


'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )




@require_tf
class 			lowerCamelCase_   :
      lowerCAmelCase__		     =     XGLMConfig
      lowerCAmelCase__		     =     {}
      lowerCAmelCase__		     =     """gelu"""






      def __init__(					self				:    Dict						,						_A				:    List[Any]						,						_A				:    int=14						,						_A				:    Dict=7						,						_A				:    List[Any]=True						,						_A				:    List[Any]=True						,						_A				:    Union[str, Any]=True						,						_A				:    Optional[int]=99						,						_A				:    Optional[Any]=32						,						_A				:    Dict=2						,						_A				:    Optional[int]=4						,						_A				:    Optional[int]=37						,						_A				:    Dict="gelu"						,						_A				:    Optional[Any]=0.1						,						_A				:    List[Any]=0.1						,						_A				:    List[str]=512						,						_A				:    Optional[int]=0.0_2						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				parent
          UpperCAmelCase__			:		Dict													=				batch_size
          UpperCAmelCase__			:		Tuple													=				seq_length
          UpperCAmelCase__			:		int													=				is_training
          UpperCAmelCase__			:		int													=				use_input_mask
          UpperCAmelCase__			:		List[str]													=				use_labels
          UpperCAmelCase__			:		Union[str, Any]													=				vocab_size
          UpperCAmelCase__			:		Union[str, Any]													=				d_model
          UpperCAmelCase__			:		Tuple													=				num_hidden_layers
          UpperCAmelCase__			:		Optional[int]													=				num_attention_heads
          UpperCAmelCase__			:		Optional[Any]													=				ffn_dim
          UpperCAmelCase__			:		Any													=				activation_function
          UpperCAmelCase__			:		Tuple													=				activation_dropout
          UpperCAmelCase__			:		Union[str, Any]													=				attention_dropout
          UpperCAmelCase__			:		int													=				max_position_embeddings
          UpperCAmelCase__			:		List[str]													=				initializer_range
          UpperCAmelCase__			:		Any													=				None
          UpperCAmelCase__			:		Optional[int]													=				0
          UpperCAmelCase__			:		int													=				2
          UpperCAmelCase__			:		int													=				1






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )






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


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				tf.clip_by_value(
              ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size )						,						clip_value_min=0						,						clip_value_max=3 )

          UpperCAmelCase__			:		Any													=				None
          if self.use_input_mask:
              UpperCAmelCase__			:		Tuple													=				random_attention_mask([self.batch_size, self.seq_length] )

          UpperCAmelCase__			:		List[Any]													=				self.get_config()

          UpperCAmelCase__			:		Tuple													=				floats_tensor([self.num_hidden_layers, self.num_attention_heads]						,						2 )

          return (
              config,
              input_ids,
              input_mask,
              head_mask,
          )






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          return XGLMConfig(
              vocab_size=self.vocab_size						,						d_model=self.hidden_size						,						num_layers=self.num_hidden_layers						,						attention_heads=self.num_attention_heads						,						ffn_dim=self.ffn_dim						,						activation_function=self.activation_function						,						activation_dropout=self.activation_dropout						,						attention_dropout=self.attention_dropout						,						max_position_embeddings=self.max_position_embeddings						,						initializer_range=self.initializer_range						,						use_cache=__snake_case						,						bos_token_id=self.bos_token_id						,						eos_token_id=self.eos_token_id						,						pad_token_id=self.pad_token_id						,						return_dict=__snake_case						,						)






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


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.prepare_config_and_inputs()

          (
              UpperCAmelCase__       
          )			:		Union[str, Any]													=				config_and_inputs

          UpperCAmelCase__			:		Dict													=				{
              """input_ids""": input_ids,
              """head_mask""": head_mask,
          }

          return config, inputs_dict




@require_tf
class 			lowerCamelCase_   (	_snake_case     ,       _snake_case     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
      lowerCAmelCase__		     =     (TFXGLMForCausalLM,) if is_tf_available() else ()
      lowerCAmelCase__		     =     (
          {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
      )
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				TFXGLMModelTester(self )
          UpperCAmelCase__			:		Any													=				ConfigTester(self						,						config_class=__snake_case						,						n_embd=37 )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__			:		Union[str, Any]													=				TFXGLMModel.from_pretrained(__snake_case )
              self.assertIsNotNone(__snake_case )






      @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          super().test_resize_token_embeddings()




@require_tf
class 			lowerCamelCase_   (	unittest.TestCase      ):






      @slow
      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    int=True ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
          UpperCAmelCase__			:		Dict													=				tf.convert_to_tensor([[2, 268, 9_865]]						,						dtype=tf.intaa )  # The dog
          # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
          # fmt: off
          UpperCAmelCase__			:		Dict													=				[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
          # fmt: on
          UpperCAmelCase__			:		int													=				model.generate(__snake_case						,						do_sample=__snake_case						,						num_beams=1 )
          if verify_outputs:
              self.assertListEqual(output_ids[0].numpy().tolist()						,						__snake_case )






