



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
     import torch


if is_vision_available():
     from PIL import Image

     from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     _A       =			ViTImageProcessor if is_vision_available() else None







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.image_processor_tester.prepare_image_processor_dict()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		(3, 32, 1_28)
           lowerCamelCase								=		tempfile.mkdtemp()

           # fmt: off
           lowerCamelCase								=		['''[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								=		dict(zip(__a				,			range(len(__a    )    )    )    )

           lowerCamelCase								=		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(__a    ) + "\n"    )

           lowerCamelCase								=		{
               '''do_normalize''': False,
               '''do_resize''': True,
               '''image_processor_type''': '''ViTImageProcessor''',
               '''resample''': 3,
               '''size''': {'''height''': 32, '''width''': 1_28},
           }
           lowerCamelCase								=		os.path.join(self.tmpdirname				,			__a    )
           with open(self.image_processor_file				,			"w"				,			encoding="utf-8"    ) as fp:
                 json.dump(__a				,			__a    )







     def 						_a      (self				,			**__a    ):






           '''simple docstring'''



           return MgpstrTokenizer.from_pretrained(self.tmpdirname				,			**__a    )







     def 						_a      (self				,			**__a    ):






           '''simple docstring'''



           return ViTImageProcessor.from_pretrained(self.tmpdirname				,			**__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           shutil.rmtree(self.tmpdirname    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		np.random.randint(2_55				,			size=(3, 30, 4_00)				,			dtype=np.uinta    )

           lowerCamelCase								=		Image.fromarray(np.moveaxis(__a				,			0				,			-1    )    )

           return image_input







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_tokenizer()
           lowerCamelCase								=		self.get_image_processor()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )
           processor.save_pretrained(self.tmpdirname    )
           lowerCamelCase								=		MgpstrProcessor.from_pretrained(self.tmpdirname				,			use_fast=__a    )

           self.assertEqual(processor.char_tokenizer.get_vocab()				,			tokenizer.get_vocab()    )
           self.assertIsInstance(processor.char_tokenizer				,			__a    )

           self.assertEqual(processor.image_processor.to_json_string()				,			image_processor.to_json_string()    )
           self.assertIsInstance(processor.image_processor				,			__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_tokenizer()
           lowerCamelCase								=		self.get_image_processor()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )
           processor.save_pretrained(self.tmpdirname    )

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

           lowerCamelCase								=		MgpstrProcessor.from_pretrained(
               self.tmpdirname				,			bos_token="(BOS)"				,			eos_token="(EOS)"				,			do_normalize=__a				,			padding_value=1.0    )

           self.assertEqual(processor.char_tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()    )
           self.assertIsInstance(processor.char_tokenizer				,			__a    )

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_image_processor()
           lowerCamelCase								=		self.get_tokenizer()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )

           lowerCamelCase								=		self.prepare_image_inputs()

           lowerCamelCase								=		image_processor(__a				,			return_tensors="np"    )
           lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_image_processor()
           lowerCamelCase								=		self.get_tokenizer()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )

           lowerCamelCase								=		'''test'''

           lowerCamelCase								=		processor(text=__a    )

           lowerCamelCase								=		tokenizer(__a    )
           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_image_processor()
           lowerCamelCase								=		self.get_tokenizer()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )

           lowerCamelCase								=		'''test'''
           lowerCamelCase								=		self.prepare_image_inputs()

           lowerCamelCase								=		processor(text=__a				,			images=__a    )

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

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_image_processor()
           lowerCamelCase								=		self.get_tokenizer()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )

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

           lowerCamelCase								=		processor.char_decode(__a    )
           lowerCamelCase								=		tokenizer.batch_decode(__a    )
           lowerCamelCase								=		[seq.replace(" "				,			""    ) for seq in decoded_tok]

           self.assertListEqual(__a				,			__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_image_processor()
           lowerCamelCase								=		self.get_tokenizer()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )

           lowerCamelCase								=		None
           lowerCamelCase								=		self.prepare_image_inputs()

           lowerCamelCase								=		processor(text=__a				,			images=__a    )

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.get_image_processor()
           lowerCamelCase								=		self.get_tokenizer()

           lowerCamelCase								=		MgpstrProcessor(tokenizer=__a				,			image_processor=__a    )

           lowerCamelCase								=		torch.randn(1				,			27				,			38    )
           lowerCamelCase								=		torch.randn(1				,			27				,			5_02_57    )
           lowerCamelCase								=		torch.randn(1				,			27				,			3_05_22    )

           lowerCamelCase								=		processor.batch_decode([char_input, bpe_input, wp_input]    )

           self.assertListEqual(list(results.keys()    )				,			["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     List[str]	     =		{
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Any	     =		[
         'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CLIPSegModel',
         'CLIPSegPreTrainedModel',
         'CLIPSegTextModel',
         'CLIPSegVisionModel',
         'CLIPSegForImageSegmentation',
     ]

if TYPE_CHECKING:
     from .configuration_clipseg import (
         CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPSegConfig,
         CLIPSegTextConfig,
         CLIPSegVisionConfig,
     )
     from .processing_clipseg import CLIPSegProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clipseg import (
              CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPSegForImageSegmentation,
              CLIPSegModel,
              CLIPSegPreTrainedModel,
              CLIPSegTextModel,
              CLIPSegVisionModel,
          )

else:
     import sys

     a_						:     Dict	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)