"""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  UpperCamelCase					(				unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      ViTImageProcessor if is_vision_available() else None







       @property
       def 		a_						( self)      ->      Union[str, Any]:
            return self.image_processor_tester.prepare_image_processor_dict()







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					(3, 32, 128)
            snake_case_					       =					tempfile.mkdtemp()

            # fmt: off
            snake_case_					       =					['[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
            snake_case_					       =					dict(zip(lowerCAmelCase__,							range(len(lowerCAmelCase__))))

            snake_case_					       =					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(lowerCAmelCase__) + '\n')

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







       def 		a_						( self,							**lowerCAmelCase__)      ->      Union[str, Any]:
            return MgpstrTokenizer.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							**lowerCAmelCase__)      ->      Union[str, Any]:
            return ViTImageProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self)      ->      Tuple:
            shutil.rmtree(self.tmpdirname)







       def 		a_						( self)      ->      Any:
            snake_case_					       =					np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta)

            snake_case_					       =					Image.fromarray(np.moveaxis(lowerCAmelCase__,							0,							-1))

            return image_input







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_image_processor()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)
            processor.save_pretrained(self.tmpdirname)
            snake_case_					       =					MgpstrProcessor.from_pretrained(self.tmpdirname,							use_fast=lowerCAmelCase__)

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

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







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_image_processor()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)
            processor.save_pretrained(self.tmpdirname)

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

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

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

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







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					image_processor(lowerCAmelCase__,							return_tensors='np')
            snake_case_					       =					processor(images=lowerCAmelCase__,							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)      ->      str:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					'test'

            snake_case_					       =					processor(text=lowerCAmelCase__)

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







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

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

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

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

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







       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

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

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

            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					None
            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

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







       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					MgpstrProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					torch.randn(1,							27,							38)
            snake_case_					       =					torch.randn(1,							27,							5_0257)
            snake_case_					       =					torch.randn(1,							27,							3_0522)

            snake_case_					       =					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 pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs


@pytest.mark.parametrize(
    """kwargs, expected"""    ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10  )]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(_UpperCAmelCase    ,i + 1  ) for i in range(10  )]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1  )]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0    ,4  ), range(4    ,7  ), range(7    ,10  )]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0    ,1  ), range(1    ,2  ), range(2    ,3  )]),
    ]    ,)
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	Dict  )  ->			Optional[Any]:
       _a    : Tuple     					=_distribute_shards(**_UpperCAmelCase  )
       assert out == expected


@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""    ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]    ,)
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	Union[str, Any]  )  ->			List[str]:
       _a    : List[str]     					=_split_gen_kwargs(_UpperCAmelCase    ,_UpperCAmelCase  )
       assert out == expected


@pytest.mark.parametrize(
    """gen_kwargs, expected"""    ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]    ,)
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	List[Any]  )  ->			Union[str, Any]:
       if expected is RuntimeError:
              with pytest.raises(_UpperCAmelCase  ):
                     _number_of_shards_in_gen_kwargs(_UpperCAmelCase  )
       else:
              _a    : Dict     					=_number_of_shards_in_gen_kwargs(_UpperCAmelCase  )
              assert out == expected


