


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPSegProcessor, ViTImageProcessor


@require_vision
class 					lowerCAmelCase_ (       unittest.TestCase):




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






         """simple docstring"""






         a__ :List[Any]						  =    tempfile.mkdtemp()

         # fmt: off
         a__ :List[str]						  =    ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
         # fmt: on
         a__ :List[Any]						  =    dict(zip(__A   ,     range(len(__A						)						)						)						)
         a__ :Union[str, Any]						  =    ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
         a__ :Union[str, Any]						  =    {"unk_token": "<unk>"}

         a__ :List[Any]						  =    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(__A						) + "\n"						)
         with open(self.merges_file   ,     "w"   ,     encoding="utf-8"						) as fp:
            fp.write("\n".join(__A						)						)

         a__ :Tuple						  =    {
             "do_resize": True,
             "size": 20,
             "do_center_crop": True,
             "crop_size": 18,
             "do_normalize": True,
             "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
             "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
         }
         a__ :Any						  =    os.path.join(self.tmpdirname   ,     __A						)
         with open(self.image_processor_file   ,     "w"   ,     encoding="utf-8"						) as fp:
            json.dump(__A   ,     __A						)




      def  _snake_case					(	self						:   str   ,     **__A						:   str						)      ->Optional[int]:






         """simple docstring"""






         return CLIPTokenizer.from_pretrained(self.tmpdirname   ,     **__A						)




      def  _snake_case					(	self						:   Any   ,     **__A						:   int						)      ->Optional[int]:






         """simple docstring"""






         return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,     **__A						)




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






         """simple docstring"""






         return ViTImageProcessor.from_pretrained(self.tmpdirname   ,     **__A						)




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






         """simple docstring"""






         shutil.rmtree(self.tmpdirname						)




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






         """simple docstring"""






         a__ :Optional[int]						  =    [np.random.randint(255   ,     size=(3, 30, 400)   ,     dtype=np.uinta						)]

         a__ :Dict						  =    [Image.fromarray(np.moveaxis(__A   ,     0   ,     -1						)						) for x in image_inputs]

         return image_inputs




      def  _snake_case					(	self						:   Any						)      ->int:






         """simple docstring"""






         a__ :Optional[Any]						  =    self.get_tokenizer()
         a__ :Any						  =    self.get_rust_tokenizer()
         a__ :List[str]						  =    self.get_image_processor()

         a__ :Dict						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)
         processor_slow.save_pretrained(self.tmpdirname						)
         a__ :Optional[int]						  =    CLIPSegProcessor.from_pretrained(self.tmpdirname   ,     use_fast=__A						)

         a__ :str						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)
         processor_fast.save_pretrained(self.tmpdirname						)
         a__ :str						  =    CLIPSegProcessor.from_pretrained(self.tmpdirname						)

         self.assertEqual(processor_slow.tokenizer.get_vocab()   ,     tokenizer_slow.get_vocab()						)
         self.assertEqual(processor_fast.tokenizer.get_vocab()   ,     tokenizer_fast.get_vocab()						)
         self.assertEqual(tokenizer_slow.get_vocab()   ,     tokenizer_fast.get_vocab()						)
         self.assertIsInstance(processor_slow.tokenizer   ,     __A						)
         self.assertIsInstance(processor_fast.tokenizer   ,     __A						)

         self.assertEqual(processor_slow.image_processor.to_json_string()   ,     image_processor.to_json_string()						)
         self.assertEqual(processor_fast.image_processor.to_json_string()   ,     image_processor.to_json_string()						)
         self.assertIsInstance(processor_slow.image_processor   ,     __A						)
         self.assertIsInstance(processor_fast.image_processor   ,     __A						)




      def  _snake_case					(	self						:   Tuple						)      ->Any:






         """simple docstring"""






         a__ :int						  =    CLIPSegProcessor(tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor()						)
         processor.save_pretrained(self.tmpdirname						)

         a__ :str						  =    self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"						)
         a__ :Tuple						  =    self.get_image_processor(do_normalize=__A   ,     padding_value=1.0						)

         a__ :int						  =    CLIPSegProcessor.from_pretrained(
             self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=__A   ,     padding_value=1.0						)

         self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()						)
         self.assertIsInstance(processor.tokenizer   ,     __A						)

         self.assertEqual(processor.image_processor.to_json_string()   ,     image_processor_add_kwargs.to_json_string()						)
         self.assertIsInstance(processor.image_processor   ,     __A						)




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






         """simple docstring"""






         a__ :Dict						  =    self.get_image_processor()
         a__ :Optional[Any]						  =    self.get_tokenizer()

         a__ :str						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

         a__ :str						  =    self.prepare_image_inputs()

         a__ :int						  =    image_processor(__A   ,     return_tensors="np"						)
         a__ :int						  =    processor(images=__A   ,     return_tensors="np"						)

         for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1E-2						)




      def  _snake_case					(	self						:   int						)      ->str:






