

"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


__A						   =			logging.get_logger(__name__)






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	str:
		__lowerCAmelCase:       Union[str, Any]          =			b.T
		__lowerCAmelCase:       str          =			np.sum(np.square(__SCREAMING_SNAKE_CASE )					, axis=1 )
		__lowerCAmelCase:       Optional[Any]          =			np.sum(np.square(__SCREAMING_SNAKE_CASE )					, axis=0 )
		__lowerCAmelCase:       int          =			np.matmul(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       int          =			aa[:, None] - 2 * ab + ba[None, :]
		return d






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Dict:
		__lowerCAmelCase:       Union[str, Any]          =			x.reshape(-1					, 3 )
		__lowerCAmelCase:       Optional[Any]          =			squared_euclidean_distance(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		return np.argmin(__SCREAMING_SNAKE_CASE					, axis=1 )





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  ["""pixel_values"""]

				def __init__(	self							:				int     ,  UpperCamelCase__							:				Optional[Union[List[List[int]], np.ndarray]] = None     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				bool = True     ,  **UpperCamelCase__							:				Union[str, Any]     ,  )->   None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)
						__lowerCAmelCase:       Any          =			size if size is not None else {"height": 2_5_6, "width": 2_5_6}
						__lowerCAmelCase:       int          =			get_size_dict(UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			np.array(UpperCamelCase__) if clusters is not None else None
						__lowerCAmelCase:       int          =			do_resize
						__lowerCAmelCase:       int          =			size
						__lowerCAmelCase:       Tuple          =			resample
						__lowerCAmelCase:       Union[str, Any]          =			do_normalize
						__lowerCAmelCase:       Union[str, Any]          =			do_color_quantize

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Dict[str, int]     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				str     ,  )->   np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			get_size_dict(UpperCamelCase__)
						if "height" not in size or "width" not in size:
								raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}")
						return resize(
						    UpperCamelCase__     ,  size=(size["height"], size["width"])     ,  resample=UpperCamelCase__     ,  data_format=UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  )->   np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			rescale(image=UpperCamelCase__     ,  scale=1 / 127.5     ,  data_format=UpperCamelCase__)
						__lowerCAmelCase:       Union[str, Any]          =			image - 1
						return image




				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				ImageInput     ,  UpperCamelCase__							:				bool = None     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				PILImageResampling = None     ,  UpperCamelCase__							:				bool = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[Union[List[List[int]], np.ndarray]] = None     ,  UpperCamelCase__							:				Optional[Union[str, TensorType]] = None     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST     ,  **UpperCamelCase__							:				Optional[int]     ,  )->   PIL.Image.Image:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase:       List[str]          =			size if size is not None else self.size
						__lowerCAmelCase:       Any          =			get_size_dict(UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			resample if resample is not None else self.resample
						__lowerCAmelCase:       int          =			do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase:       Optional[int]          =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						__lowerCAmelCase:       Optional[Any]          =			clusters if clusters is not None else self.clusters
						__lowerCAmelCase:       Tuple          =			np.array(UpperCamelCase__)

						__lowerCAmelCase:       Tuple          =			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_color_quantize and clusters is None:
								raise ValueError("Clusters must be specified if do_color_quantize is True.")

						# All transformations expect numpy arrays.
						__lowerCAmelCase:       Optional[Any]          =			[to_numpy_array(UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase:       Union[str, Any]          =			[self.resize(image=UpperCamelCase__     ,  size=UpperCamelCase__     ,  resample=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase:       List[Any]          =			[self.normalize(image=UpperCamelCase__) for image in images]

						if do_color_quantize:
								__lowerCAmelCase:       Union[str, Any]          =			[to_channel_dimension_format(UpperCamelCase__     ,  ChannelDimension.LAST) for image in images]
								# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
								__lowerCAmelCase:       int          =			np.array(UpperCamelCase__)
								__lowerCAmelCase:       int          =			color_quantize(UpperCamelCase__     ,  UpperCamelCase__).reshape(images.shape[:-1])

								# flatten to (batch_size, height*width)
								__lowerCAmelCase:       Optional[Any]          =			images.shape[0]
								__lowerCAmelCase:       int          =			images.reshape(UpperCamelCase__     ,  -1)

								# We need to convert back to a list of images to keep consistent behaviour across processors.
								__lowerCAmelCase:       Union[str, Any]          =			list(UpperCamelCase__)
						else:
								__lowerCAmelCase:       Optional[Any]          =			[to_channel_dimension_format(UpperCamelCase__     ,  UpperCamelCase__) for image in images]

						__lowerCAmelCase:       int          =			{"input_ids": images}
						return BatchFeature(data=UpperCamelCase__     ,  tensor_type=UpperCamelCase__)





"""simple docstring"""


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      str         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      int         =  True
				SCREAMING_SNAKE_CASE_   :      Tuple         =  False
				SCREAMING_SNAKE_CASE_   :      int         =  """tokenizer_file"""
				SCREAMING_SNAKE_CASE_   :      List[str]         =  {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

				def 							lowercase_     (	self							:				List[Any])->   Dict:



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase:       Optional[Any]          =			BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
						tokenizer.save_pretrained(self.tmpdirname)

				def 							lowercase_     (	self							:				List[Any]     ,  **UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.get_rust_tokenizer()

						__lowerCAmelCase:       int          =			["The quick brown fox</s>", "jumps over the lazy dog</s>"]
						__lowerCAmelCase:       List[str]          =			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

						__lowerCAmelCase:       List[str]          =			tokenizer.batch_encode_plus(UpperCamelCase__)["input_ids"]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			tokenizer.batch_decode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple=6)->   Tuple:



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Optional[Any]          =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										# tokenizer_r.pad_token = None # Hotfixing padding = None
										# Simple input
										__lowerCAmelCase:       Dict          =			"This is a simple input"
										__lowerCAmelCase:       str          =			["This is a simple input 1", "This is a simple input 2"]
										__lowerCAmelCase:       int          =			("This is a simple input", "This is a pair")
										__lowerCAmelCase:       Union[str, Any]          =			[
										    ("This is a simple input 1", "This is a simple input 2"),
										    ("This is a simple pair 1", "This is a simple pair 2"),
										]

										# Simple input tests
										try:
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)

												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
										except ValueError:
												self.fail("Bloom Tokenizer should be able to deal with padding")

										__lowerCAmelCase:       Tuple          =			None  # Hotfixing padding = None
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

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



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.get_rust_tokenizer()
						__lowerCAmelCase:       List[str]          =			load_dataset("xnli"     ,  "all_languages"     ,  split="test"     ,  streaming=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			next(iter(UpperCamelCase__))["premise"]  # pick up one data
						__lowerCAmelCase:       Any          =			list(sample_data.values())

						__lowerCAmelCase:       int          =			list(map(tokenizer.encode     ,  UpperCamelCase__))
						__lowerCAmelCase:       str          =			[tokenizer.decode(UpperCamelCase__     ,  clean_up_tokenization_spaces=UpperCamelCase__) for x in output_tokens]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)




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



						'''simple docstring'''


						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)     ,  1)
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])     ,  1)


