


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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_lowercase				     = logging.get_logger(__name__)



class 	lowerCamelCase__			(      __UpperCamelCase      ):
   __lowerCamelCase			      =  ["""pixel_values"""]




   def __init__(   self						:						Optional[Any]    ,    __a						:						bool = True    ,    __a						:						Dict[str, int] = None    ,    __a						:						int = 0.9    ,    __a						:						PILImageResampling = PILImageResampling.BICUBIC    ,    __a						:						bool = True    ,    __a						:						Dict[str, int] = None    ,    __a						:						Union[int, float] = 1 / 255    ,    __a						:						bool = True    ,    __a						:						bool = True    ,    __a						:						Optional[Union[float, List[float]]] = None    ,    __a						:						Optional[Union[float, List[float]]] = None    ,    **__a						:						List[str]    ,    ):




         '''simple docstring'''







         super().__init__(**__UpperCAmelCase							)
         lowerCamelCase__:						Optional[Any]							   = size if size is not None else {"shortest_edge": 224}
         lowerCamelCase__:						Tuple							   = get_size_dict(__UpperCAmelCase    ,    default_to_square=__UpperCAmelCase							)
         lowerCamelCase__:						Dict							   = crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCamelCase__:						str							   = get_size_dict(__UpperCAmelCase    ,    param_name="""crop_size"""							)

         lowerCamelCase__:						Optional[Any]							   = do_resize
         lowerCamelCase__:						Dict							   = size
         lowerCamelCase__:						Tuple							   = crop_pct
         lowerCamelCase__:						Union[str, Any]							   = resample
         lowerCamelCase__:						int							   = do_center_crop
         lowerCamelCase__:						Optional[int]							   = crop_size
         lowerCamelCase__:						int							   = do_rescale
         lowerCamelCase__:						List[Any]							   = rescale_factor
         lowerCamelCase__:						Optional[int]							   = do_normalize
         lowerCamelCase__:						Any							   = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         lowerCamelCase__:						int							   = image_std if image_std is not None else IMAGENET_DEFAULT_STD




   def 							lowerCamelCase_   (   self						:						Tuple    ,    __a						:						np.ndarray    ,    __a						:						Dict[str, int]    ,    __a						:						Optional[float] = None    ,    __a						:						PILImageResampling = PILImageResampling.BICUBIC    ,    __a						:						Optional[Union[str, ChannelDimension]] = None    ,    **__a						:						int    ,    ):




         '''simple docstring'''







         lowerCamelCase__:						Optional[Any]							   = get_size_dict(__UpperCAmelCase    ,    default_to_square=__UpperCAmelCase							)
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
               raise ValueError(f"""size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""							)
         if crop_pct is not None:
               if "shortest_edge" in size:
                     lowerCamelCase__:						str							   = int(size["""shortest_edge"""] / crop_pct							)
               elif "height" in size and "width" in size:
                     if size["height"] == size["width"]:
                           lowerCamelCase__:						Optional[int]							   = int(size["""height"""] / crop_pct							)
                     else:
                           lowerCamelCase__:						Optional[int]							   = (int(size["""height"""] / crop_pct							), int(size["""width"""] / crop_pct							))
               else:
                     raise ValueError("""Invalid size for resize: {}""".format(__UpperCAmelCase							)							)

               lowerCamelCase__:						Any							   = get_resize_output_image_size(__UpperCAmelCase    ,    size=__UpperCAmelCase    ,    default_to_square=__UpperCAmelCase							)
         else:
               if "shortest_edge" in size:
                     lowerCamelCase__:						List[Any]							   = get_resize_output_image_size(__UpperCAmelCase    ,    size=size["""shortest_edge"""]    ,    default_to_square=__UpperCAmelCase							)
               elif "height" in size and "width" in size:
                     lowerCamelCase__:						List[str]							   = (size["height"], size["width"])
               else:
                     raise ValueError("""Invalid size for resize: {}""".format(__UpperCAmelCase							)							)

         return resize(__UpperCAmelCase    ,    size=__UpperCAmelCase    ,    resample=__UpperCAmelCase    ,    data_format=__UpperCAmelCase    ,    **__UpperCAmelCase							)




   def 							lowerCamelCase_   (   self						:						Optional[Any]    ,    __a						:						np.ndarray    ,    __a						:						Dict[str, int]    ,    __a						:						Optional[Union[str, ChannelDimension]] = None    ,    **__a						:						Tuple    ,    ):




