


"""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


lowerCAmelCase__				    =       logging.get_logger(__name__)
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


		'''simple docstring'''







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


		'''simple docstring'''







		lowerCAmelCase					:					str								=							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 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
							"""simple docstring"""


							a  :    str       =["pixel_values"]

							def __init__(						self		,					snake_case__ = None		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = True		,					snake_case__ = True		,					**snake_case__		,					):






									"""simple docstring"""





									super().__init__(**snake_case__						)
									lowerCAmelCase					:					List[Any]								=							size if size is not None else {"height": 256, "width": 256}
									lowerCAmelCase					:					Any								=							get_size_dict(snake_case__						)
									lowerCAmelCase					:					Tuple								=							np.array(snake_case__						) if clusters is not None else None
									lowerCAmelCase					:					Union[str, Any]								=							do_resize
									lowerCAmelCase					:					Optional[Any]								=							size
									lowerCAmelCase					:					Tuple								=							resample
									lowerCAmelCase					:					Dict								=							do_normalize
									lowerCAmelCase					:					Any								=							do_color_quantize

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = None		,					**snake_case__		,					):






									"""simple docstring"""





									lowerCAmelCase					:					str								=							get_size_dict(snake_case__						)
									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(
									    snake_case__		,					size=(size["height"], size["width"])		,					resample=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					):






									"""simple docstring"""





									lowerCAmelCase					:					List[Any]								=							rescale(image=snake_case__		,					scale=1 / 127.5		,					data_format=snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							image - 1
									return image







							def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = ChannelDimension.FIRST		,					**snake_case__		,					):






									"""simple docstring"""





									lowerCAmelCase					:					Any								=							do_resize if do_resize is not None else self.do_resize
									lowerCAmelCase					:					int								=							size if size is not None else self.size
									lowerCAmelCase					:					Tuple								=							get_size_dict(snake_case__						)
									lowerCAmelCase					:					int								=							resample if resample is not None else self.resample
									lowerCAmelCase					:					Dict								=							do_normalize if do_normalize is not None else self.do_normalize
									lowerCAmelCase					:					Tuple								=							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					:					Any								=							np.array(snake_case__						)

									lowerCAmelCase					:					str								=							make_list_of_images(snake_case__						)

									if not valid_images(snake_case__						):
											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					:					Tuple								=							[to_numpy_array(snake_case__						) for image in images]

									if do_resize:
											lowerCAmelCase					:					str								=							[self.resize(image=snake_case__		,					size=snake_case__		,					resample=snake_case__						) for image in images]

									if do_normalize:
											lowerCAmelCase					:					Any								=							[self.normalize(image=snake_case__						) for image in images]

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

											# flatten to (batch_size, height*width)
											lowerCAmelCase					:					int								=							images.shape[0]
											lowerCAmelCase					:					Dict								=							images.reshape(snake_case__		,					-1						)

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

									lowerCAmelCase					:					Optional[Any]								=							{"input_ids": images}
									return BatchFeature(data=snake_case__		,					tensor_type=snake_case__						)









"""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 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =None
       a  :    List[Any]       =BloomTokenizerFast
       a  :    Optional[int]       =BloomTokenizerFast
       a  :    Optional[Any]       =True
       a  :    Dict       =False
       a  :    Optional[Any]       ="tokenizer_file"
       a  :    Optional[int]       ={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()
         lowerCAmelCase					:					Tuple								=							BloomTokenizerFast.from_pretrained("bigscience/tokenizer"						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()

         lowerCAmelCase					:					List[Any]								=							["The quick brown fox</s>", "jumps over the lazy dog</s>"]
         lowerCAmelCase					:					str								=							[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_encode_plus(snake_case__						)["input_ids"]
         self.assertListEqual(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_decode(snake_case__						)
         self.assertListEqual(snake_case__		,					snake_case__						)

       def 	lowercase__							(						self		,					snake_case__=6						):






         """simple docstring"""





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase					:					Tuple								=							self.rust_tokenizer_class.from_pretrained(snake_case__		,					**snake_case__						)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             lowerCAmelCase					:					str								=							"This is a simple input"
             lowerCAmelCase					:					Tuple								=							["This is a simple input 1", "This is a simple input 2"]
             lowerCAmelCase					:					Any								=							("This is a simple input", "This is a pair")
             lowerCAmelCase					:					Tuple								=							[
                 ("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(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode_plus(snake_case__		,					max_length=snake_case__						)

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

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

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

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

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

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

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()
         lowerCAmelCase					:					int								=							load_dataset("xnli"		,					"all_languages"		,					split="test"		,					streaming=snake_case__						)

         lowerCAmelCase					:					Tuple								=							next(iter(snake_case__						)						)["premise"]  # pick up one data
         lowerCAmelCase					:					Optional[Any]								=							list(sample_data.values()						)

         lowerCAmelCase					:					int								=							list(map(tokenizer.encode		,					snake_case__						)						)
         lowerCAmelCase					:					List[Any]								=							[tokenizer.decode(snake_case__		,					clean_up_tokenization_spaces=snake_case__						) for x in output_tokens]
         self.assertListEqual(snake_case__		,					snake_case__						)







       def 	lowercase__							(						self						):






         """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						)





