



"""simple docstring"""

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
			import tensorflow as tf

if is_keras_nlp_available():
			from transformers.models.gpta import TFGPTaTokenizer


a_			   =					['''gpt2''']
a_			   =					'''gpt2'''

if is_tf_available():







			class    __lowercase     (					tf.Module):







									"""simple docstring"""







									def __init__(self   ,		lowercase__       ):
														super().__init__()
														snake_case_					:		Any 					=					tokenizer
														snake_case_					:		Union[str, Any] 					=					AutoConfig.from_pretrained(lowercase__       )
														snake_case_					:		Union[str, Any] 					=					TFGPTaLMHeadModel.from_config(lowercase__       )







									@tf.function(input_signature=(tf.TensorSpec((None,)   ,		tf.string   ,		name="""text"""       ),)       )
									def 						__UpperCamelCase							(self   ,		lowercase__       ):
														snake_case_					:		str 					=					self.tokenizer(lowercase__       )
														snake_case_					:		str 					=					tokenized["""input_ids"""].to_tensor()

														snake_case_					:		Optional[int] 					=					tf.cast(input_ids_dense > 0   ,		tf.intaa       )
														# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

														snake_case_					:		str 					=					self.model(input_ids=lowercase__   ,		attention_mask=lowercase__       )["""logits"""]

														return outputs







@require_tf
@require_keras_nlp
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self       ):
											super().setUp()

											snake_case_					:		List[Any] 					=					[GPTaTokenizer.from_pretrained(lowercase__       ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
											snake_case_					:		Optional[Any] 					=					[TFGPTaTokenizer.from_pretrained(lowercase__       ) for checkpoint in TOKENIZER_CHECKPOINTS]
											assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

											snake_case_					:		Any 					=					[
											    """This is a straightforward English test sentence.""",
											    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
											    """Now we're going to add some Chinese: 一 二 三 一二三""",
											    """And some much more rare Chinese: 齉 堃 齉堃""",
											    """Je vais aussi écrire en français pour tester les accents""",
											    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
											]
											snake_case_					:		Union[str, Any] 					=					list(zip(self.test_sentences   ,		self.test_sentences[::-1]       )       )







						def 						__UpperCamelCase							(self       ):
											for tokenizer, tf_tokenizer in zip(self.tokenizers   ,		self.tf_tokenizers       ):
																for test_inputs in self.test_sentences:
																					snake_case_					:		List[str] 					=					tokenizer([test_inputs]   ,		return_tensors="""tf"""       )
																					snake_case_					:		Tuple 					=					tf_tokenizer([test_inputs]       )

																					for key in python_outputs.keys():
																										# convert them to numpy to avoid messing with ragged tensors
																										snake_case_					:		Dict 					=					python_outputs[key].numpy()
																										snake_case_					:		Dict 					=					tf_outputs[key].numpy()

																										self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape       )       )
																										self.assertTrue(tf.reduce_all(tf.cast(lowercase__   ,		tf.intaa       ) == tf_outputs_values       )       )







						@slow
						def 						__UpperCamelCase							(self       ):
											for tf_tokenizer in self.tf_tokenizers:
																snake_case_					:		int 					=					tf.function(lowercase__       )
																for test_inputs in self.test_sentences:
																					snake_case_					:		int 					=					tf.constant(lowercase__       )
																					snake_case_					:		str 					=					compiled_tokenizer(lowercase__       )
																					snake_case_					:		Dict 					=					tf_tokenizer(lowercase__       )

																					for key in eager_outputs.keys():
																										self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )







						@slow
						def 						__UpperCamelCase							(self       ):
											for tf_tokenizer in self.tf_tokenizers:
																snake_case_					:		Optional[int] 					=					ModelToSave(tokenizer=lowercase__       )
																snake_case_					:		Optional[Any] 					=					tf.convert_to_tensor([self.test_sentences[0]]       )
																snake_case_					:		Tuple 					=					model.serving(lowercase__       )  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																					snake_case_					:		int 					=					Path(lowercase__       ) / """saved.model"""
																					tf.saved_model.save(lowercase__   ,		lowercase__   ,		signatures={"""serving_default""": model.serving}       )
																					snake_case_					:		Tuple 					=					tf.saved_model.load(lowercase__       )
																snake_case_					:		List[Any] 					=					loaded_model.signatures["""serving_default"""](lowercase__       )["""output_0"""]
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertTrue(tf.reduce_all(out == loaded_output       )       )







