





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


__lowerCamelCase : str											=			logging.get_logger(__name__)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if isinstance(lowerCAmelCase_		,							(list, tuple)							) and isinstance(videos[0]		,							(list, tuple)							) and is_valid_image(videos[0][0]							):
								return videos

				elif isinstance(lowerCAmelCase_		,							(list, tuple)							) and is_valid_image(videos[0]							):
								return [videos]

				elif is_valid_image(lowerCAmelCase_							):
								return [[videos]]

				raise ValueError(f'Could not make batched video from {videos}'							)


class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	int 			=		['''pixel_values''']




		def __init__(self      :	Dict							,			A__      :	bool = True							,			A__      :	Dict[str, int] = None							,			A__      :	PILImageResampling = PILImageResampling.BILINEAR							,			A__      :	bool = True							,			A__      :	Dict[str, int] = None							,			A__      :	bool = True							,			A__      :	Union[int, float] = 1 / 2_5_5							,			A__      :	bool = True							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			**A__      :	Any							,			)					-> None:
						super().__init__(**A__				)
						lowercase         = size if size is not None else {"shortest_edge": 2_2_4}
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)
						lowercase         = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						lowercase         = get_size_dict(A__							,			param_name="crop_size"				)

						lowercase         = do_resize
						lowercase         = size
						lowercase         = do_center_crop
						lowercase         = crop_size
						lowercase         = resample
						lowercase         = do_rescale
						lowercase         = rescale_factor
						lowercase         = do_normalize
						lowercase         = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowercase         = image_std if image_std is not None else IMAGENET_STANDARD_STD




		def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	np.ndarray							,			A__      :	Dict[str, int]							,			A__      :	PILImageResampling = PILImageResampling.BILINEAR							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Optional[int]							,			)					-> np.ndarray:
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)
						if "shortest_edge" in size:
										lowercase         = get_resize_output_image_size(A__							,			size["shortest_edge"]							,			default_to_square=A__				)
						elif "height" in size and "width" in size:
										lowercase         = (size["height"], size["width"])
						else:
										raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'				)
						return resize(A__							,			size=A__							,			resample=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	np.ndarray							,			A__      :	Dict[str, int]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	str							,			)					-> np.ndarray:
						lowercase         = get_size_dict(A__				)
						if "height" not in size or "width" not in size:
										raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'				)
						return center_crop(A__							,			size=(size["height"], size["width"])							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	np.ndarray							,			A__      :	Union[int, float]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Tuple							,			)					-> Optional[int]:
						return rescale(A__							,			scale=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	np.ndarray							,			A__      :	Union[float, List[float]]							,			A__      :	Union[float, List[float]]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	List[Any]							,			)					-> np.ndarray:
						return normalize(A__							,			mean=A__							,			std=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	ImageInput							,			A__      :	bool = None							,			A__      :	Dict[str, int] = None							,			A__      :	PILImageResampling = None							,			A__      :	bool = None							,			A__      :	Dict[str, int] = None							,			A__      :	bool = None							,			A__      :	float = None							,			A__      :	bool = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[ChannelDimension] = ChannelDimension.FIRST							,			)					-> np.ndarray:
						if do_resize and size is None or resample is None:
										raise ValueError("Size and resample must be specified if do_resize is True."				)

						if do_center_crop and crop_size is None:
										raise ValueError("Crop size must be specified if do_center_crop is True."				)

						if do_rescale and rescale_factor is None:
										raise ValueError("Rescale factor must be specified if do_rescale is True."				)

						if do_normalize and (image_mean is None or image_std is None):
										raise ValueError("Image mean and std must be specified if do_normalize is True."				)

						# All transformations expect numpy arrays.
						lowercase         = to_numpy_array(A__				)

						if do_resize:
										lowercase         = self.resize(image=A__							,			size=A__							,			resample=A__				)

						if do_center_crop:
										lowercase         = self.center_crop(A__							,			size=A__				)

						if do_rescale:
										lowercase         = self.rescale(image=A__							,			scale=A__				)

						if do_normalize:
										lowercase         = self.normalize(image=A__							,			mean=A__							,			std=A__				)

						lowercase         = to_channel_dimension_format(A__							,			A__				)
						return image





