import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__  	=      get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					ErnieMTokenizer
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					True
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		_lowercase   =ErnieMTokenizer(lowerCAmelCase_       ,  unk_token="<unk>"       ,  pad_token="<pad>"       )
		tokenizer.save_pretrained(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ="this is a test"
		_lowercase   ="this is a test"
		return input_text, output_text


	def __lowerCAmelCase      (   self       ):
		_lowercase   ="<pad>"
		_lowercase   =0

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_       )       ,  lowerCAmelCase_       )
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_       )       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =list(self.get_tokenizer().get_vocab().keys()       )

		self.assertEqual(vocab_keys[0]       ,  "<pad>"       )
		self.assertEqual(vocab_keys[1]       ,  "<unk>"       )
		self.assertEqual(vocab_keys[-1]       ,  "▁eloquent"       )
		self.assertEqual(len(lowerCAmelCase_       )       ,  30000       )


	def __lowerCAmelCase      (   self       ):
		self.assertEqual(self.get_tokenizer().vocab_size       ,  30000       )


	def __lowerCAmelCase      (   self       ):
		if not self.test_rust_tokenizer:
			return

		_lowercase   =self.get_tokenizer()
		_lowercase   =self.get_rust_tokenizer()

		_lowercase   ="I was born in 92000, and this is falsé."

		_lowercase   =tokenizer.tokenize(lowerCAmelCase_       )
		_lowercase   =rust_tokenizer.tokenize(lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =tokenizer.encode(lowerCAmelCase_       ,  add_special_tokens=lowerCAmelCase_       )
		_lowercase   =rust_tokenizer.encode(lowerCAmelCase_       ,  add_special_tokens=lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =self.get_rust_tokenizer()
		_lowercase   =tokenizer.encode(lowerCAmelCase_       )
		_lowercase   =rust_tokenizer.encode(lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =ErnieMTokenizer(lowerCAmelCase_       ,  do_lower_case=lowerCAmelCase_       ,  unk_token="<unk>"       ,  pad_token="<pad>"       )

		_lowercase   =tokenizer.tokenize("This is a test"       )
		self.assertListEqual(lowerCAmelCase_       ,  ["▁this", "▁is", "▁a", "▁test"]       )

		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_       )       ,  [48, 25, 21, 1289]       )

		_lowercase   =tokenizer.tokenize("I was born in 92000, and this is falsé."       )
		# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
		self.assertListEqual(
		    lowerCAmelCase_       ,  ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]       )
		_lowercase   =tokenizer.convert_tokens_to_ids(lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]       )

		_lowercase   =tokenizer.convert_ids_to_tokens(lowerCAmelCase_       )
		self.assertListEqual(
		    lowerCAmelCase_       ,  ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]       ,  )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =ErnieMTokenizer(lowerCAmelCase_       ,  unk_token="<unk>"       ,  pad_token="<pad>"       )

		_lowercase   =tokenizer.encode("sequence builders"       )
		_lowercase   =tokenizer.encode("multi-sequence build"       )

		_lowercase   =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_       )
		_lowercase   =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_       ,  lowerCAmelCase_       )

		assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
		assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
		    tokenizer.sep_token_id
		] + text_a + [tokenizer.sep_token_id]


	@slow
	def __lowerCAmelCase      (   self       ):
		# fmt: off
		_lowercase   ={"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
		# fmt: on

		self.tokenizer_integration_test_util(
		    expected_encoding=lowerCAmelCase_       ,  model_name="susnato/ernie-m-base_pytorch"       ,  sequences=[
		        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
		        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
		        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
		        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
		        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
		        "conditioning on both left and right context in all layers.",
		        "The quick brown fox jumps over the lazy dog.",
		    ]       ,  )






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__lowerCamelCase								=					logging.get_logger(__name__)
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Union[str, Any]				   =      ["""pixel_values"""]

						def __init__(					self		:			str	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			PILImageResampling = PIL.Image.NEAREST	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			Union[int, float] = 1 / 2_55	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			bool = True	,						**_lowerCamelCase		:			Tuple	,						):
													super().__init__(**_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		size if size is not None else {"height": 3_46, "width": 3_46}
													_UpperCAmelCase  :			Dict	       =		get_size_dict(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
													_UpperCAmelCase  :			int	       =		get_size_dict(_lowerCamelCase	,						param_name="crop_size"			)

													_UpperCAmelCase  :			Dict	       =		do_resize
													_UpperCAmelCase  :			Tuple	       =		size
													_UpperCAmelCase  :			Union[str, Any]	       =		resample
													_UpperCAmelCase  :			List[Any]	       =		do_center_crop
													_UpperCAmelCase  :			List[str]	       =		crop_size
													_UpperCAmelCase  :			int	       =		do_rescale
													_UpperCAmelCase  :			str	       =		rescale_factor
													_UpperCAmelCase  :			int	       =		rescale_offset
													_UpperCAmelCase  :			Any	       =		do_normalize
													_UpperCAmelCase  :			Optional[int]	       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													_UpperCAmelCase  :			List[str]	       =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													_UpperCAmelCase  :			Dict	       =		include_top

