


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      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       tempfile.mkdtemp()

        # fmt: off
        lowercase_       :Any     =       ['''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
        lowercase_       :Tuple     =       dict(zip(_lowerCamelCase   ,							range(len(_lowerCamelCase		)		)		)		)
        lowercase_       :Union[str, Any]     =       ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        lowercase_       :Tuple     =       {'''unk_token''': '''<unk>'''}

        lowercase_       :Optional[Any]     =       os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES['''vocab_file''']		)
        lowercase_       :Optional[Any]     =       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(_lowerCamelCase		) + '''\n'''		)
        with open(self.merges_file   ,							'''w'''   ,							encoding='''utf-8'''		) as fp:
             fp.write('''\n'''.join(_lowerCamelCase		)		)

        lowercase_       :Union[str, Any]     =       {
            '''do_resize''': True,
            '''size''': 20,
            '''do_center_crop''': True,
            '''crop_size''': 18,
            '''do_normalize''': True,
            '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
            '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        lowercase_       :List[Any]     =       os.path.join(self.tmpdirname   ,							_lowerCamelCase		)
        with open(self.image_processor_file   ,							'''w'''   ,							encoding='''utf-8'''		) as fp:
             json.dump(_lowerCamelCase   ,							_lowerCamelCase		)
   def 		UpperCamelCase  (     self   ,							**UpperCamelCase_		):
        return CLIPTokenizer.from_pretrained(self.tmpdirname   ,							**_lowerCamelCase		)
   def 		UpperCamelCase  (     self   ,							**UpperCamelCase_		):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,							**_lowerCamelCase		)
   def 		UpperCamelCase  (     self   ,							**UpperCamelCase_		):
        return ViTImageProcessor.from_pretrained(self.tmpdirname   ,							**_lowerCamelCase		)
   def 		UpperCamelCase  (     self		):
        shutil.rmtree(self.tmpdirname		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       [np.random.randint(255   ,							size=(3, 30, 400)   ,							dtype=np.uinta		)]

        lowercase_       :Optional[Any]     =       [Image.fromarray(np.moveaxis(_lowerCamelCase   ,							0   ,							-1		)		) for x in image_inputs]

        return image_inputs
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       self.get_tokenizer()
        lowercase_       :Optional[Any]     =       self.get_rust_tokenizer()
        lowercase_       :Dict     =       self.get_image_processor()

        lowercase_       :Union[str, Any]     =       CLIPSegProcessor(tokenizer=_lowerCamelCase   ,							image_processor=_lowerCamelCase		)
        processor_slow.save_pretrained(self.tmpdirname		)
        lowercase_       :List[str]     =       CLIPSegProcessor.from_pretrained(self.tmpdirname   ,							use_fast=_lowerCamelCase		)

        lowercase_       :int     =       CLIPSegProcessor(tokenizer=_lowerCamelCase   ,							image_processor=_lowerCamelCase		)
        processor_fast.save_pretrained(self.tmpdirname		)
        lowercase_       :List[Any]     =       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   ,							_lowerCamelCase		)
        self.assertIsInstance(processor_fast.tokenizer   ,							_lowerCamelCase		)

        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   ,							_lowerCamelCase		)
        self.assertIsInstance(processor_fast.image_processor   ,							_lowerCamelCase		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       CLIPSegProcessor(tokenizer=self.get_tokenizer()   ,							image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

        lowercase_       :Union[str, Any]     =       self.get_tokenizer(bos_token='''(BOS)'''   ,							eos_token='''(EOS)'''		)
        lowercase_       :Any     =       self.get_image_processor(do_normalize=_lowerCamelCase   ,							padding_value=1.0		)

        lowercase_       :Dict     =       CLIPSegProcessor.from_pretrained(
            self.tmpdirname   ,							bos_token='''(BOS)'''   ,							eos_token='''(EOS)'''   ,							do_normalize=_lowerCamelCase   ,							padding_value=1.0		)