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


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
          UpperCAmelCase__			:		Optional[int]													=				TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )

          tf.random.set_seed(0 )
          UpperCAmelCase__			:		int													=				tokenizer('''Today is a nice day and'''						,						return_tensors='''tf''' )
          UpperCAmelCase__			:		Optional[Any]													=				tokenized.input_ids
          # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
          with tf.device(''':/CPU:0''' ):
              UpperCAmelCase__			:		Dict													=				model.generate(__snake_case						,						do_sample=__snake_case						,						seed=[7, 0] )
          UpperCAmelCase__			:		Optional[int]													=				tokenizer.decode(output_ids[0]						,						skip_special_tokens=__snake_case )

          UpperCAmelCase__			:		Optional[Any]													=				(
              """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
          )
          self.assertEqual(__snake_case						,						__snake_case )






      @slow
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
          UpperCAmelCase__			:		Any													=				XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )

          UpperCAmelCase__			:		Dict													=				"""left"""

          # use different length sentences to test batching
          UpperCAmelCase__			:		int													=				[
              """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
              """left-padding, such as in batched generation. The output for the sequence below should be the same """
              """regardless of whether left padding is applied or not. When""",
              """Hello, my dog is a little""",
          ]

          UpperCAmelCase__			:		str													=				tokenizer(__snake_case						,						return_tensors='''tf'''						,						padding=__snake_case )
          UpperCAmelCase__			:		List[str]													=				inputs["""input_ids"""]

          UpperCAmelCase__			:		Union[str, Any]													=				model.generate(input_ids=__snake_case						,						attention_mask=inputs['''attention_mask''']						,						max_new_tokens=12 )

          UpperCAmelCase__			:		Optional[Any]													=				tokenizer(sentences[0]						,						return_tensors='''tf''' ).input_ids
          UpperCAmelCase__			:		List[Any]													=				model.generate(input_ids=__snake_case						,						max_new_tokens=12 )

          UpperCAmelCase__			:		int													=				tokenizer(sentences[1]						,						return_tensors='''tf''' ).input_ids
          UpperCAmelCase__			:		Dict													=				model.generate(input_ids=__snake_case						,						max_new_tokens=12 )

          UpperCAmelCase__			:		str													=				tokenizer.batch_decode(__snake_case						,						skip_special_tokens=__snake_case )
          UpperCAmelCase__			:		Optional[int]													=				tokenizer.decode(output_non_padded[0]						,						skip_special_tokens=__snake_case )
          UpperCAmelCase__			:		List[str]													=				tokenizer.decode(output_padded[0]						,						skip_special_tokens=__snake_case )

          UpperCAmelCase__			:		Optional[int]													=				[
              """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
              """left-padding, such as in batched generation. The output for the sequence below should be the same """
              """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
              """a single""",
              """Hello, my dog is a little bit of a shy one, but he is very friendly""",
          ]
          self.assertListEqual(__snake_case						,						__snake_case )
          self.assertListEqual(__snake_case						,						[non_padded_sentence, padded_sentence] )




'''simple docstring'''


import argparse
import struct
import unittest





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      data

											# Initialize hash values
											_SCREAMING_SNAKE_CASE :       Tuple     		=      [
											    0X6A09_E667,
											    0XBB67_AE85,
											    0X3C6E_F372,
											    0XA54F_F53A,
											    0X510E_527F,
											    0X9B05_688C,
											    0X1F83_D9AB,
											    0X5BE0_CD19,
											]

