
"""simple docstring"""






from collections.abc import Generator
from math import sin






def 						_snake_case       (	lowercase__		:  Union[str, Any]		)  ->				List[Any]:


			'''simple docstring'''





			if len(_lowercase		) != 3_2:
						raise ValueError("""Input must be of length 32"""		)

			lowerCAmelCase_    :Optional[int]			     =  B''''''
			for i in [3, 2, 1, 0]:
						little_endian += string_aa[8 * i : 8 * i + 8]
			return little_endian






def 						_snake_case       (	lowercase__		:  Union[str, Any]		)  ->				str:


			'''simple docstring'''





			if i < 0:
						raise ValueError("""Input must be non-negative"""		)

			lowerCAmelCase_    :Optional[Any]			     =  format(_lowercase      ,     """08x"""		)[-8:]
			lowerCAmelCase_    :Dict			     =  B''''''
			for i in [3, 2, 1, 0]:
						little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""		)
			return little_endian_hex






def 						_snake_case       (	lowercase__		:  List[str]		)  ->				Any:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  B''''''
			for char in message:
						bit_string += format(_lowercase      ,     """08b"""		).encode("""utf-8"""		)
			lowerCAmelCase_    :List[str]			     =  format(len(_lowercase		)      ,     """064b"""		).encode("""utf-8"""		)

			# Pad bit_string to a multiple of 512 chars
			bit_string += b"1"
			while len(_lowercase		) % 5_1_2 != 4_4_8:
						bit_string += b"0"
			bit_string += to_little_endian(start_len[3_2:]		) + to_little_endian(start_len[:3_2]		)

			return bit_string






def 						_snake_case       (	lowercase__		:  Dict		)  ->				List[str]:


			'''simple docstring'''





			if len(_lowercase		) % 5_1_2 != 0:
						raise ValueError("""Input must have length that\'s a multiple of 512"""		)

			for pos in range(0      ,     len(_lowercase		)      ,     5_1_2		):
						lowerCAmelCase_    :Optional[int]			     =  bit_string[pos : pos + 5_1_2]
						lowerCAmelCase_    :Optional[Any]			     =  []
						for i in range(0      ,     5_1_2      ,     3_2		):
									block_words.append(int(to_little_endian(block[i : i + 3_2]		)      ,     2		)		)
						yield block_words






def 						_snake_case       (	lowercase__		:  str		)  ->				Any:


			'''simple docstring'''





			if i < 0:
						raise ValueError("""Input must be non-negative"""		)

			lowerCAmelCase_    :Optional[int]			     =  format(_lowercase      ,     """032b"""		)
			lowerCAmelCase_    :List[str]			     =  ''''''
			for c in i_str:
						new_str += "1" if c == "0" else "0"
			return int(_lowercase      ,     2		)






def 						_snake_case       (	lowercase__		:  Optional[Any]      ,     lowercase__		:  Union[str, Any]		)  ->				Any:


			'''simple docstring'''





			return (a + b) % 2**3_2






def 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  Optional[Any]		)  ->				int:


			'''simple docstring'''





			if i < 0:
						raise ValueError("""Input must be non-negative"""		)
			if shift < 0:
						raise ValueError("""Shift must be non-negative"""		)
			return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2






def 						_snake_case       (	lowercase__		:  Optional[int]		)  ->				Any:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  preprocess(_lowercase		)

			lowerCAmelCase_    :Optional[Any]			     =  [int(2**3_2 * abs(sin(i + 1		)		)		) for i in range(6_4		)]

			# Starting states
			lowerCAmelCase_    :Tuple			     =  0X6745_2301
			lowerCAmelCase_    :Any			     =  0Xefcd_ab89
			lowerCAmelCase_    :Dict			     =  0X98ba_dcfe
			lowerCAmelCase_    :Any			     =  0X1032_5476

			lowerCAmelCase_    :List[Any]			     =  [
			    7,
			    1_2,
			    1_7,
			    2_2,
			    7,
			    1_2,
			    1_7,
			    2_2,
			    7,
			    1_2,
			    1_7,
			    2_2,
			    7,
			    1_2,
			    1_7,
			    2_2,
			    5,
			    9,
			    1_4,
			    2_0,
			    5,
			    9,
			    1_4,
			    2_0,
			    5,
			    9,
			    1_4,
			    2_0,
			    5,
			    9,
			    1_4,
			    2_0,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    6,
			    1_0,
			    1_5,
			    2_1,
			    6,
			    1_0,
			    1_5,
			    2_1,
			    6,
			    1_0,
			    1_5,
			    2_1,
			    6,
			    1_0,
			    1_5,
			    2_1,
			]

			# Process bit string in chunks, each with 16 32-char words
			for block_words in get_block_words(_lowercase		):
						lowerCAmelCase_    :int			     =  aa
						lowerCAmelCase_    :str			     =  ba
						lowerCAmelCase_    :str			     =  ca
						lowerCAmelCase_    :List[Any]			     =  da

