



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFResNetForImageClassification, TFResNetModel
							from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class   __lowerCAmelCase  :
					"""simple docstring"""






					def __init__(  self       :							List[Any]					,       _snake_case       :							Optional[Any]					,       _snake_case       :							Optional[int]=3					,       _snake_case       :							Union[str, Any]=32					,       _snake_case       :							Optional[int]=3					,       _snake_case       :							List[Any]=10					,       _snake_case       :							Union[str, Any]=[10, 20, 30, 40]					,       _snake_case       :							int=[1, 1, 2, 1]					,       _snake_case       :							Union[str, Any]=True					,       _snake_case       :							Union[str, Any]=True					,       _snake_case       :							Optional[Any]="relu"					,       _snake_case       :							Dict=3					,       _snake_case       :							Optional[int]=None					,       ):






								"""simple docstring"""


								A__	  =     parent
								A__	  =     batch_size
								A__	  =     image_size
								A__	  =     num_channels
								A__	  =     embeddings_size
								A__	  =     hidden_sizes
								A__	  =     depths
								A__	  =     is_training
								A__	  =     use_labels
								A__	  =     hidden_act
								A__	  =     num_labels
								A__	  =     scope
								A__	  =     len(_snake_case       )



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

								A__	  =     None
								if self.use_labels:
											A__	  =     ids_tensor([self.batch_size]					,       self.num_labels       )

								A__	  =     self.get_config()

								return config, pixel_values, labels



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								return ResNetConfig(
								    num_channels=self.num_channels					,       embeddings_size=self.embeddings_size					,       hidden_sizes=self.hidden_sizes					,       depths=self.depths					,       hidden_act=self.hidden_act					,       num_labels=self.num_labels					,       image_size=self.image_size					,       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Tuple					,       _snake_case       :							str					,       _snake_case       :							str       ):






								"""simple docstring"""


								A__	  =     TFResNetModel(config=_snake_case       )
								A__	  =     model(_snake_case       )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,       )



					def      _a				(  self       :							List[str]					,       _snake_case       :							Optional[int]					,       _snake_case       :							Optional[Any]					,       _snake_case       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.num_labels
								A__	  =     TFResNetForImageClassification(_snake_case       )
								A__	  =     model(_snake_case					,       labels=_snake_case       )
								self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     self.prepare_config_and_inputs()
								A__  ,			A__  ,			A__	  =     config_and_inputs
								A__	  =     {'pixel_values': pixel_values}
								return config, inputs_dict







@require_tf
class   __lowerCAmelCase  (		UpperCAmelCase_				,  UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Any      							=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
					A__					:		List[str]      							=     (
					    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__					:		List[str]      							=     False
					A__					:		str      							=     False
					A__					:		List[Any]      							=     False
					A__					:		Tuple      							=     False
					A__					:		str      							=     False



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     TFResNetModelTester(self       )
								A__	  =     ConfigTester(self					,       config_class=_snake_case					,       has_text_modality=_snake_case       )



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								return



					@unittest.skip(reason='ResNet does not use inputs_embeds'       )
					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ResNet does not support input and output embeddings'       )
					def      _a				(  self       :							str       ):






								"""simple docstring"""


								pass



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__	  =     model_class(_snake_case       )
											A__	  =     inspect.signature(model.call       )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											A__	  =     [*signature.parameters.keys()]

											A__	  =     ['pixel_values']
											self.assertListEqual(arg_names[:1]					,       _snake_case       )



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								def check_hidden_states_output(_snake_case       :							Dict					,       _snake_case       :							List[Any]					,       _snake_case       :							str       ):
											A__	  =     model_class(_snake_case       )
											A__	  =     model(**self._prepare_for_class(_snake_case					,       _snake_case       )       )

											A__	  =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											A__	  =     self.model_tester.num_stages
											self.assertEqual(len(_snake_case       )					,       expected_num_stages + 1       )

											# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]       )					,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()
								A__	  =     ['basic', 'bottleneck']
								for model_class in self.all_model_classes:
											for layer_type in layers_type:
														A__	  =     layer_type
														A__	  =     True
														check_hidden_states_output(_snake_case					,       _snake_case					,       _snake_case       )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														A__	  =     True

														check_hidden_states_output(_snake_case					,       _snake_case					,       _snake_case       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case       )



					@slow
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__	  =     TFResNetModel.from_pretrained(_snake_case       )
											self.assertIsNotNone(_snake_case       )







def A    (							)    ->   str:
			A__	  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
			return image






