"""simple docstring"""
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   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			tempfile.mkdtemp()

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

          snake_case								=			{
              'do_resize': True,
              'size': 20,
              'do_center_crop': True,
              'crop_size': 18,
              'do_normalize': True,
              'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
              'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
          }
          snake_case								=			os.path.join(self.tmpdirname		,    UpperCamelCase__    )
          with open(self.image_processor_file		,    'w'		,    encoding='utf-8'    ) as fp:
                json.dump(UpperCamelCase__		,    UpperCamelCase__    )

    def 		snake_case (				self		,    **lowerCAmelCase    ):






          """simple docstring"""

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

    def 		snake_case (				self		,    **lowerCAmelCase    ):






          """simple docstring"""

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

    def 		snake_case (				self		,    **lowerCAmelCase    ):






          """simple docstring"""

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

    def 		snake_case (				self    ):






          """simple docstring"""

          shutil.rmtree(self.tmpdirname    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			[np.random.randint(2_55		,    size=(3, 30, 4_00)		,    dtype=np.uinta    )]
          snake_case								=			[Image.fromarray(np.moveaxis(UpperCamelCase__		,    0		,    -1    )    ) for x in image_inputs]
          return image_inputs

    def 		snake_case (				self    ):






          """simple docstring"""

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

          snake_case								=			AlignProcessor(tokenizer=UpperCamelCase__		,    image_processor=UpperCamelCase__    )
          processor_slow.save_pretrained(self.tmpdirname    )
          snake_case								=			AlignProcessor.from_pretrained(self.tmpdirname		,    use_fast=UpperCamelCase__    )

          snake_case								=			AlignProcessor(tokenizer=UpperCamelCase__		,    image_processor=UpperCamelCase__    )
          processor_fast.save_pretrained(self.tmpdirname    )
          snake_case								=			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		,    UpperCamelCase__    )
          self.assertIsInstance(processor_fast.tokenizer		,    UpperCamelCase__    )

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

    def 		snake_case (				self    ):






          """simple docstring"""

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

          snake_case								=			self.get_tokenizer(bos_token='(BOS)'		,    eos_token='(EOS)'    )
          snake_case								=			self.get_image_processor(do_normalize=UpperCamelCase__		,    padding_value=1.0    )

          snake_case								=			AlignProcessor.from_pretrained(
              self.tmpdirname		,    bos_token='(BOS)'		,    eos_token='(EOS)'		,    do_normalize=UpperCamelCase__		,    padding_value=1.0    )

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

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

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			AlignProcessor(tokenizer=UpperCamelCase__		,    image_processor=UpperCamelCase__    )

          snake_case								=			self.prepare_image_inputs()

          snake_case								=			image_processor(UpperCamelCase__		,    return_tensors='np'    )
          snake_case								=			processor(images=UpperCamelCase__		,    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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			AlignProcessor(tokenizer=UpperCamelCase__		,    image_processor=UpperCamelCase__    )

          snake_case								=			'lower newer'

          snake_case								=			processor(text=UpperCamelCase__    )

          snake_case								=			tokenizer(UpperCamelCase__		,    padding='max_length'		,    max_length=64    )

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

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			AlignProcessor(tokenizer=UpperCamelCase__		,    image_processor=UpperCamelCase__    )

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

          snake_case								=			processor(text=UpperCamelCase__		,    images=UpperCamelCase__    )

          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(UpperCamelCase__    ):
                processor()

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			AlignProcessor(tokenizer=UpperCamelCase__		,    image_processor=UpperCamelCase__    )

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

          snake_case								=			processor.batch_decode(UpperCamelCase__    )
          snake_case								=			tokenizer.batch_decode(UpperCamelCase__    )

          self.assertListEqual(UpperCamelCase__		,    UpperCamelCase__    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			AlignProcessor(tokenizer=UpperCamelCase__		,    image_processor=UpperCamelCase__    )

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

          snake_case								=			processor(text=UpperCamelCase__		,    images=UpperCamelCase__    )

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



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      						=  {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      						=  [
					    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SEWForCTC",
					    "SEWForSequenceClassification",
					    "SEWModel",
					    "SEWPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew import (
										    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWForCTC,
										    SEWForSequenceClassification,
										    SEWModel,
										    SEWPreTrainedModel,
										)


else:
					import sys

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


