


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						):
			UpperCamelCase        =					ViTImageProcessor if is_vision_available() else None



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[int]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






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

						# fmt: off
						_UpperCAmelCase			=       ['[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
						_UpperCAmelCase			=       dict(zip(A			,		range(len(A))))

						_UpperCAmelCase			=       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')

						_UpperCAmelCase			=       {
						    'do_normalize': False,
						    'do_resize': True,
						    'image_processor_type': 'ViTImageProcessor',
						    'resample': 3,
						    'size': {'height': 32, 'width': 1_28},
						}
						_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       :  Union[str, Any]			,		**A       :  Optional[int])       ->   Optional[int]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Any			,		**A       :  List[str])       ->   Dict:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  List[str])       ->   Any:



						"""simple docstring"""






						shutil.rmtree(self.tmpdirname)



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






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

						_UpperCAmelCase			=       Image.fromarray(np.moveaxis(A			,		0			,		-1))

						return image_input



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_tokenizer()
						_UpperCAmelCase			=       self.get_image_processor()

						_UpperCAmelCase			=       MgpstrProcessor(tokenizer=A			,		image_processor=A)
						processor.save_pretrained(self.tmpdirname)
						_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_tokenizer()
						_UpperCAmelCase			=       self.get_image_processor()

						_UpperCAmelCase			=       MgpstrProcessor(tokenizer=A			,		image_processor=A)
						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			=       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    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_image_processor()
						_UpperCAmelCase			=       self.get_tokenizer()

						_UpperCAmelCase			=       MgpstrProcessor(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       :  str)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_image_processor()
						_UpperCAmelCase			=       self.get_tokenizer()

						_UpperCAmelCase			=       MgpstrProcessor(tokenizer=A			,		image_processor=A)

						_UpperCAmelCase			=       'test'

						_UpperCAmelCase			=       processor(text=A)

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



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_image_processor()
						_UpperCAmelCase			=       self.get_tokenizer()

						_UpperCAmelCase			=       MgpstrProcessor(tokenizer=A			,		image_processor=A)

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

						_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  Tuple)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_image_processor()
						_UpperCAmelCase			=       self.get_tokenizer()

						_UpperCAmelCase			=       MgpstrProcessor(tokenizer=A			,		image_processor=A)

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

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

						self.assertListEqual(A			,		A)



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_image_processor()
						_UpperCAmelCase			=       self.get_tokenizer()

						_UpperCAmelCase			=       MgpstrProcessor(tokenizer=A			,		image_processor=A)

						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       self.prepare_image_inputs()

						_UpperCAmelCase			=       processor(text=A			,		images=A)

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



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_image_processor()
						_UpperCAmelCase			=       self.get_tokenizer()

						_UpperCAmelCase			=       MgpstrProcessor(tokenizer=A			,		image_processor=A)

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

						_UpperCAmelCase			=       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 _LazyModule


UpperCAmelCase__       =	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
							from .tokenization_bertweet import BertweetTokenizer

else:
							import sys

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