@require_tf
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					@cached_property
					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
								    if is_vision_available()
								    else None
								)



					@slow
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

								A__	  =     self.default_image_processor
								A__	  =     prepare_img()
								A__	  =     image_processor(images=_snake_case					,       return_tensors='tf'       )

								# forward pass
								A__	  =     model(**_snake_case       )

								# verify the logits
								A__	  =     tf.TensorShape((1, 10_00)       )
								self.assertEqual(outputs.logits.shape					,       _snake_case       )

								A__	  =     tf.constant([-11.1069, -9.7877, -8.3777]       )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()					,       _snake_case					,       atol=1E-4       )       )






import argparse
import struct
import unittest





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase	)							-> None:
							lowerCAmelCase_         =      data

							# Initialize hash values
							lowerCAmelCase_         =      [
							    0x6_A_0_9_E_6_6_7,
							    0xB_B_6_7_A_E_8_5,
							    0x3_C_6_E_F_3_7_2,
							    0xA_5_4_F_F_5_3_A,
							    0x5_1_0_E_5_2_7_F,
							    0x9_B_0_5_6_8_8_C,
							    0x1_F_8_3_D_9_A_B,
							    0x5_B_E_0_C_D_1_9,
							]

							# Initialize round constants
							lowerCAmelCase_         =      [
							    0x4_2_8_A_2_F_9_8,
							    0x7_1_3_7_4_4_9_1,
							    0xB_5_C_0_F_B_C_F,
							    0xE_9_B_5_D_B_A_5,
							    0x3_9_5_6_C_2_5_B,
							    0x5_9_F_1_1_1_F_1,
							    0x9_2_3_F_8_2_A_4,
							    0xA_B_1_C_5_E_D_5,
							    0xD_8_0_7_A_A_9_8,
							    0x1_2_8_3_5_B_0_1,
							    0x2_4_3_1_8_5_B_E,
							    0x5_5_0_C_7_D_C_3,
							    0x7_2_B_E_5_D_7_4,
							    0x8_0_D_E_B_1_F_E,
							    0x9_B_D_C_0_6_A_7,
							    0xC_1_9_B_F_1_7_4,
							    0xE_4_9_B_6_9_C_1,
							    0xE_F_B_E_4_7_8_6,
							    0x0_F_C_1_9_D_C_6,
							    0x2_4_0_C_A_1_C_C,
							    0x2_D_E_9_2_C_6_F,
							    0x4_A_7_4_8_4_A_A,
							    0x5_C_B_0_A_9_D_C,
							    0x7_6_F_9_8_8_D_A,
							    0x9_8_3_E_5_1_5_2,
							    0xA_8_3_1_C_6_6_D,
							    0xB_0_0_3_2_7_C_8,
							    0xB_F_5_9_7_F_C_7,
							    0xC_6_E_0_0_B_F_3,
							    0xD_5_A_7_9_1_4_7,
							    0x0_6_C_A_6_3_5_1,
							    0x1_4_2_9_2_9_6_7,
							    0x2_7_B_7_0_A_8_5,
							    0x2_E_1_B_2_1_3_8,
							    0x4_D_2_C_6_D_F_C,
							    0x5_3_3_8_0_D_1_3,
							    0x6_5_0_A_7_3_5_4,
							    0x7_6_6_A_0_A_B_B,
							    0x8_1_C_2_C_9_2_E,
							    0x9_2_7_2_2_C_8_5,
							    0xA_2_B_F_E_8_A_1,
							    0xA_8_1_A_6_6_4_B,
							    0xC_2_4_B_8_B_7_0,
							    0xC_7_6_C_5_1_A_3,
							    0xD_1_9_2_E_8_1_9,
							    0xD_6_9_9_0_6_2_4,
							    0xF_4_0_E_3_5_8_5,
							    0x1_0_6_A_A_0_7_0,
							    0x1_9_A_4_C_1_1_6,
							    0x1_E_3_7_6_C_0_8,
							    0x2_7_4_8_7_7_4_C,
							    0x3_4_B_0_B_C_B_5,
							    0x3_9_1_C_0_C_B_3,
							    0x4_E_D_8_A_A_4_A,
							    0x5_B_9_C_C_A_4_F,
							    0x6_8_2_E_6_F_F_3,
							    0x7_4_8_F_8_2_E_E,
							    0x7_8_A_5_6_3_6_F,
							    0x8_4_C_8_7_8_1_4,
							    0x8_C_C_7_0_2_0_8,
							    0x9_0_B_E_F_F_F_A,
							    0xA_4_5_0_6_C_E_B,
							    0xB_E_F_9_A_3_F_7,
							    0xC_6_7_1_7_8_F_2,
							]

							lowerCAmelCase_         =      self.preprocessing(self.data	)
							self.final_hash()