		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	ImageInput							,			A__      :	bool = None							,			A__      :	Dict[str, int] = None							,			A__      :	PILImageResampling = None							,			A__      :	bool = None							,			A__      :	Dict[str, int] = None							,			A__      :	bool = None							,			A__      :	float = None							,			A__      :	bool = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[str, TensorType]] = None							,			A__      :	ChannelDimension = ChannelDimension.FIRST							,			**A__      :	Optional[Any]							,			)					-> PIL.Image.Image:
						lowercase         = do_resize if do_resize is not None else self.do_resize
						lowercase         = resample if resample is not None else self.resample
						lowercase         = do_center_crop if do_center_crop is not None else self.do_center_crop
						lowercase         = do_rescale if do_rescale is not None else self.do_rescale
						lowercase         = rescale_factor if rescale_factor is not None else self.rescale_factor
						lowercase         = do_normalize if do_normalize is not None else self.do_normalize
						lowercase         = image_mean if image_mean is not None else self.image_mean
						lowercase         = image_std if image_std is not None else self.image_std

						lowercase         = size if size is not None else self.size
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)
						lowercase         = crop_size if crop_size is not None else self.crop_size
						lowercase         = get_size_dict(A__							,			param_name="crop_size"				)

						if not valid_images(A__				):
										raise ValueError(
										    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
										    "torch.Tensor, tf.Tensor or jax.ndarray."				)

						lowercase         = make_batched(A__				)

						lowercase         = [
						    [
						        self._preprocess_image(
						            image=A__							,			do_resize=A__							,			size=A__							,			resample=A__							,			do_center_crop=A__							,			crop_size=A__							,			do_rescale=A__							,			rescale_factor=A__							,			do_normalize=A__							,			image_mean=A__							,			image_std=A__							,			data_format=A__							,			)
						        for img in video
						    ]
						    for video in videos
						]

						lowercase         = {"pixel_values": videos}
						return BatchFeature(data=A__							,			tensor_type=A__				)











'''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  ):
		UpperCAmelCase					:	Optional[int] 			=		ViTImageProcessor if is_vision_available() else None




		@property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = (3, 3_2, 1_2_8)
						lowercase         = tempfile.mkdtemp()

						# fmt: off
						lowercase         = ["[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(zip(A__							,			range(len(A__				)				)				)				)

						lowercase         = 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"				)

						lowercase         = {
						    "do_normalize": False,
						    "do_resize": True,
						    "image_processor_type": "ViTImageProcessor",
						    "resample": 3,
						    "size": {"height": 3_2, "width": 1_2_8},
						}
						lowercase         = os.path.join(self.tmpdirname							,			A__				)
						with open(self.image_processor_file							,			"w"							,			encoding="utf-8"				) as fp:
										json.dump(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Dict							,			**A__      :	List[Any]				)					-> Union[str, Any]:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			**A__      :	Tuple				)					-> Tuple:
						return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> int:
						shutil.rmtree(self.tmpdirname				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta				)

						lowercase         = Image.fromarray(np.moveaxis(A__							,			0							,			-1				)				)

						return image_input




		def 				UpperCAmelCase__   (self      :	str				)					-> List[Any]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)
						lowercase         = 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 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)

						lowercase         = self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"				)
						lowercase         = self.get_image_processor(do_normalize=A__							,			padding_value=1.0				)

						lowercase         = 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 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = self.prepare_image_inputs()

						lowercase         = image_processor(A__							,			return_tensors="np"				)
						lowercase         = 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 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"

						lowercase         = processor(text=A__				)

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




		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"
						lowercase         = self.prepare_image_inputs()

						lowercase         = 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 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

						lowercase         = processor.char_decode(A__				)
						lowercase         = tokenizer.batch_decode(A__				)
						lowercase         = [seq.replace(" "							,			""				) for seq in decoded_tok]

						self.assertListEqual(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = None
						lowercase         = self.prepare_image_inputs()

						lowercase         = processor(text=A__							,			images=A__				)

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





		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = torch.randn(1							,			2_7							,			3_8				)
						lowercase         = torch.randn(1							,			2_7							,			5_0_2_5_7				)
						lowercase         = torch.randn(1							,			2_7							,			3_0_5_2_2				)

						lowercase         = processor.batch_decode([char_input, bpe_input, wp_input]				)

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