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

        self.assertEqual(processor.image_processor.to_json_string()   ,							image_processor_add_kwargs.to_json_string()		)
        self.assertIsInstance(processor.image_processor   ,							_lowerCamelCase		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.get_image_processor()
        lowercase_       :List[str]     =       self.get_tokenizer()

        lowercase_       :List[str]     =       CLIPSegProcessor(tokenizer=_lowerCamelCase   ,							image_processor=_lowerCamelCase		)

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

        lowercase_       :Union[str, Any]     =       image_processor(_lowerCamelCase   ,							return_tensors='''np'''		)
        lowercase_       :Optional[int]     =       processor(images=_lowerCamelCase   ,							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 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       self.get_image_processor()
        lowercase_       :Optional[int]     =       self.get_tokenizer()

        lowercase_       :int     =       CLIPSegProcessor(tokenizer=_lowerCamelCase   ,							image_processor=_lowerCamelCase		)

        lowercase_       :Dict     =       '''lower newer'''

        lowercase_       :Optional[int]     =       processor(text=_lowerCamelCase		)

        lowercase_       :List[Any]     =       tokenizer(_lowerCamelCase		)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]   ,							encoded_processor[key]		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       self.get_image_processor()
        lowercase_       :Any     =       self.get_tokenizer()

        lowercase_       :List[str]     =       CLIPSegProcessor(tokenizer=_lowerCamelCase   ,							image_processor=_lowerCamelCase		)

        lowercase_       :int     =       '''lower newer'''
        lowercase_       :Optional[Any]     =       self.prepare_image_inputs()

        lowercase_       :List[Any]     =       processor(text=_lowerCamelCase   ,							images=_lowerCamelCase		)

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

        # test if it raises when no input is passed
        with pytest.raises(_lowerCamelCase		):
             processor()
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       self.get_image_processor()
        lowercase_       :str     =       self.get_tokenizer()

        lowercase_       :int     =       CLIPSegProcessor(tokenizer=_lowerCamelCase   ,							image_processor=_lowerCamelCase		)

        lowercase_       :Optional[int]     =       self.prepare_image_inputs()
        lowercase_       :List[str]     =       self.prepare_image_inputs()

        lowercase_       :List[Any]     =       processor(images=_lowerCamelCase   ,							visual_prompt=_lowerCamelCase		)

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

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




   def 		UpperCamelCase  (     self		):
        lowercase_       :Tuple     =       self.get_image_processor()
        lowercase_       :Optional[int]     =       self.get_tokenizer()

        lowercase_       :List[Any]     =       CLIPSegProcessor(tokenizer=_lowerCamelCase   ,							image_processor=_lowerCamelCase		)

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

        lowercase_       :Union[str, Any]     =       processor.batch_decode(_lowerCamelCase		)
        lowercase_       :Any     =       tokenizer.batch_decode(_lowerCamelCase		)

        self.assertListEqual(_lowerCamelCase   ,							_lowerCamelCase		)





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE       :		int         =				299_792_458

# Symbols
SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Optional[Any]         =				symbols("ct x y z")




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     if velocity > c:
          raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''		)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError('''Speed must be greater than or equal to 1!'''		)

     return velocity / c




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return 1 / sqrt(1 - beta(_a		) ** 2		)




def     UpperCamelCase    (      _a		)					->							np.ndarray:
     '''simple docstring'''





     return np.array(
         [
             [gamma(_a		), -gamma(_a		) * beta(_a		), 0, 0],
             [-gamma(_a		) * beta(_a		), gamma(_a		), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]		)




def     UpperCamelCase    (      _a       ,		_a = None		)					->							np.ndarray:
     '''simple docstring'''





     if event is None:
          lowercase_       :Optional[int]     =       np.array([ct, x, y, z]		)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(_a		) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   SCREAMING_SNAKE_CASE       :		List[str]         =				transform(29_979_245)
   print("Example of four vector: ")
   print(f"ct' = {four_vector[0]}")
   print(f"x' = {four_vector[1]}")
   print(f"y' = {four_vector[2]}")
   print(f"z' = {four_vector[3]}")

   # Substitute symbols with numerical values
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				{ct: c, x: 1, y: 1, z: 1}
   SCREAMING_SNAKE_CASE       :		str         =				[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f"\n{numerical_vector}")

