   lowercase__	: Dict     		=   processor(text=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   tokenizer(SCREAMING_SNAKE_CASE_       ,	padding="""max_length"""       ,	max_length=64)

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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.get_image_processor()
   lowercase__	: Any     		=   self.get_tokenizer()

   lowercase__	: Tuple     		=   AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,	image_processor=SCREAMING_SNAKE_CASE_)

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

   lowercase__	: List[str]     		=   processor(text=SCREAMING_SNAKE_CASE_       ,	images=SCREAMING_SNAKE_CASE_)

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

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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.get_image_processor()
   lowercase__	: Any     		=   self.get_tokenizer()

   lowercase__	: str     		=   AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,	image_processor=SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   lowercase__	: int     		=   processor.batch_decode(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_)

   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   self.get_image_processor()
   lowercase__	: Optional[int]     		=   self.get_tokenizer()

   lowercase__	: Tuple     		=   AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,	image_processor=SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   """lower newer"""
   lowercase__	: Any     		=   self.prepare_image_inputs()

   lowercase__	: int     		=   processor(text=SCREAMING_SNAKE_CASE_       ,	images=SCREAMING_SNAKE_CASE_)

   self.assertListEqual(list(inputs.keys())       ,	processor.model_input_names)








from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['image_processor', 'tokenizer']
 __lowerCAmelCase	:  Union[str, Any]	   =      'AutoImageProcessor'
 __lowerCAmelCase	:  int	   =      'AutoTokenizer'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.image_processor


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

   if text is not None:
     lowercase__	: List[str]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if images is not None:
     lowercase__	: Optional[int]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if text is not None and images is not None:
     lowercase__	: Union[str, Any]     		=   image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_)       ,	tensor_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ["input_ids", "attention_mask", "pixel_values"]






