


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'''







					__UpperCAmelCase            =				ViTImageProcessor if is_vision_available() else None






					@property
					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   (3, 3_2, 1_2_8)
								__lowercase 					=   tempfile.mkdtemp()

								# fmt: off
								__lowercase 					=   ['''[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
								__lowercase 					=   dict(zip(UpperCamelCase_ ,       range(len(UpperCamelCase_						)						)						)						)

								__lowercase 					=   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(UpperCamelCase_						) + '''\n'''						)

								__lowercase 					=   {
								    '''do_normalize''': False,
								    '''do_resize''': True,
								    '''image_processor_type''': '''ViTImageProcessor''',
								    '''resample''': 3,
								    '''size''': {'''height''': 3_2, '''width''': 1_2_8},
								}
								__lowercase 					=   os.path.join(self.tmpdirname ,       UpperCamelCase_						)
								with open(self.image_processor_file ,       '''w''' ,       encoding='''utf-8'''						) as fp:
											json.dump(UpperCamelCase_ ,       UpperCamelCase_						)






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

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






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

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






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







								'''simple docstring'''

								shutil.rmtree(self.tmpdirname						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   np.random.randint(2_5_5 ,       size=(3, 3_0, 4_0_0) ,       dtype=np.uinta						)

								__lowercase 					=   Image.fromarray(np.moveaxis(UpperCamelCase_ ,       0 ,       -1						)						)

								return image_input






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







								'''simple docstring'''

								__lowercase 					=   self.get_tokenizer()
								__lowercase 					=   self.get_image_processor()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)
								processor.save_pretrained(self.tmpdirname						)
								__lowercase 					=   MgpstrProcessor.from_pretrained(self.tmpdirname ,       use_fast=UpperCamelCase_						)

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

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






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







								'''simple docstring'''

								__lowercase 					=   self.get_tokenizer()
								__lowercase 					=   self.get_image_processor()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)
								processor.save_pretrained(self.tmpdirname						)

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

								__lowercase 					=   MgpstrProcessor.from_pretrained(
								    self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=UpperCamelCase_ ,       padding_value=1.0						)

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

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






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.get_image_processor()
								__lowercase 					=   self.get_tokenizer()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)

								__lowercase 					=   self.prepare_image_inputs()

								__lowercase 					=   image_processor(UpperCamelCase_ ,       return_tensors='''np'''						)
								__lowercase 					=   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 A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   self.get_image_processor()
								__lowercase 					=   self.get_tokenizer()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)

								__lowercase 					=   '''test'''

								__lowercase 					=   processor(text=UpperCamelCase_						)

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






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







								'''simple docstring'''

								__lowercase 					=   self.get_image_processor()
								__lowercase 					=   self.get_tokenizer()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)

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

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

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

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






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.get_image_processor()
								__lowercase 					=   self.get_tokenizer()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)

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

								__lowercase 					=   processor.char_decode(UpperCamelCase_						)
								__lowercase 					=   tokenizer.batch_decode(UpperCamelCase_						)
								__lowercase 					=   [seq.replace(''' ''' ,       ''''''						) for seq in decoded_tok]

								self.assertListEqual(UpperCamelCase_ ,       UpperCamelCase_						)






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







								'''simple docstring'''

								__lowercase 					=   self.get_image_processor()
								__lowercase 					=   self.get_tokenizer()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)

								__lowercase 					=   None
								__lowercase 					=   self.prepare_image_inputs()

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

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






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







								'''simple docstring'''

								__lowercase 					=   self.get_image_processor()
								__lowercase 					=   self.get_tokenizer()

								__lowercase 					=   MgpstrProcessor(tokenizer=UpperCamelCase_ ,       image_processor=UpperCamelCase_						)

								__lowercase 					=   torch.randn(1 ,       2_7 ,       3_8						)
								__lowercase 					=   torch.randn(1 ,       2_7 ,       5_0_2_5_7						)
								__lowercase 					=   torch.randn(1 ,       2_7 ,       3_0_5_2_2						)

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

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






from ..utils import DummyObject, requires_backends
class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)




class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Tuple:







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)