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Dict[str, int]	,						_lowerCamelCase		:			PILImageResampling = PIL.Image.NEAREST	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Tuple	,						):
													_UpperCAmelCase  :			Optional[int]	       =		get_size_dict(_lowerCamelCase			)
													if "height" not in size or "width" not in size:
																				raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
													return resize(
													    _lowerCamelCase	,						size=(size["height"], size["width"])	,						resample=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Dict[str, int]	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			List[str]	,						):
													_UpperCAmelCase  :			Optional[Any]	       =		get_size_dict(_lowerCamelCase			)
													if "height" not in size or "width" not in size:
																				raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
													return center_crop(_lowerCamelCase	,						size=(size["height"], size["width"])	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Union[int, float]	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Tuple	,						):
													if offset:
																				_UpperCAmelCase  :			Any	       =		(image - 1_27.5) * scale
																				if data_format is not None:
																											_UpperCAmelCase  :			Union[str, Any]	       =		to_channel_dimension_format(_lowerCamelCase	,						_lowerCamelCase			)
																				_UpperCAmelCase  :			Optional[Any]	       =		rescaled_image.astype(np.floataa			)
													else:
																				_UpperCAmelCase  :			List[Any]	       =		rescale(_lowerCamelCase	,						scale=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)
													return rescaled_image

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






						def 						a__ (					self		:			str	,						_lowerCamelCase		:			ImageInput	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			List[Any]=None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			float = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Optional[Union[str, TensorType]] = None	,						_lowerCamelCase		:			ChannelDimension = ChannelDimension.FIRST	,						**_lowerCamelCase		:			str	,						):
													_UpperCAmelCase  :			Optional[Any]	       =		do_resize if do_resize is not None else self.do_resize
													_UpperCAmelCase  :			Optional[int]	       =		resample if resample is not None else self.resample
													_UpperCAmelCase  :			Tuple	       =		do_center_crop if do_center_crop is not None else self.do_center_crop
													_UpperCAmelCase  :			str	       =		do_rescale if do_rescale is not None else self.do_rescale
													_UpperCAmelCase  :			str	       =		rescale_factor if rescale_factor is not None else self.rescale_factor
													_UpperCAmelCase  :			Tuple	       =		rescale_offset if rescale_offset is not None else self.rescale_offset
													_UpperCAmelCase  :			str	       =		do_normalize if do_normalize is not None else self.do_normalize
													_UpperCAmelCase  :			Dict	       =		image_mean if image_mean is not None else self.image_mean
													_UpperCAmelCase  :			Tuple	       =		image_std if image_std is not None else self.image_std
													_UpperCAmelCase  :			Tuple	       =		include_top if include_top is not None else self.include_top

													_UpperCAmelCase  :			Optional[int]	       =		size if size is not None else self.size
													_UpperCAmelCase  :			Union[str, Any]	       =		get_size_dict(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		crop_size if crop_size is not None else self.crop_size
													_UpperCAmelCase  :			int	       =		get_size_dict(_lowerCamelCase	,						param_name="crop_size"			)

													_UpperCAmelCase  :			Union[str, Any]	       =		make_list_of_images(_lowerCamelCase			)

													if not valid_images(_lowerCamelCase			):
																				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_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.
													_UpperCAmelCase  :			Dict	       =		[to_numpy_array(_lowerCamelCase			) for image in images]

													if do_resize:
																				_UpperCAmelCase  :			Any	       =		[self.resize(image=_lowerCamelCase	,						size=_lowerCamelCase	,						resample=_lowerCamelCase			) for image in images]

													if do_center_crop:
																				_UpperCAmelCase  :			int	       =		[self.center_crop(image=_lowerCamelCase	,						size=_lowerCamelCase			) for image in images]

													if do_rescale:
																				_UpperCAmelCase  :			int	       =		[self.rescale(image=_lowerCamelCase	,						scale=_lowerCamelCase	,						offset=_lowerCamelCase			) for image in images]

													if do_normalize:
																				_UpperCAmelCase  :			str	       =		[self.normalize(image=_lowerCamelCase	,						mean=_lowerCamelCase	,						std=_lowerCamelCase			) for image in images]

													if include_top:
																				_UpperCAmelCase  :			int	       =		[self.normalize(image=_lowerCamelCase	,						mean=[0, 0, 0]	,						std=_lowerCamelCase			) for image in images]

													_UpperCAmelCase  :			str	       =		[to_channel_dimension_format(_lowerCamelCase	,						_lowerCamelCase			) for image in images]

													_UpperCAmelCase  :			Dict	       =		{"pixel_values": images}
													return BatchFeature(data=_lowerCamelCase	,						tensor_type=_lowerCamelCase			)