											# Initialize round constants
											_SCREAMING_SNAKE_CASE :       int     		=      [
											    0X428A_2F98,
											    0X7137_4491,
											    0XB5C0_FBCF,
											    0XE9B5_DBA5,
											    0X3956_C25B,
											    0X59F1_11F1,
											    0X923F_82A4,
											    0XAB1C_5ED5,
											    0XD807_AA98,
											    0X1283_5B01,
											    0X2431_85BE,
											    0X550C_7DC3,
											    0X72BE_5D74,
											    0X80DE_B1FE,
											    0X9BDC_06A7,
											    0XC19B_F174,
											    0XE49B_69C1,
											    0XEFBE_4786,
											    0X0FC1_9DC6,
											    0X240C_A1CC,
											    0X2DE9_2C6F,
											    0X4A74_84AA,
											    0X5CB0_A9DC,
											    0X76F9_88DA,
											    0X983E_5152,
											    0XA831_C66D,
											    0XB003_27C8,
											    0XBF59_7FC7,
											    0XC6E0_0BF3,
											    0XD5A7_9147,
											    0X06CA_6351,
											    0X1429_2967,
											    0X27B7_0A85,
											    0X2E1B_2138,
											    0X4D2C_6DFC,
											    0X5338_0D13,
											    0X650A_7354,
											    0X766A_0ABB,
											    0X81C2_C92E,
											    0X9272_2C85,
											    0XA2BF_E8A1,
											    0XA81A_664B,
											    0XC24B_8B70,
											    0XC76C_51A3,
											    0XD192_E819,
											    0XD699_0624,
											    0XF40E_3585,
											    0X106A_A070,
											    0X19A4_C116,
											    0X1E37_6C08,
											    0X2748_774C,
											    0X34B0_BCB5,
											    0X391C_0CB3,
											    0X4ED8_AA4A,
											    0X5B9C_CA4F,
											    0X682E_6FF3,
											    0X748F_82EE,
											    0X78A5_636F,
											    0X84C8_7814,
											    0X8CC7_0208,
											    0X90BE_FFFA,
											    0XA450_6CEB,
											    0XBEF9_A3F7,
											    0XC671_78F2,
											]

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.preprocessing(self.data  )
											self.final_hash()






							@staticmethod
							def UpperCAmelCase_	(    __snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      B"""\x80""" + (B"""\x00""" * (63 - (len(__snake_case  ) + 8) % 64))
											_SCREAMING_SNAKE_CASE :       List[str]     		=      struct.pack(""">Q"""    ,					(len(__snake_case  ) * 8)  )
											return data + padding + big_endian_integer






							def UpperCAmelCase_	(    self  ):
											# Convert into blocks of 64 bytes
											_SCREAMING_SNAKE_CASE :       Any     		=      [
											    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
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      list(struct.unpack(""">16L"""    ,					__snake_case  )  )
															# add 48 0-ed integers
															words += [0] * 48

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.hashes

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

																							_SCREAMING_SNAKE_CASE :       Tuple     		=      (
																							    words[index - 16] + sa + words[index - 7] + sa
																							) % 0X1_0000_0000

																			# Compression
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.ror(__snake_case    ,					6  ) ^ self.ror(__snake_case    ,					11  ) ^ self.ror(__snake_case    ,					25  )
																			_SCREAMING_SNAKE_CASE :       str     		=      (e & f) ^ ((~e & 0XFFFF_FFFF) & g)
																			_SCREAMING_SNAKE_CASE :       str     		=      (
																			    h + sa + ch + self.round_constants[index] + words[index]
																			) % 0X1_0000_0000
																			_SCREAMING_SNAKE_CASE :       Dict     		=      self.ror(__snake_case    ,					2  ) ^ self.ror(__snake_case    ,					13  ) ^ self.ror(__snake_case    ,					22  )
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      (a & b) ^ (a & c) ^ (b & c)
																			_SCREAMING_SNAKE_CASE :       Dict     		=      (sa + maj) % 0X1_0000_0000

																			_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Dict     		=      (
																			    g,
																			    f,
																			    e,
																			    ((d + tempa) % 0X1_0000_0000),
																			    c,
																			    b,
																			    a,
																			    ((tempa + tempa) % 0X1_0000_0000),
																			)

															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      [a, b, c, d, e, f, g, h]

															# Modify final values
															_SCREAMING_SNAKE_CASE :       Tuple     		=      [
															    ((element + mutated_hash_values[index]) % 0X1_0000_0000)
															    for index, element in enumerate(self.hashes  )
															]

											_SCREAMING_SNAKE_CASE :       Dict     		=      """""".join([hex(__snake_case  )[2:].zfill(8  ) for value in self.hashes]  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											return 0XFFFF_FFFF & (value << (32 - rotations)) | (value >> rotations)





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											import hashlib

											_SCREAMING_SNAKE_CASE :       Tuple     		=      bytes("""Test String"""    ,					"""utf-8"""  )
											self.assertEqual(SHAaaa(__snake_case  ).hash    ,					hashlib.shaaaa(__snake_case  ).hexdigest()  )







def 			snake_case_   (  ):







				"""simple docstring"""





				import doctest

				doctest.testmod()

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      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"""     )

				_SCREAMING_SNAKE_CASE :       Dict     		=      parser.parse_args()

				_SCREAMING_SNAKE_CASE :       Tuple     		=      args.input_string

				# hash input should be a bytestring
				if args.input_file:
								with open(args.input_file							,      """rb"""     ) as f:
												_SCREAMING_SNAKE_CASE :       str     		=      f.read()
				else:
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      bytes(SCREAMING_SNAKE_CASE__							,      """utf-8"""     )

				print(SHAaaa(SCREAMING_SNAKE_CASE__     ).hash     )


if __name__ == "__main__":
				main()




