

import argparse
import struct
import unittest







class 			lowercase    :




				def __init__( self				,       snake_case	):
											snake_case_          =   data

											# Initialize hash values
											snake_case_          =   [
											    0x6A09E667,
											    0xBB67AE85,
											    0x3C6EF372,
											    0xA54FF53A,
											    0x510E527F,
											    0x9B05688C,
											    0x1F83D9AB,
											    0x5BE0CD19,
											]

											# Initialize round constants
											snake_case_          =   [
											    0x428A2F98,
											    0x71374491,
											    0xB5C0FBCF,
											    0xE9B5DBA5,
											    0x3956C25B,
											    0x59F111F1,
											    0x923F82A4,
											    0xAB1C5ED5,
											    0xD807AA98,
											    0x12835B01,
											    0x243185BE,
											    0x550C7DC3,
											    0x72BE5D74,
											    0x80DEB1FE,
											    0x9BDC06A7,
											    0xC19BF174,
											    0xE49B69C1,
											    0xEFBE4786,
											    0x0FC19DC6,
											    0x240CA1CC,
											    0x2DE92C6F,
											    0x4A7484AA,
											    0x5CB0A9DC,
											    0x76F988DA,
											    0x983E5152,
											    0xA831C66D,
											    0xB00327C8,
											    0xBF597FC7,
											    0xC6E00BF3,
											    0xD5A79147,
											    0x06CA6351,
											    0x14292967,
											    0x27B70A85,
											    0x2E1B2138,
											    0x4D2C6DFC,
											    0x53380D13,
											    0x650A7354,
											    0x766A0ABB,
											    0x81C2C92E,
											    0x92722C85,
											    0xA2BFE8A1,
											    0xA81A664B,
											    0xC24B8B70,
											    0xC76C51A3,
											    0xD192E819,
											    0xD6990624,
											    0xF40E3585,
											    0x106AA070,
											    0x19A4C116,
											    0x1E376C08,
											    0x2748774C,
											    0x34B0BCB5,
											    0x391C0CB3,
											    0x4ED8AA4A,
											    0x5B9CCA4F,
											    0x682E6FF3,
											    0x748F82EE,
											    0x78A5636F,
											    0x84C87814,
											    0x8CC70208,
											    0x90BEFFFA,
											    0xA4506CEB,
											    0xBEF9A3F7,
											    0xC67178F2,
											]

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




				@staticmethod
				def    a							( snake_case	):
											snake_case_          =   b'\x80' + (b'\x00' * (63 - (len(snake_case	) + 8) % 64))
											snake_case_          =   struct.pack('>Q'				,       (len(snake_case	) * 8)	)
											return data + padding + big_endian_integer




				def    a							( self	):
											# Convert into blocks of 64 bytes
											snake_case_          =   [
											    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
																		snake_case_          =   list(struct.unpack('>16L'				,       snake_case	)	)
																		# add 48 0-ed integers
																		words += [0] * 48

																		snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_          =   self.hashes

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

																																snake_case_          =   (
																																    words[index - 16] + sa + words[index - 7] + sa
																																) % 0x100000000

																									# Compression
																									snake_case_          =   self.ror(snake_case				,       6	) ^ self.ror(snake_case				,       11	) ^ self.ror(snake_case				,       25	)
																									snake_case_          =   (e & f) ^ ((~e & 0xFFFFFFFF) & g)
																									snake_case_          =   (
																									    h + sa + ch + self.round_constants[index] + words[index]
																									) % 0x100000000
																									snake_case_          =   self.ror(snake_case				,       2	) ^ self.ror(snake_case				,       13	) ^ self.ror(snake_case				,       22	)
																									snake_case_          =   (a & b) ^ (a & c) ^ (b & c)
																									snake_case_          =   (sa + maj) % 0x100000000

																									snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_          =   (
																									    g,
																									    f,
																									    e,
																									    ((d + tempa) % 0x100000000),
																									    c,
																									    b,
																									    a,
																									    ((tempa + tempa) % 0x100000000),
																									)