         '''simple docstring'''







         lowerCamelCase__:						Dict							   = get_size_dict(__UpperCAmelCase							)
         if "height" not in size or "width" not in size:
               raise ValueError(f"""size must contain \'height\' and \'width\' as keys. Got {size.keys()}"""							)
         return center_crop(__UpperCAmelCase    ,    size=(size["""height"""], size["""width"""])    ,    data_format=__UpperCAmelCase    ,    **__UpperCAmelCase							)




   def 							lowerCamelCase_   (   self						:						List[Any]    ,    __a						:						np.ndarray    ,    __a						:						Union[int, float]    ,    __a						:						Optional[Union[str, ChannelDimension]] = None    ,    **__a						:						int    ,    ):




         '''simple docstring'''







         return rescale(__UpperCAmelCase    ,    scale=__UpperCAmelCase    ,    data_format=__UpperCAmelCase    ,    **__UpperCAmelCase							)




   def 							lowerCamelCase_   (   self						:						Any    ,    __a						:						np.ndarray    ,    __a						:						Union[float, List[float]]    ,    __a						:						Union[float, List[float]]    ,    __a						:						Optional[Union[str, ChannelDimension]] = None    ,    **__a						:						List[str]    ,    ):




         '''simple docstring'''







         return normalize(__UpperCAmelCase    ,    mean=__UpperCAmelCase    ,    std=__UpperCAmelCase    ,    data_format=__UpperCAmelCase    ,    **__UpperCAmelCase							)




   def 							lowerCamelCase_   (   self						:						Tuple    ,    __a						:						ImageInput    ,    __a						:						bool = None    ,    __a						:						Dict[str, int] = None    ,    __a						:						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						:						Union[str, Any]    ,    ):




         '''simple docstring'''







         lowerCamelCase__:						int							   = do_resize if do_resize is not None else self.do_resize
         lowerCamelCase__:						Union[str, Any]							   = crop_pct if crop_pct is not None else self.crop_pct
         lowerCamelCase__:						str							   = resample if resample is not None else self.resample
         lowerCamelCase__:						str							   = do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCamelCase__:						str							   = do_rescale if do_rescale is not None else self.do_rescale
         lowerCamelCase__:						Dict							   = rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCamelCase__:						str							   = do_normalize if do_normalize is not None else self.do_normalize
         lowerCamelCase__:						List[str]							   = image_mean if image_mean is not None else self.image_mean
         lowerCamelCase__:						Optional[Any]							   = image_std if image_std is not None else self.image_std

         lowerCamelCase__:						List[Any]							   = size if size is not None else self.size
         lowerCamelCase__:						Dict							   = get_size_dict(__UpperCAmelCase    ,    default_to_square=__UpperCAmelCase							)
         lowerCamelCase__:						Union[str, Any]							   = crop_size if crop_size is not None else self.crop_size
         lowerCamelCase__:						str							   = get_size_dict(__UpperCAmelCase    ,    param_name="""crop_size"""							)

         lowerCamelCase__:						Optional[Any]							   = make_list_of_images(__UpperCAmelCase							)

         if not valid_images(__UpperCAmelCase							):
               raise ValueError(
                   """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                   """torch.Tensor, tf.Tensor or jax.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_pct is None:
               raise ValueError("""Crop_pct 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.
         lowerCamelCase__:						Optional[int]							   = [to_numpy_array(__UpperCAmelCase							) for image in images]

         if do_resize:
               lowerCamelCase__:						str							   = [self.resize(image=__UpperCAmelCase    ,    size=__UpperCAmelCase    ,    crop_pct=__UpperCAmelCase    ,    resample=__UpperCAmelCase							) for image in images]

         if do_center_crop:
               lowerCamelCase__:						Optional[Any]							   = [self.center_crop(image=__UpperCAmelCase    ,    size=__UpperCAmelCase							) for image in images]

         if do_rescale:
               lowerCamelCase__:						Tuple							   = [self.rescale(image=__UpperCAmelCase    ,    scale=__UpperCAmelCase							) for image in images]

         if do_normalize:
               lowerCamelCase__:						Optional[int]							   = [self.normalize(image=__UpperCAmelCase    ,    mean=__UpperCAmelCase    ,    std=__UpperCAmelCase							) for image in images]

         lowerCamelCase__:						Dict							   = [to_channel_dimension_format(__UpperCAmelCase    ,    __UpperCAmelCase							) for image in images]

         lowerCamelCase__:						Union[str, Any]							   = {"pixel_values": images}
         return BatchFeature(data=__UpperCAmelCase    ,    tensor_type=__UpperCAmelCase							)














import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      MvpTokenizer
						snake_case__        =      MvpTokenizerFast
						snake_case__        =      True
						snake_case__        =      filter_roberta_detectors



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Any:




											'''simple docstring'''



											super().setUp()
											__a					:						Dict      =					[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											__a					:						Optional[int]      =					dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase   )   )   )   )
											__a					:						Dict      =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											__a					:						Optional[int]      =					{"unk_token": "<unk>"}

