

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class       __A (							UpperCamelCase__  ,  unittest.TestCase       ):
    lowerCAmelCase_							:						Optional[Any]						 =							MgpstrTokenizer
    lowerCAmelCase_							:						List[Any]						 =							False
    lowerCAmelCase_							:						List[str]						 =							{}
    lowerCAmelCase_							:						List[Any]						 =							False






    def 		lowercase__       (		self	:			Union[str, Any]			):
        super().setUp()

        # fmt: off
        lowerCAmelCase :	List[str]				=					["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
        # fmt: on
        lowerCAmelCase :	Tuple				=					dict(zip(lowerCAmelCase__			, range(len(lowerCAmelCase__			)			)			)			)

        lowerCAmelCase :	Union[str, Any]				=					os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES['vocab_file']			)
        with open(self.vocab_file			, 'w'			, encoding='utf-8'			) as fp:
            fp.write(json.dumps(lowerCAmelCase__			) + '\n'			)






    def 		lowercase__       (		self	:			Tuple			, **UpperCAmelCase_	:			List[str]			):
        return MgpstrTokenizer.from_pretrained(self.tmpdirname			, **lowerCAmelCase__			)






    def 		lowercase__       (		self	:			Tuple			, UpperCAmelCase_	:			Union[str, Any]			):
        lowerCAmelCase :	Any				=					"tester"
        lowerCAmelCase :	Dict				=					"tester"
        return input_text, output_text






    @unittest.skip('MGP-STR always lower cases letters.'			)
    def 		lowercase__       (		self	:			Dict			):
        pass






    def 		lowercase__       (		self	:			Optional[int]			):
        lowerCAmelCase :	Dict				=					self.get_tokenizers(do_lower_case=lowerCAmelCase__			)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"			):
                lowerCAmelCase :	Dict				=					"[SPECIAL_TOKEN]"

                tokenizer.add_special_tokens({'cls_token': special_token}			)
                lowerCAmelCase :	Optional[int]				=					tokenizer.encode([special_token]			, add_special_tokens=lowerCAmelCase__			)
                self.assertEqual(len(lowerCAmelCase__			)			, 1			)

                lowerCAmelCase :	Optional[int]				=					tokenizer.decode(lowerCAmelCase__			, skip_special_tokens=lowerCAmelCase__			)
                self.assertTrue(special_token not in decoded			)






    def 		lowercase__       (		self	:			Dict			):
        lowerCAmelCase :	List[str]				=					self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"			):
                lowerCAmelCase :	Optional[int]				=					self.get_input_output_texts(lowerCAmelCase__			)

                lowerCAmelCase :	int				=					tokenizer.tokenize(lowerCAmelCase__			)
                lowerCAmelCase :	Any				=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__			)
                lowerCAmelCase :	Dict				=					tokenizer.encode(lowerCAmelCase__			, add_special_tokens=lowerCAmelCase__			)
                self.assertListEqual(lowerCAmelCase__			, lowerCAmelCase__			)

                lowerCAmelCase :	List[Any]				=					tokenizer.convert_ids_to_tokens(lowerCAmelCase__			)
                self.assertNotEqual(len(lowerCAmelCase__			)			, 0			)
                lowerCAmelCase :	Optional[Any]				=					tokenizer.decode(lowerCAmelCase__			)
                self.assertIsInstance(lowerCAmelCase__			, lowerCAmelCase__			)

                self.assertEqual(text_a.replace(' '			, ''			)			, lowerCAmelCase__			)






    @unittest.skip('MGP-STR tokenizer only handles one sequence.'			)
    def 		lowercase__       (		self	:			Tuple			):
        pass






    @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'			)
    def 		lowercase__       (		self	:			Tuple			):
        pass












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor







class 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      )   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					List[str]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Union[str, Any]										=batch_size
        a__	:					List[str]										=num_channels
        a__	:					List[Any]										=image_size
        a__	:					str										=min_resolution
        a__	:					Optional[int]										=max_resolution
        a__	:					Tuple										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					List[Any]										=do_center_crop
        a__	:					List[str]										=crop_size
        a__	:					Optional[int]										=do_flip_channel_order







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_flip_channel_order": self.do_flip_channel_order,
        }







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					int						=			MobileViTImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Tuple										=MobileViTImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_flip_channel_order"							)							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					List[Any]										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 2_0}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Union[str, Any]										=self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2		,      crop_size=8_4							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 8_4, "width": 8_4}							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					Any										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      Image.Image							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					List[Any]										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      numpify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      np.ndarray							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					int										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					int										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					Tuple										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      torchify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      torch.Tensor							)

        # Test not batched input
        a__	:					List[str]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[str]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )






