import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					str=18	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Optional[int]=400	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[int]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					List[str]=[0.5, 0.5, 0.5]	,							)					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 18, "width": 18}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std





							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							DPTImageProcessor if is_vision_available() else None




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DPTImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"height": 18, "width": 18}      )

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict	,							size=42      )
												self.assertEqual(image_processor.size	,							{"height": 42, "width": 42}      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							Image.Image      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							np.ndarray      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)





							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							torch.Tensor      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)





import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase					:   Dict							=		logging.get_logger(__name__)

_lowerCamelCase					:   List[Any]							=		'''▁'''

_lowerCamelCase					:   Optional[int]							=		{'''vocab_file''': '''prophetnet.tokenizer'''}

_lowerCamelCase					:   str							=		{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]  ):
					SCREAMING_SNAKE_CASE    			=       collections.OrderedDict()
					with open(UpperCAmelCase__      , "r"      , encoding="utf-8"  ) as reader:
										SCREAMING_SNAKE_CASE    			=       reader.readlines()
					for index, token in enumerate(UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       token.rstrip("\n"  )
										SCREAMING_SNAKE_CASE    			=       index
					return vocab


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							VOCAB_FILES_NAMES
							lowercase__						:       Optional[Any]								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       Union[str, Any]								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__						:       Any								=							["""input_ids""", """attention_mask"""]




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					Dict="[SEP]"	,							_UpperCamelCase   :					Tuple="[UNK]"	,							_UpperCamelCase   :					Dict="[PAD]"	,							_UpperCamelCase   :					Any="[CLS]"	,							_UpperCamelCase   :					Optional[Any]="[MASK]"	,							_UpperCamelCase   :					Optional[Dict[str, Any]] = None	,							**_UpperCamelCase   :					Dict	,							)					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=_UpperCamelCase	,							eos_token=_UpperCamelCase	,							sep_token=_UpperCamelCase	,							unk_token=_UpperCamelCase	,							pad_token=_UpperCamelCase	,							cls_token=_UpperCamelCase	,							mask_token=_UpperCamelCase	,							sp_model_kwargs=self.sp_model_kwargs	,							**_UpperCamelCase	,							)

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(str(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												SCREAMING_SNAKE_CASE    			=       {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

												for i in range(10      ):
																	SCREAMING_SNAKE_CASE    			=       F"[unused{i}]"
																	SCREAMING_SNAKE_CASE    			=       5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												SCREAMING_SNAKE_CASE    			=       12
												SCREAMING_SNAKE_CASE    			=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(_UpperCamelCase      )




							def __getstate__(						self   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.__dict__.copy()
												SCREAMING_SNAKE_CASE    			=       None
												return state




							def __setstate__(						self   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

												# for backward compatibility
												if not hasattr(self	,							"sp_model_kwargs"      ):
																	SCREAMING_SNAKE_CASE    			=       {}

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(self.vocab_file      )




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None	,							_UpperCamelCase   :					bool = False      )					->				List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase	,							token_ids_a=_UpperCamelCase	,							already_has_special_tokens=_UpperCamelCase      )

												if token_ids_a is None:
																	return ([0] * len(_UpperCamelCase      )) + [1]
												return ([0] * len(_UpperCamelCase      )) + [1] + ([0] * len(_UpperCamelCase      )) + [1]




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]

												if token_ids_a is None:
																	return len(token_ids_a + sep      ) * [0]
												return len(token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




							@property
							def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												return len(self.sp_model      ) + self.fairseq_offset




							def 	__snake_case(						self   :					Optional[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {self.convert_ids_to_tokens(_UpperCamelCase      ): i for i in range(self.vocab_size      )}
												vocab.update(self.added_tokens_encoder      )
												return vocab




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					str      )					->				str:



												'''simple docstring'''

												return self.sp_model.encode(_UpperCamelCase	,							out_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												SCREAMING_SNAKE_CASE    			=       self.sp_model.PieceToId(_UpperCamelCase      )

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str      )					->				int:



												'''simple docstring'''

												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset      )




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "".join(_UpperCamelCase      ).replace(_UpperCamelCase	,							" "      ).strip()
												return out_string




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple[str]:



												'''simple docstring'''

												if not os.path.isdir(_UpperCamelCase      ):
																	logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
																	return
												SCREAMING_SNAKE_CASE    			=       os.path.join(
												    _UpperCamelCase	,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

												if os.path.abspath(self.vocab_file      ) != os.path.abspath(_UpperCamelCase      ) and os.path.isfile(self.vocab_file      ):
																	copyfile(self.vocab_file	,							_UpperCamelCase      )
												elif not os.path.isfile(self.vocab_file      ):
																	with open(_UpperCamelCase	,							"wb"      ) as fi:
																						SCREAMING_SNAKE_CASE    			=       self.sp_model.serialized_model_proto()
																						fi.write(_UpperCamelCase      )

												return (out_vocab_file,)





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