											__a					:						Tuple      =					os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]   )
											__a					:						List[str]      =					os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["merges_file"]   )
											with open(self.vocab_file ,					"w" ,					encoding="utf-8"   ) as fp:
																fp.write(json.dumps(__UpperCAmelCase   ) + "\n"   )
											with open(self.merges_file ,					"w" ,					encoding="utf-8"   ) as fp:
																fp.write("\n".join(__UpperCAmelCase   )   )



						def        UpperCAmelCase__					(self:					List[Any] ,					**__UpperCAmelCase:					Dict   )    ->     int:




											'''simple docstring'''



											kwargs.update(self.special_tokens_map   )
											return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					str ,					**__UpperCAmelCase:					Tuple   )    ->     Tuple:




											'''simple docstring'''



											kwargs.update(self.special_tokens_map   )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					int   )    ->     List[str]:




											'''simple docstring'''



											return "lower newer", "lower newer"



						@cached_property
						def        UpperCAmelCase__					(self:					List[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											return MvpTokenizer.from_pretrained("RUCAIBox/mvp"   )



						@cached_property
						def        UpperCAmelCase__					(self:					str   )    ->     List[Any]:




											'''simple docstring'''



											return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"   )



						@require_torch
						def        UpperCAmelCase__					(self:					Dict   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											__a					:						List[Any]      =					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Optional[Any]      =					tokenizer(__UpperCAmelCase ,					max_length=len(__UpperCAmelCase   ) ,					padding=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )

																self.assertEqual((2, 9) ,					batch.input_ids.shape   )
																self.assertEqual((2, 9) ,					batch.attention_mask.shape   )
																__a					:						Optional[int]      =					batch.input_ids.tolist()[0]
																self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase   )
																# Test that special tokens are reset



						@require_torch
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						int      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						int      =					tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase ,					return_tensors="pt"   )
																# check if input_ids are returned and no labels
																self.assertIn("input_ids" ,					__UpperCAmelCase   )
																self.assertIn("attention_mask" ,					__UpperCAmelCase   )
																self.assertNotIn("labels" ,					__UpperCAmelCase   )
																self.assertNotIn("decoder_attention_mask" ,					__UpperCAmelCase   )



						@require_torch
						def        UpperCAmelCase__					(self:					Any   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						List[Any]      =					[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						int      =					tokenizer(text_target=__UpperCAmelCase ,					max_length=32 ,					padding="max_length" ,					return_tensors="pt"   )
																self.assertEqual(32 ,					targets["input_ids"].shape[1]   )



						@require_torch
						def        UpperCAmelCase__					(self:					int   )    ->     Union[str, Any]:




											'''simple docstring'''



											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						str      =					tokenizer(
																    ["I am a small frog" * 1024, "I am a small frog"] ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )
																self.assertEqual(batch.input_ids.shape ,					(2, 1024)   )



						@require_torch
						def        UpperCAmelCase__					(self:					str   )    ->     Any:




											'''simple docstring'''



											__a					:						Optional[int]      =					["A long paragraph for summarization."]
											__a					:						Tuple      =					[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Dict      =					tokenizer(__UpperCAmelCase ,					text_target=__UpperCAmelCase ,					return_tensors="pt"   )
																__a					:						str      =					inputs["input_ids"]
																__a					:						Optional[Any]      =					inputs["labels"]
																self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
																self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
																self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
																self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											pass



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




											'''simple docstring'''



											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																					__a					:						Tuple      =					self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						List[Any]      =					self.tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						Tuple      =					"A, <mask> AllenNLP sentence."
																					__a					:						Dict      =					tokenizer_r.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )
																					__a					:						List[Any]      =					tokenizer_p.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )

																					# token_type_ids should put 0 everywhere
																					self.assertEqual(sum(tokens_r["token_type_ids"]   ) ,					sum(tokens_p["token_type_ids"]   )   )

																					# attention_mask should put 1 everywhere, so sum over length should be 1
																					self.assertEqual(
																					    sum(tokens_r["attention_mask"]   ) / len(tokens_r["attention_mask"]   ) ,					sum(tokens_p["attention_mask"]   ) / len(tokens_p["attention_mask"]   ) ,					)

																					__a					:						Optional[int]      =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
																					__a					:						Union[str, Any]      =					tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]   )

																					# Rust correctly handles the space before the mask while python doesnt
																					self.assertSequenceEqual(tokens_p["input_ids"] ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )
																					self.assertSequenceEqual(tokens_r["input_ids"] ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )

																					self.assertSequenceEqual(
																					    __UpperCAmelCase ,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )
																					self.assertSequenceEqual(
																					    __UpperCAmelCase ,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )





