
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 						lowercase_				(				unittest.TestCase ):

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				tempfile.mkdtemp()

        UpperCamelCase_											=				[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        UpperCamelCase_											=				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]   )   )

        UpperCamelCase_											=				{
            'do_resize': True,
            'size': 2_0,
            'do_center_crop': True,
            'crop_size': 1_8,
            'do_normalize': True,
            'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
            'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
        }
        UpperCamelCase_											=				os.path.join(self.tmpdirname   ,		_a   )
        with open(self.image_processor_file   ,		"""w"""   ,		encoding="""utf-8"""   ) as fp:
               json.dump(_a   ,		_a   )

 def lowerCamelCase_ (					self   ,		**__UpperCamelCase   ):




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		**__UpperCamelCase   ):




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		**__UpperCamelCase   ):




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        shutil.rmtree(self.tmpdirname   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				[np.random.randint(2_5_5   ,		size=(3, 3_0, 4_0_0)   ,		dtype=np.uinta   )]
        UpperCamelCase_											=				[Image.fromarray(np.moveaxis(_a   ,		0   ,		-1   )   ) for x in image_inputs]
        return image_inputs

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_tokenizer()
        UpperCamelCase_											=				self.get_rust_tokenizer()
        UpperCamelCase_											=				self.get_image_processor()

        UpperCamelCase_											=				AlignProcessor(tokenizer=_a   ,		image_processor=_a   )
        processor_slow.save_pretrained(self.tmpdirname   )
        UpperCamelCase_											=				AlignProcessor.from_pretrained(self.tmpdirname   ,		use_fast=_a   )

        UpperCamelCase_											=				AlignProcessor(tokenizer=_a   ,		image_processor=_a   )
        processor_fast.save_pretrained(self.tmpdirname   )
        UpperCamelCase_											=				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   ,		_a   )
        self.assertIsInstance(processor_fast.tokenizer   ,		_a   )

        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   ,		_a   )
        self.assertIsInstance(processor_fast.image_processor   ,		_a   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				AlignProcessor(tokenizer=self.get_tokenizer()   ,		image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        UpperCamelCase_											=				self.get_tokenizer(bos_token="""(BOS)"""   ,		eos_token="""(EOS)"""   )
        UpperCamelCase_											=				self.get_image_processor(do_normalize=_a   ,		padding_value=1.0   )

        UpperCamelCase_											=				AlignProcessor.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 lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_image_processor()
        UpperCamelCase_											=				self.get_tokenizer()

        UpperCamelCase_											=				AlignProcessor(tokenizer=_a   ,		image_processor=_a   )

        UpperCamelCase_											=				self.prepare_image_inputs()

        UpperCamelCase_											=				image_processor(_a   ,		return_tensors="""np"""   )
        UpperCamelCase_											=				processor(images=_a   ,		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 lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_image_processor()
        UpperCamelCase_											=				self.get_tokenizer()

        UpperCamelCase_											=				AlignProcessor(tokenizer=_a   ,		image_processor=_a   )

        UpperCamelCase_											=				'lower newer'

        UpperCamelCase_											=				processor(text=_a   )

        UpperCamelCase_											=				tokenizer(_a   ,		padding="""max_length"""   ,		max_length=6_4   )

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_image_processor()
        UpperCamelCase_											=				self.get_tokenizer()

        UpperCamelCase_											=				AlignProcessor(tokenizer=_a   ,		image_processor=_a   )

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

        UpperCamelCase_											=				processor(text=_a   ,		images=_a   )

        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(_a   ):
               processor()

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_image_processor()
        UpperCamelCase_											=				self.get_tokenizer()

        UpperCamelCase_											=				AlignProcessor(tokenizer=_a   ,		image_processor=_a   )

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

        UpperCamelCase_											=				processor.batch_decode(_a   )
        UpperCamelCase_											=				tokenizer.batch_decode(_a   )

        self.assertListEqual(_a   ,		_a   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_image_processor()
        UpperCamelCase_											=				self.get_tokenizer()

        UpperCamelCase_											=				AlignProcessor(tokenizer=_a   ,		image_processor=_a   )

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

        UpperCamelCase_											=				processor(text=_a   ,		images=_a   )

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





'''simple docstring'''







import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__lowerCAmelCase         =    logging.getLogger()







@unittest.skip('''Temporarily disable the doc tests.'''	)
@require_torch
@require_tf
@slow
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     str							,_a      :     Path							,_a      :     Union[str, None] = None							,_a      :     Union[List[str], None] = None							,_a      :     Union[str, List[str], None] = None							,_a      :     bool = True							,):


     '''simple docstring'''






     _a				:	Optional[int]			=   [file for file in os.listdir(_a       ) if os.path.isfile(os.path.join(_a							,_a       )       )]

     if identifier is not None:
        _a				:	List[str]			=   [file for file in files if identifier in file]

     if n_identifier is not None:
        if isinstance(_a							,_a       ):
           for n_ in n_identifier:
              _a				:	Tuple			=   [file for file in files if n_ not in file]
        else:
           _a				:	Optional[Any]			=   [file for file in files if n_identifier not in file]

     _a				:	List[str]			=   ignore_files or []
     ignore_files.append('__init__.py'       )
     _a				:	Tuple			=   [file for file in files if file not in ignore_files]

     for file in files:
        # Open all files
        print('Testing'							,_a       )

        if only_modules:
           _a				:	Any			=   file.split('.'       )[0]
           try:
              _a				:	List[str]			=   getattr(_a							,_a       )
              _a				:	int			=   doctest.DocTestSuite(_a       )
              _a				:	Any			=   unittest.TextTestRunner().run(_a       )
              self.assertIs(len(result.failures       )							,0       )
           except AttributeError:
              logger.info(F"""{module_identifier} is not a module."""       )
        else:
           _a				:	Union[str, Any]			=   doctest.testfile(str('..' / directory / file       )							,optionflags=doctest.ELLIPSIS       )
           self.assertIs(result.failed							,0       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	int			=   Path('src/transformers'       )
     _a				:	List[Any]			=   'modeling'
     _a				:	Optional[Any]			=   [
         'modeling_ctrl.py',
         'modeling_tf_ctrl.py',
     ]
     self.analyze_directory(_a							,identifier=_a							,ignore_files=_a       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   Path('src/transformers'       )
     _a				:	Optional[Any]			=   'tokenization'
     self.analyze_directory(_a							,identifier=_a       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Dict			=   Path('src/transformers'       )
     _a				:	str			=   'configuration'
     self.analyze_directory(_a							,identifier=_a       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	Tuple			=   Path('src/transformers'       )
     _a				:	List[Any]			=   ['configuration', 'modeling', 'tokenization']
     self.analyze_directory(_a							,n_identifier=_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[Any]			=   Path('docs/source'       )
     _a				:	List[str]			=   ['favicon.ico']
     self.analyze_directory(_a							,ignore_files=_a							,only_modules=_a       )