						# Hash current chunk
						for i in range(6_4		):
									if i <= 1_5:
												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
												lowerCAmelCase_    :List[str]			     =  d ^ (b & (c ^ d))
												lowerCAmelCase_    :Optional[int]			     =  i
									elif i <= 3_1:
												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
												lowerCAmelCase_    :Any			     =  c ^ (d & (b ^ c))
												lowerCAmelCase_    :str			     =  (5 * i + 1) % 1_6
									elif i <= 4_7:
												lowerCAmelCase_    :List[Any]			     =  b ^ c ^ d
												lowerCAmelCase_    :List[str]			     =  (3 * i + 5) % 1_6
									else:
												lowerCAmelCase_    :Any			     =  c ^ (b | not_aa(_lowercase		))
												lowerCAmelCase_    :List[str]			     =  (7 * i) % 1_6
									lowerCAmelCase_    :Tuple			     =  (f + a + added_consts[i] + block_words[g]) % 2**3_2
									lowerCAmelCase_    :Union[str, Any]			     =  d
									lowerCAmelCase_    :Union[str, Any]			     =  c
									lowerCAmelCase_    :Optional[int]			     =  b
									lowerCAmelCase_    :int			     =  sum_aa(_lowercase      ,     left_rotate_aa(_lowercase      ,     shift_amounts[i]		)		)

						# Add hashed chunk to running total
						lowerCAmelCase_    :str			     =  sum_aa(_lowercase      ,     _lowercase		)
						lowerCAmelCase_    :Dict			     =  sum_aa(_lowercase      ,     _lowercase		)
						lowerCAmelCase_    :Dict			     =  sum_aa(_lowercase      ,     _lowercase		)
						lowerCAmelCase_    :Optional[Any]			     =  sum_aa(_lowercase      ,     _lowercase		)

			lowerCAmelCase_    :List[str]			     =  reformat_hex(_lowercase		) + reformat_hex(_lowercase		) + reformat_hex(_lowercase		) + reformat_hex(_lowercase		)
			return digest


if __name__ == "__main__":
				import doctest

				doctest.testmod()







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


__UpperCamelCase		:       Optional[Any]									=     ['gpt2']
__UpperCamelCase		:       str									=     'gpt2'

if is_tf_available():





					class 					lowercase__						(   tf.Module):


											def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





																	'''simple docstring'''





																	super().__init__()
																	SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer
																	SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Dict							=							TFGPTaLMHeadModel.from_config(UpperCamelCase__						)



											@tf.function(input_signature=(tf.TensorSpec((None,)    ,		tf.string    ,		name='''text'''						),)						)
											def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.tokenizer(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenized['''input_ids'''].to_tensor()

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

																	SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model(input_ids=UpperCamelCase__    ,		attention_mask=UpperCamelCase__						)['''logits''']

																	return outputs





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


						def        __A       (       self	:				int						):





												'''simple docstring'''





												super().setUp()

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

												SCREAMING_SNAKE_CASE							:    Tuple							=							[
												    '''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''',
												]
												SCREAMING_SNAKE_CASE							:    Dict							=							list(zip(self.test_sentences    ,		self.test_sentences[::-1]						)						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												for tokenizer, tf_tokenizer in zip(self.tokenizers    ,		self.tf_tokenizers						):
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer([test_inputs]    ,		return_tensors='''tf'''						)
																								SCREAMING_SNAKE_CASE							:    Any							=							tf_tokenizer([test_inputs]						)

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

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


						@slow
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.function(UpperCamelCase__						)
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.constant(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[str]							=							compiled_tokenizer(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							tf_tokenizer(UpperCamelCase__						)

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


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





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    str							=							ModelToSave(tokenizer=UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model.serving(UpperCamelCase__						)  # Build model with some sample inputs
																		with TemporaryDirectory() as tempdir:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							Path(UpperCamelCase__						) / '''saved.model'''
																								tf.saved_model.save(UpperCamelCase__    ,		UpperCamelCase__    ,		signatures={'''serving_default''': model.serving}						)
																								SCREAMING_SNAKE_CASE							:    str							=							tf.saved_model.load(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    int							=							loaded_model.signatures['''serving_default'''](UpperCamelCase__						)['''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        __A       (       self	:				List[str]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__						)  # Build model with some sample inputs

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tf_tokenizer.get_config()
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TFGPTaTokenizer.from_config(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							model_from_config(UpperCamelCase__						)

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



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





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		SCREAMING_SNAKE_CASE							:    Tuple							=							12_3123

																		for max_length in [3, 5, 1024]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																								SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__    ,		max_length=UpperCamelCase__						)

																								SCREAMING_SNAKE_CASE							:    Tuple							=							out['''input_ids'''].numpy().shape[1]

																								assert out_length == max_length





