



"""simple docstring"""







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'''





		lowerCAmelCase			:					List[str]   	=				ViTImageProcessor if is_vision_available() else None







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






				"""simple docstring"""



				return self.image_processor_tester.prepare_image_processor_dict()







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






				"""simple docstring"""



				lowercase__   :    int				=			(3, 32, 128)
				lowercase__   :    List[str]				=			tempfile.mkdtemp()

				# fmt: off
				lowercase__   :    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
				lowercase__   :    Tuple				=			dict(zip(_snake_case			,range(len(_snake_case						)						)						)						)

				lowercase__   :    Optional[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(_snake_case						) + '''\n'''						)

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







		def 					UpperCAmelCase    (	self						:  Any			,**_snake_case						:  Dict						)					->		List[str]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Any			,**_snake_case						:  List[Any]						)					->		Dict:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				shutil.rmtree(self.tmpdirname						)







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






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			np.random.randint(255			,size=(3, 30, 400)			,dtype=np.uinta						)

				lowercase__   :    Any				=			Image.fromarray(np.moveaxis(_snake_case			,0			,-1						)						)

				return image_input







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






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.get_tokenizer()
				lowercase__   :    Union[str, Any]				=			self.get_image_processor()

				lowercase__   :    int				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)
				processor.save_pretrained(self.tmpdirname						)
				lowercase__   :    Dict				=			MgpstrProcessor.from_pretrained(self.tmpdirname			,use_fast=_snake_case						)

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

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







		def 					UpperCAmelCase    (	self						:  int						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.get_tokenizer()
				lowercase__   :    Dict				=			self.get_image_processor()

				lowercase__   :    List[str]				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)
				processor.save_pretrained(self.tmpdirname						)

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

				lowercase__   :    Dict				=			MgpstrProcessor.from_pretrained(
				    self.tmpdirname			,bos_token='''(BOS)'''			,eos_token='''(EOS)'''			,do_normalize=_snake_case			,padding_value=1.0						)

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

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







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.get_image_processor()
				lowercase__   :    Dict				=			self.get_tokenizer()

				lowercase__   :    List[Any]				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)

				lowercase__   :    Tuple				=			self.prepare_image_inputs()

				lowercase__   :    Any				=			image_processor(_snake_case			,return_tensors='''np'''						)
				lowercase__   :    str				=			processor(images=_snake_case			,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 					UpperCAmelCase    (	self						:  List[str]						)					->		Any:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.get_image_processor()
				lowercase__   :    Any				=			self.get_tokenizer()

				lowercase__   :    str				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)

				lowercase__   :    Tuple				=			'''test'''

				lowercase__   :    str				=			processor(text=_snake_case						)

				lowercase__   :    List[Any]				=			tokenizer(_snake_case						)
				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]			,encoded_processor[key]						)







		def 					UpperCAmelCase    (	self						:  int						)					->		Any:






				"""simple docstring"""



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

				lowercase__   :    Union[str, Any]				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)

				lowercase__   :    Union[str, Any]				=			'''test'''
				lowercase__   :    List[Any]				=			self.prepare_image_inputs()

				lowercase__   :    Tuple				=			processor(text=_snake_case			,images=_snake_case						)

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

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







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.get_image_processor()
				lowercase__   :    Dict				=			self.get_tokenizer()

				lowercase__   :    Union[str, Any]				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)

				lowercase__   :    Union[str, Any]				=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

				lowercase__   :    List[Any]				=			processor.char_decode(_snake_case						)
				lowercase__   :    Dict				=			tokenizer.batch_decode(_snake_case						)
				lowercase__   :    Optional[int]				=			[seq.replace(''' '''			,''''''						) for seq in decoded_tok]

				self.assertListEqual(_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



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

				lowercase__   :    int				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)

				lowercase__   :    List[Any]				=			None
				lowercase__   :    List[Any]				=			self.prepare_image_inputs()

				lowercase__   :    Tuple				=			processor(text=_snake_case			,images=_snake_case						)

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







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






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.get_image_processor()
				lowercase__   :    Tuple				=			self.get_tokenizer()

				lowercase__   :    Any				=			MgpstrProcessor(tokenizer=_snake_case			,image_processor=_snake_case						)

				lowercase__   :    Tuple				=			torch.randn(1			,27			,38						)
				lowercase__   :    int				=			torch.randn(1			,27			,50_257						)
				lowercase__   :    List[Any]				=			torch.randn(1			,27			,30_522						)

				lowercase__   :    Any				=			processor.batch_decode([char_input, bpe_input, wp_input]						)

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





"""simple docstring"""







import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

		lowercase__   :    List[Any]				=			ArgumentParser()
		parser.add_argument('''--streaming'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--local_rank'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--num_workers'''						,     type=__lowerCamelCase						,     default=0	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Optional[Any]				=			args.streaming
		lowercase__   :    List[Any]				=			args.num_workers

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowercase__   :    str				=			full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowercase__   :    str				=			sum(1 for _ in dataloader	)
		if local_size != expected_local_size:
				raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()