						@slow
						def 						__UpperCamelCase							(self       ):
											for tf_tokenizer in self.tf_tokenizers:
																snake_case_					:		List[str] 					=					tf.convert_to_tensor([self.test_sentences[0]]       )
																snake_case_					:		List[Any] 					=					tf_tokenizer(lowercase__       )  # Build model with some sample inputs

																snake_case_					:		Any 					=					tf_tokenizer.get_config()
																snake_case_					:		List[Any] 					=					TFGPTaTokenizer.from_config(lowercase__       )
																snake_case_					:		List[str] 					=					model_from_config(lowercase__       )

																for key in from_config_output.keys():
																					self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]       )       )







						@slow
						def 						__UpperCamelCase							(self       ):
											for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																snake_case_					:		Tuple 					=					12_31_23

																for max_length in [3, 5, 10_24]:
																					snake_case_					:		Tuple 					=					tf.convert_to_tensor([self.test_sentences[0]]       )
																					snake_case_					:		List[Any] 					=					tf_tokenizer(lowercase__   ,		max_length=lowercase__       )

																					snake_case_					:		Union[str, Any] 					=					out["""input_ids"""].numpy().shape[1]

																					assert out_length == max_length









"""simple docstring"""

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__       ):
											snake_case_					:		Union[str, Any] 					=					data
											snake_case_					:		List[str] 					=					[0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]







						@staticmethod
						def 						__UpperCamelCase							(lowercase__   ,		lowercase__       ):
											return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					B"""\x80""" + B"""\x00""" * (63 - (len(self.data       ) + 8) % 64)
											snake_case_					:		Tuple 					=					self.data + padding + struct.pack(""">Q"""   ,		8 * len(self.data       )       )
											return padded_data







						def 						__UpperCamelCase							(self       ):
											return [
											    self.padded_data[i : i + 64] for i in range(0   ,		len(self.padded_data       )   ,		64       )
											]







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					list(struct.unpack(""">16L"""   ,		lowercase__       )       ) + [0] * 64
											for i in range(16   ,		80       ):
																snake_case_					:		Dict 					=					self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])   ,		1       )
											return w







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.padding()
											snake_case_					:		Any 					=					self.split_blocks()
											for block in self.blocks:
																snake_case_					:		Any 					=					self.expand_block(lowercase__       )
																snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		List[Any] 					=					self.h
																for i in range(0   ,		80       ):
																					if 0 <= i < 20:
																										snake_case_					:		Optional[Any] 					=					(b & c) | ((~b) & d)
																										snake_case_					:		List[str] 					=					0X5_a_8_2_7_9_9_9
																					elif 20 <= i < 40:
																										snake_case_					:		Union[str, Any] 					=					b ^ c ^ d
																										snake_case_					:		Tuple 					=					0X6_e_d_9_e_b_a_1
																					elif 40 <= i < 60:
																										snake_case_					:		str 					=					(b & c) | (b & d) | (c & d)
																										snake_case_					:		List[str] 					=					0X8_f_1_b_b_c_d_c
																					elif 60 <= i < 80:
																										snake_case_					:		Tuple 					=					b ^ c ^ d
																										snake_case_					:		str 					=					0Xc_a_6_2_c_1_d_6
																					snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Optional[Any] 					=					(
																					    self.rotate(lowercase__   ,		5       ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
																					    a,
																					    self.rotate(lowercase__   ,		30       ),
																					    c,
																					    d,
																					)
																snake_case_					:		Any 					=					(
																    self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
																    self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
																    self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
																    self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
																    self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
																)
											return ("{:08x}" * 5).format(*self.h       )







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					b"""Test String"""
					assert SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE__			).hexdigest()  # noqa: S324







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		int 					=					argparse.ArgumentParser(description="""Process some strings or files"""			)
					parser.add_argument(
					    """--string"""				,    dest="""input_string"""				,    default="""Hello World!! Welcome to Cryptography"""				,    help="""Hash the string"""				,    )
					parser.add_argument("""--file"""				,    dest="""input_file"""				,    help="""Hash contents of a file"""			)
					snake_case_					:		Optional[int] 					=					parser.parse_args()
					snake_case_					:		Optional[int] 					=					args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file				,    """rb"""			) as f:
															snake_case_					:		List[str] 					=					f.read()
					else:
										snake_case_					:		Dict 					=					bytes(SCREAMING_SNAKE_CASE__				,    """utf-8"""			)
					print(SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash()			)


if __name__ == "__main__":
			main()
			import doctest

			doctest.testmod()




