'''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 					__magic_name__		(					unittest.TestCase):
			UpperCamelCase__       = ViTImageProcessor if is_vision_available() else None







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return self.image_processor_tester.prepare_image_processor_dict()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Tuple       		= (3, 32, 128)
										lowercase_      :    Any       		= tempfile.mkdtemp()

										# fmt: off
										lowercase_      :    str       		= ["""[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       		= dict(zip(lowercase_       ,    range(len(lowercase_					)					)					)					)

										lowercase_      :    Optional[int]       		= 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(lowercase_					) + """\n"""					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    **lowercase_       :						Any					):
										return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    **lowercase_       :						Any					):
										return ViTImageProcessor.from_pretrained(self.tmpdirname       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										shutil.rmtree(self.tmpdirname					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										lowercase_      :    int       		= np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta					)

										lowercase_      :    Dict       		= Image.fromarray(np.moveaxis(lowercase_       ,    0       ,    -1					)					)

										return image_input







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										lowercase_      :    Optional[int]       		= self.get_tokenizer()
										lowercase_      :    Optional[int]       		= self.get_image_processor()

										lowercase_      :    Dict       		= MgpstrProcessor(tokenizer=lowercase_       ,    image_processor=lowercase_					)
										processor.save_pretrained(self.tmpdirname					)
										lowercase_      :    Optional[int]       		= MgpstrProcessor.from_pretrained(self.tmpdirname       ,    use_fast=lowercase_					)

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

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    Any       		= self.get_tokenizer()
										lowercase_      :    Optional[int]       		= self.get_image_processor()

										lowercase_      :    int       		= MgpstrProcessor(tokenizer=lowercase_       ,    image_processor=lowercase_					)
										processor.save_pretrained(self.tmpdirname					)

										lowercase_      :    Any       		= self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""					)
										lowercase_      :    Optional[int]       		= self.get_image_processor(do_normalize=lowercase_       ,    padding_value=1.0					)

										lowercase_      :    int       		= MgpstrProcessor.from_pretrained(
										    self.tmpdirname       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    do_normalize=lowercase_       ,    padding_value=1.0					)

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

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Optional[Any]       		= self.get_image_processor()
										lowercase_      :    Tuple       		= self.get_tokenizer()

										lowercase_      :    Dict       		= MgpstrProcessor(tokenizer=lowercase_       ,    image_processor=lowercase_					)

										lowercase_      :    Tuple       		= self.prepare_image_inputs()

										lowercase_      :    List[str]       		= image_processor(lowercase_       ,    return_tensors="""np"""					)
										lowercase_      :    Optional[int]       		= processor(images=lowercase_       ,    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 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Optional[Any]       		= self.get_image_processor()
										lowercase_      :    str       		= self.get_tokenizer()

										lowercase_      :    Tuple       		= MgpstrProcessor(tokenizer=lowercase_       ,    image_processor=lowercase_					)

										lowercase_      :    Any       		= """test"""

										lowercase_      :    str       		= processor(text=lowercase_					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    Union[str, Any]       		= self.get_image_processor()
										lowercase_      :    List[Any]       		= self.get_tokenizer()

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

										lowercase_      :    Optional[Any]       		= """test"""
										lowercase_      :    int       		= self.prepare_image_inputs()

										lowercase_      :    Dict       		= processor(text=lowercase_       ,    images=lowercase_					)

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

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    Tuple       		= self.get_image_processor()
										lowercase_      :    Tuple       		= self.get_tokenizer()

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

										lowercase_      :    str       		= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

										lowercase_      :    List[str]       		= processor.char_decode(lowercase_					)
										lowercase_      :    Optional[int]       		= tokenizer.batch_decode(lowercase_					)
										lowercase_      :    List[str]       		= [seq.replace(""" """       ,    """"""					) for seq in decoded_tok]

										self.assertListEqual(lowercase_       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Optional[Any]       		= self.get_image_processor()
										lowercase_      :    Any       		= self.get_tokenizer()

										lowercase_      :    Dict       		= MgpstrProcessor(tokenizer=lowercase_       ,    image_processor=lowercase_					)

										lowercase_      :    int       		= None
										lowercase_      :    Optional[int]       		= self.prepare_image_inputs()

										lowercase_      :    Any       		= processor(text=lowercase_       ,    images=lowercase_					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Optional[Any]       		= self.get_image_processor()
										lowercase_      :    str       		= self.get_tokenizer()

										lowercase_      :    Any       		= MgpstrProcessor(tokenizer=lowercase_       ,    image_processor=lowercase_					)

										lowercase_      :    Optional[int]       		= torch.randn(1       ,    27       ,    38					)
										lowercase_      :    List[str]       		= torch.randn(1       ,    27       ,    50257					)
										lowercase_      :    List[Any]       		= torch.randn(1       ,    27       ,    30522					)

										lowercase_      :    Dict       		= 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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=_UpperCAmelCase)
class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = field(default='''image-classification''',  metadata={'''include_in_asdict_even_if_is_default''': True})
			UpperCamelCase__       = Features({'''image''': Image()})
			UpperCamelCase__       = Features({'''labels''': ClassLabel})
			UpperCamelCase__       = "image"
			UpperCamelCase__       = "labels"







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						str					):
										if self.label_column not in features:
																	raise ValueError(f'''Column {self.label_column} is not present in features.'''					)
										if not isinstance(features[self.label_column]       ,    lowercase_					):
																	raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''					)
										lowercase_      :    List[str]       		= copy.deepcopy(self					)
										lowercase_      :    List[str]       		= self.label_schema.copy()
										lowercase_      :    List[Any]       		= features[self.label_column]
										lowercase_      :    Optional[Any]       		= label_schema
										return task_template







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}





