


from __future__ import annotations

import time

import numpy as np

snake_case				:    Any       				=     [8, 5, 9, 7]
snake_case				:    Optional[Any]       				=     [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
snake_case				:    List[Any]       				=     [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       a						:List[Any] 							=       claim_vector
       a						:List[str] 							=       allocated_resources_table
       a						:Any 							=       maximum_claim_table




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table    )
           for i in range(len(self.__allocated_resources_table[0]    )    )
       ]




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return np.array(self.__claim_vector    ) - np.array(
           self.__processes_resource_summation()    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return [
           list(np.array(self.__maximum_claim_table[i]    ) - np.array(_lowerCamelCase    )    )
           for i, allocated_resource in enumerate(self.__allocated_resources_table    )
       ]




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return {self.__need().index(_lowerCamelCase    ): i for i in self.__need()}




 def        SCREAMING_SNAKE_CASE__				(		self					,  **_lowerCamelCase    ):
       a						:Tuple 							=       self.__need()
       a						:int 							=       self.__allocated_resources_table
       a						:Dict 							=       self.__available_resources()
       a						:str 							=       self.__need_index_manager()
       for kw, val in kwargs.items():
             if kw and val is True:
                   self.__pretty_data()
       print('''_''' * 50 + '''\n'''    )
       while need_list:
             a						:Optional[Any] 							=       False
             for each_need in need_list:
                   a						:int 							=       True
                   for index, need in enumerate(_lowerCamelCase    ):
                         if need > available_resources[index]:
                               a						:List[Any] 							=       False
                               break
                   if execution:
                         a						:Dict 							=       True
                         # get the original index of the process from ind_ctrl db
                         for original_need_index, need_clone in need_index_manager.items():
                               if each_need == need_clone:
                                     a						:int 							=       original_need_index
                         print(F'''Process {process_number + 1} is executing.'''    )
                         # remove the process run from stack
                         need_list.remove(_lowerCamelCase    )
                         # update available/freed resources stack
                         a						:List[Any] 							=       np.array(_lowerCamelCase    ) + np.array(
                             alloc_resources_table[process_number]    )
                         print(
                             '''Updated available resource stack for processes: '''
                             + ''' '''.join([str(_lowerCamelCase    ) for x in available_resources]    )    )
                         break
             if safe:
                   print('''The process is in a safe state.\n'''    )
             else:
                   print('''System in unsafe state. Aborting...\n'''    )
                   break




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       print(''' ''' * 9 + '''Allocated Resource Table'''    )
       for item in self.__allocated_resources_table:
             print(
                 F'''P{self.__allocated_resources_table.index(_lowerCamelCase    ) + 1}'''
                 + ''' '''.join(F'''{it:>8}''' for it in item    )
                 + '''\n'''    )
       print(''' ''' * 9 + '''System Resource Table'''    )
       for item in self.__maximum_claim_table:
             print(
                 F'''P{self.__maximum_claim_table.index(_lowerCamelCase    ) + 1}'''
                 + ''' '''.join(F'''{it:>8}''' for it in item    )
                 + '''\n'''    )
       print(
           '''Current Usage by Active Processes: '''
           + ''' '''.join(str(_lowerCamelCase    ) for x in self.__claim_vector    )    )
       print(
           '''Initial Available Resources:       '''
           + ''' '''.join(str(_lowerCamelCase    ) for x in self.__available_resources()    )    )
       time.sleep(1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
						import torch


if is_vision_available():
						from PIL import Image

						from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       ViTImageProcessor if is_vision_available() else None



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  (3, 32, 128)
					lowerCAmelCase   					=  tempfile.mkdtemp()

					# fmt: off
					lowerCAmelCase   					=  ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
					# fmt: on
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					with open(self.vocab_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write(json.dumps(__lowerCAmelCase) + """\n""")

					lowerCAmelCase   					=  {
					    """do_normalize""": False,
					    """do_resize""": True,
					    """image_processor_type""": """ViTImageProcessor""",
					    """resample""": 3,
					    """size""": {"""height""": 32, """width""": 128},
					}
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    __lowerCAmelCase)
					with open(self.image_processor_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							json.dump(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)

					lowerCAmelCase   					=  Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1))

					return image_input



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)
					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(self.tmpdirname       ,    use_fast=__lowerCAmelCase)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)

					lowerCAmelCase   					=  self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)""")
					lowerCAmelCase   					=  self.get_image_processor(do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(
					    self.tmpdirname       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""np""")

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer(__lowerCAmelCase)
					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

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

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					lowerCAmelCase   					=  processor.char_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  tokenizer.batch_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  [seq.replace(""" """       ,    """""") for seq in decoded_tok]

					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

					self.assertListEqual(list(inputs.keys())       ,    processor.model_input_names)







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    38)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    50257)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    30522)

					lowerCAmelCase   					=  processor.batch_decode([char_input, bpe_input, wp_input])

					self.assertListEqual(list(results.keys())       ,    ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])




