



import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image
else:






				class     __a			:



					@staticmethod
					def 		UpperCamelCase				(    *snake_case_				:     Any					,						**snake_case_				:     str)->				int:
										pass






@is_pipeline_test
@require_vision
class     __a			(  unittest.TestCase					):



	@require_torch
	def 		UpperCamelCase				(    self				:     Dict)->				List[str]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						)
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""a""", """b""", """c"""])

						# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
						# python and torch versions.
						self.assertIn(
						    nested_simplify(snake_case_)					,						[
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}],
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """c"""}, {"""score""": 0.3_3_3, """label""": """b"""}],
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



	@require_tf
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						framework="""tf""")
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""a""", """b""", """c"""])

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



	@slow
	@require_torch
	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 =pipeline(
						    task="""zero-shot-image-classification"""					,						model="""openai/clip-vit-base-patch32"""					,						)
						# This is an image of 2 cats with remotes and no planes
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)



	@slow
	@require_tf
	def 		UpperCamelCase				(    self				:     Optional[int])->				int:
						__lowerCAmelCase						 =pipeline(
						    task="""zero-shot-image-classification"""					,						model="""openai/clip-vit-base-patch32"""					,						framework="""tf""")
						# This is an image of 2 cats with remotes and no planes
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)









from __future__ import annotations

import time

import numpy as np

lowercase_													=      [8, 5, 9, 7]
lowercase_													=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase_													=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class     __a			:



	def __init__(    self				:     Tuple					,						snake_case_				:     list[int]					,						snake_case_				:     list[list[int]]					,						snake_case_				:     list[list[int]]					,						)->				None:
						__lowerCAmelCase						 =claim_vector
						__lowerCAmelCase						 =allocated_resources_table
						__lowerCAmelCase						 =maximum_claim_table



	def 		UpperCamelCase				(    self				:     Optional[int])->				list[int]:
						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table)
						    for i in range(len(self.__allocated_resources_table[0]))
						]



	def 		UpperCamelCase				(    self				:     Dict)->				list[int]:
						return np.array(self.__claim_vector) - np.array(
						    self.__processes_resource_summation())



	def 		UpperCamelCase				(    self				:     Optional[Any])->				list[list[int]]:
						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]



	def 		UpperCamelCase				(    self				:     Dict)->				dict[int, list[int]]:
						return {self.__need().index(snake_case_): i for i in self.__need()}



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						**snake_case_				:     Optional[Any])->				None:
						__lowerCAmelCase						 =self.__need()
						__lowerCAmelCase						 =self.__allocated_resources_table
						__lowerCAmelCase						 =self.__available_resources()
						__lowerCAmelCase						 =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:
											__lowerCAmelCase						 =False
											for each_need in need_list:
																__lowerCAmelCase						 =True
																for index, need in enumerate(snake_case_):
																					if need > available_resources[index]:
																										__lowerCAmelCase						 =False
																										break
																if execution:
																					__lowerCAmelCase						 =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:
																															__lowerCAmelCase						 =original_need_index
																					print(F"""Process {process_number + 1} is executing.""")
																					# remove the process run from stack
																					need_list.remove(snake_case_)
																					# update available/freed resources stack
																					__lowerCAmelCase						 =np.array(snake_case_) + np.array(
																					    alloc_resources_table[process_number])
																					print(
																					    """Updated available resource stack for processes: """
																					    + """ """.join([str(snake_case_) 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 		UpperCamelCase				(    self				:     List[str])->				Optional[Any]:
						print(""" """ * 9 + """Allocated Resource Table""")
						for item in self.__allocated_resources_table:
											print(
											    F"""P{self.__allocated_resources_table.index(snake_case_) + 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(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(
						    """Current Usage by Active Processes: """
						    + """ """.join(str(snake_case_) for x in self.__claim_vector))
						print(
						    """Initial Available Resources:       """
						    + """ """.join(str(snake_case_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




