





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       __A							(   unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   ViTImageProcessor if is_vision_available() else None


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		(3, 32, 128)
             _lowerCamelCase				:  str						=		tempfile.mkdtemp()

             # fmt: off
             _lowerCamelCase				:  Any						=		['''[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				:  List[str]						=		dict(zip(a__   ,				range(len(a__))))

             _lowerCamelCase				:  Tuple						=		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				:  int						=		{
                 '''do_normalize''': False,
                 '''do_resize''': True,
                 '''image_processor_type''': '''ViTImageProcessor''',
                 '''resample''': 3,
                 '''size''': {'''height''': 32, '''width''': 128},
             }
             _lowerCamelCase				:  Optional[Any]						=		os.path.join(self.tmpdirname   ,				a__)
             with open(self.image_processor_file   ,				'''w'''   ,				encoding='''utf-8''') as fp:
                    json.dump(a__   ,				a__)


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta)

             _lowerCamelCase				:  Any						=		Image.fromarray(np.moveaxis(a__   ,				0   ,				-1))

             return image_input


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.get_tokenizer()
             _lowerCamelCase				:  int						=		self.get_image_processor()

             _lowerCamelCase				:  List[Any]						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)
             processor.save_pretrained(self.tmpdirname)
             _lowerCamelCase				:  Optional[Any]						=		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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.get_tokenizer()
             _lowerCamelCase				:  int						=		self.get_image_processor()

             _lowerCamelCase				:  Optional[Any]						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)
             processor.save_pretrained(self.tmpdirname)

             _lowerCamelCase				:  List[Any]						=		self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)''')
             _lowerCamelCase				:  Any						=		self.get_image_processor(do_normalize=a__   ,				padding_value=1.0)

             _lowerCamelCase				:  List[Any]						=		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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.get_image_processor()
             _lowerCamelCase				:  Union[str, Any]						=		self.get_tokenizer()

             _lowerCamelCase				:  List[str]						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)

             _lowerCamelCase				:  Optional[Any]						=		self.prepare_image_inputs()

             _lowerCamelCase				:  str						=		image_processor(a__   ,				return_tensors='''np''')
             _lowerCamelCase				:  Union[str, Any]						=		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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.get_image_processor()
             _lowerCamelCase				:  int						=		self.get_tokenizer()

             _lowerCamelCase				:  Optional[Any]						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)

             _lowerCamelCase				:  List[str]						=		'''test'''

             _lowerCamelCase				:  Union[str, Any]						=		processor(text=a__)

             _lowerCamelCase				:  Union[str, Any]						=		tokenizer(a__)
             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()

             _lowerCamelCase				:  Optional[int]						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)

             _lowerCamelCase				:  List[Any]						=		'''test'''
             _lowerCamelCase				:  int						=		self.prepare_image_inputs()

             _lowerCamelCase				:  int						=		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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		self.get_image_processor()
             _lowerCamelCase				:  int						=		self.get_tokenizer()

             _lowerCamelCase				:  Any						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)

             _lowerCamelCase				:  List[Any]						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase				:  Optional[Any]						=		processor.char_decode(a__)
             _lowerCamelCase				:  int						=		tokenizer.batch_decode(a__)
             _lowerCamelCase				:  Optional[int]						=		[seq.replace(''' '''   ,				'''''') for seq in decoded_tok]

             self.assertListEqual(a__   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.get_image_processor()
             _lowerCamelCase				:  str						=		self.get_tokenizer()

             _lowerCamelCase				:  Optional[Any]						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)

             _lowerCamelCase				:  Any						=		None
             _lowerCamelCase				:  Union[str, Any]						=		self.prepare_image_inputs()

             _lowerCamelCase				:  Tuple						=		processor(text=a__   ,				images=a__)

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



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.get_image_processor()
             _lowerCamelCase				:  Tuple						=		self.get_tokenizer()

             _lowerCamelCase				:  Tuple						=		MgpstrProcessor(tokenizer=a__   ,				image_processor=a__)

             _lowerCamelCase				:  Optional[Any]						=		torch.randn(1   ,				27   ,				38)
             _lowerCamelCase				:  List[str]						=		torch.randn(1   ,				27   ,				5_0257)
             _lowerCamelCase				:  List[str]						=		torch.randn(1   ,				27   ,				3_0522)

             _lowerCamelCase				:  Union[str, Any]						=		processor.batch_decode([char_input, bpe_input, wp_input])

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








import doctest
from collections import deque

import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		[2, 1, 2, -1]
             _lowerCamelCase				:  Optional[int]						=		[1, 2, 3, 4]



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		len(self.first_signal)
             _lowerCamelCase				:  str						=		len(self.second_signal)

             _lowerCamelCase				:  str						=		max(a__   ,				a__)

             # create a zero matrix of max_length x max_length
             _lowerCamelCase				:  Optional[Any]						=		[[0] * max_length for i in range(a__)]

             # fills the smaller signal with zeros to make both signals of same length
             if length_first_signal < length_second_signal:
                    self.first_signal += [0] * (max_length - length_first_signal)
             elif length_first_signal > length_second_signal:
                    self.second_signal += [0] * (max_length - length_second_signal)
             for i in range(a__):
                    _lowerCamelCase				:  Optional[Any]						=		deque(self.second_signal)
                    rotated_signal.rotate(a__)
                    for j, item in enumerate(a__):
                           matrix[i][j] += item

        # multiply the matrix with the first signal
             _lowerCamelCase				:  List[Any]						=		np.matmul(np.transpose(a__)   ,				np.transpose(self.first_signal))

             # rounding-off to two decimal places
             return [round(a__   ,				2) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()