				@staticmethod
				def 	__a		(   _UpperCamelCase	)							-> bytes:
							lowerCAmelCase_         =      B"\x80" + (B"\x00" * (63 - (len(_UpperCamelCase	) + 8) % 64))
							lowerCAmelCase_         =      struct.pack(">Q"							,							(len(_UpperCamelCase	) * 8)	)
							return data + padding + big_endian_integer




				def 	__a		(   self	)							-> None:
							# Convert into blocks of 64 bytes
							lowerCAmelCase_         =      [
							    self.preprocessed_data[x : x + 64]
							    for x in range(0							,							len(self.preprocessed_data	)							,							64	)
							]

							for block in self.blocks:
										# Convert the given block into a list of 4 byte integers
										lowerCAmelCase_         =      list(struct.unpack(">16L"							,							_UpperCamelCase	)	)
										# add 48 0-ed integers
										words += [0] * 48

										lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      self.hashes

										for index in range(0							,							64	):
													if index > 15:
																# modify the zero-ed indexes at the end of the array
																lowerCAmelCase_         =      (
																    self.ror(words[index - 15]							,							7	)
																    ^ self.ror(words[index - 15]							,							18	)
																    ^ (words[index - 15] >> 3)
																)
																lowerCAmelCase_         =      (
																    self.ror(words[index - 2]							,							17	)
																    ^ self.ror(words[index - 2]							,							19	)
																    ^ (words[index - 2] >> 10)
																)

																lowerCAmelCase_         =      (
																    words[index - 16] + sa + words[index - 7] + sa
																) % 0x1_0_0_0_0_0_0_0_0

													# Compression
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							6	) ^ self.ror(_UpperCamelCase							,							11	) ^ self.ror(_UpperCamelCase							,							25	)
													lowerCAmelCase_         =      (e & f) ^ ((~e & 0xF_F_F_F_F_F_F_F) & g)
													lowerCAmelCase_         =      (
													    h + sa + ch + self.round_constants[index] + words[index]
													) % 0x1_0_0_0_0_0_0_0_0
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							2	) ^ self.ror(_UpperCamelCase							,							13	) ^ self.ror(_UpperCamelCase							,							22	)
													lowerCAmelCase_         =      (a & b) ^ (a & c) ^ (b & c)
													lowerCAmelCase_         =      (sa + maj) % 0x1_0_0_0_0_0_0_0_0

													lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      (
													    g,
													    f,
													    e,
													    ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
													    c,
													    b,
													    a,
													    ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
													)

										lowerCAmelCase_         =      [a, b, c, d, e, f, g, h]

										# Modify final values
										lowerCAmelCase_         =      [
										    ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
										    for index, element in enumerate(self.hashes	)
										]

							lowerCAmelCase_         =      "".join([hex(_UpperCamelCase	)[2:].zfill(8	) for value in self.hashes]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							return 0xF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)





class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> None:
							import hashlib

							lowerCAmelCase_         =      bytes("Test String"							,							"utf-8"	)
							self.assertEqual(SHAaaa(_UpperCamelCase	).hash							,							hashlib.shaaaa(_UpperCamelCase	).hexdigest()	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


			import doctest

			doctest.testmod()

			lowerCAmelCase_         =      argparse.ArgumentParser()
			parser.add_argument(
			    "-s"       ,				"--string"       ,				dest="input_string"       ,				default="Hello World!! Welcome to Cryptography"       ,				help="Hash the string"       ,				)
			parser.add_argument(
			    "-f"       ,				"--file"       ,				dest="input_file"       ,				help="Hash contents of a file"							)

			lowerCAmelCase_         =      parser.parse_args()

			lowerCAmelCase_         =      args.input_string

			# hash input should be a bytestring
			if args.input_file:
						with open(args.input_file       ,				"rb"							) as f:
									lowerCAmelCase_         =      f.read()
			else:
						lowerCAmelCase_         =      bytes(__lowerCAmelCase       ,				"utf-8"							)

			print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
			main()