         """simple docstring"""






         a__ :List[str]						  =    self.get_image_processor()
         a__ :Tuple						  =    self.get_tokenizer()

         a__ :str						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

         a__ :str						  =    "lower newer"

         a__ :List[str]						  =    processor(text=__A						)

         a__ :Tuple						  =    tokenizer(__A						)

         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]						)




      def  _snake_case					(	self						:   str						)      ->str:






         """simple docstring"""






         a__ :str						  =    self.get_image_processor()
         a__ :Tuple						  =    self.get_tokenizer()

         a__ :Optional[int]						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

         a__ :Union[str, Any]						  =    "lower newer"
         a__ :Optional[Any]						  =    self.prepare_image_inputs()

         a__ :Union[str, Any]						  =    processor(text=__A   ,     images=__A						)

         self.assertListEqual(list(inputs.keys()						)   ,     ["input_ids", "attention_mask", "pixel_values"]						)

         # test if it raises when no input is passed
         with pytest.raises(__A						):
            processor()




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






         """simple docstring"""






         a__ :List[Any]						  =    self.get_image_processor()
         a__ :List[Any]						  =    self.get_tokenizer()

         a__ :int						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

         a__ :List[str]						  =    self.prepare_image_inputs()
         a__ :List[str]						  =    self.prepare_image_inputs()

         a__ :Union[str, Any]						  =    processor(images=__A   ,     visual_prompt=__A						)

         self.assertListEqual(list(inputs.keys()						)   ,     ["pixel_values", "conditional_pixel_values"]						)

         # test if it raises when no input is passed
         with pytest.raises(__A						):
            processor()






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






         """simple docstring"""






         a__ :Tuple						  =    self.get_image_processor()
         a__ :Optional[Any]						  =    self.get_tokenizer()

         a__ :List[str]						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

         a__ :int						  =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         a__ :Dict						  =    processor.batch_decode(__A						)
         a__ :Dict						  =    tokenizer.batch_decode(__A						)

         self.assertListEqual(__A   ,     __A						)






def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list[list]:





			"""simple docstring"""
			a__ :Any						  =    current_set.copy()
			for row_index, row in enumerate(a				):
						a__ :List[str]						  =    row[0]
						for column_index, column in enumerate(a				):
									if magnitude == 0:
												a__ :Tuple						  =    column
												continue
									a__ :Tuple						  =    column / magnitude
    # Subtract to cancel term
			a__ :Any						  =    current_set[0]
			a__ :Dict						  =    [first_row]
			a__ :Dict						  =    current_set[1::]
			for row in current_set:
						a__ :int						  =    []
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(a				)
									continue
						for column_index in range(len(a				)				):
									temp_row.append(first_row[column_index] - row[column_index]				)
						final_set.append(a				)
			# Create next recursion iteration set
			if len(final_set[0]				) != 3:
						a__ :List[str]						  =    final_set[0]
						a__ :Optional[Any]						  =    []
						a__ :List[Any]						  =    []
						for row in final_set[1::]:
									current_first_column.append(row[0]				)
									next_iteration.append(row[1::]				)
						a__ :Dict						  =    simplify(a				)
						for i in range(len(a				)				):
									resultant[i].insert(0	,  current_first_column[i]				)
						resultant.insert(0	,  a				)
						a__ :Optional[int]						  =    resultant
			return final_set


def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list:





			"""simple docstring"""
			if len(a				) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			a__ :Any						  =    len(a				) + 1
			if any(len(a				) != _length for item in equations				):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			for row in equations:
						if any(not isinstance(a	,  (int, float)				) for column in row				):
									raise ValueError("solve_simultaneous() requires lists of integers"				)
			if len(a				) == 1:
						return [equations[0][-1] / equations[0][0]]
			a__ :Union[str, Any]						  =    equations.copy()
			if any(0 in row for row in data_set				):
						a__ :int						  =    data_set.copy()
						a__ :Dict						  =    []
						for row_index, row in enumerate(a				):
									if 0 not in row:
												a__ :Union[str, Any]						  =    data_set.pop(a				)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"				)
						data_set.insert(0	,  a				)
			a__ :Optional[int]						  =    data_set.copy()
			a__ :Any						  =    simplify(a				)
			a__ :str						  =    simplified[::-1]
			a__ :list						  =    []
			for row in simplified:
						a__ :Optional[int]						  =    row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0				)
												continue
									solutions.append(current_solution / row[-2]				)
									continue
						a__ :Dict						  =    row.copy()[: len(a				) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0				)
						if len(a				) == 0:
									solutions.append(0				)
									continue
						a__ :List[Any]						  =    temp_row[1::]
						a__ :Union[str, Any]						  =    temp_row[::-1]
						for column_index, column in enumerate(a				):
									current_solution -= column * solutions[column_index]
						solutions.append(a				)
			a__ :List[str]						  =    []
			for item in solutions:
						final.append(float(round(a	,  5				)				)				)
			return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		snake_case__			 =						[
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))