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

																		# Modify final values
																		snake_case_          =   [
																		    ((element + mutated_hash_values[index]) % 0x100000000)
																		    for index, element in enumerate(self.hashes	)
																		]

											snake_case_          =   ''.join([hex(snake_case	)[2:].zfill(8	) for value in self.hashes]	)




				def    a							( self				,       snake_case				,       snake_case	):
											return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)







class 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											import hashlib

											snake_case_          =   bytes('Test String'				,       'utf-8'	)
											self.assertEqual(SHAaaa(snake_case	).hash				,       hashlib.shaaaa(snake_case	).hexdigest()	)





def  __lowerCamelCase     (			):







							'''simple docstring'''






							import doctest

							doctest.testmod()

							snake_case_          =   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'				)

							snake_case_          =   parser.parse_args()

							snake_case_          =   args.input_string

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

							print(SHAaaa(UpperCamelCase__				).hash				)


if __name__ == "__main__":
					main()







import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    XLMForMultipleChoice,
					    XLMForQuestionAnswering,
					    XLMForQuestionAnsweringSimple,
					    XLMForSequenceClassification,
					    XLMForTokenClassification,
					    XLMModel,
					    XLMWithLMHeadModel,
					)
					from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=13				,       snake_case=7				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=False				,       snake_case=False				,       snake_case=False				,       snake_case=2				,       snake_case=99				,       snake_case=0				,       snake_case=32				,       snake_case=5				,       snake_case=4				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=512				,       snake_case=2				,       snake_case=0.02				,       snake_case=2				,       snake_case=4				,       snake_case="last"				,       snake_case=True				,       snake_case=None				,       snake_case=0				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   seq_length
											snake_case_          =   is_training
											snake_case_          =   use_input_lengths
											snake_case_          =   use_token_type_ids
											snake_case_          =   use_labels
											snake_case_          =   gelu_activation
											snake_case_          =   sinusoidal_embeddings
											snake_case_          =   causal
											snake_case_          =   asm
											snake_case_          =   n_langs
											snake_case_          =   vocab_size
											snake_case_          =   n_special
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   max_position_embeddings
											snake_case_          =   type_sequence_label_size
											snake_case_          =   initializer_range
											snake_case_          =   num_labels
											snake_case_          =   num_choices
											snake_case_          =   summary_type
											snake_case_          =   use_proj
											snake_case_          =   scope
											snake_case_          =   bos_token_id




				def    a							( self	):
											snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size	)
											snake_case_          =   random_attention_mask([self.batch_size, self.seq_length]	)

											snake_case_          =   None
											if self.use_input_lengths:
																		snake_case_          =   (
																		    ids_tensor([self.batch_size]				,       vocab_size=2	) + self.seq_length - 2
																		)  # small variation of seq_length

											snake_case_          =   None
											if self.use_token_type_ids:
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.n_langs	)

											snake_case_          =   None
											snake_case_          =   None
											snake_case_          =   None
											if self.use_labels:
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.type_sequence_label_size	)
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels	)
																		snake_case_          =   ids_tensor([self.batch_size]				,       2	).float()
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.num_choices	)

											snake_case_          =   self.get_config()

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_lengths,
											    sequence_labels,
											    token_labels,
											    is_impossible_labels,
											    choice_labels,
											    input_mask,
											)




				def    a							( self	):
											return XLMConfig(
											    vocab_size=self.vocab_size				,       n_special=self.n_special				,       emb_dim=self.hidden_size				,       n_layers=self.num_hidden_layers				,       n_heads=self.num_attention_heads				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       gelu_activation=self.gelu_activation				,       sinusoidal_embeddings=self.sinusoidal_embeddings				,       asm=self.asm				,       causal=self.causal				,       n_langs=self.n_langs				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       summary_type=self.summary_type				,       use_proj=self.use_proj				,       num_labels=self.num_labels				,       bos_token_id=self.bos_token_id				,       )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMModel(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   model(snake_case				,       lengths=snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case	)
											self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMWithLMHeadModel(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       token_type_ids=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnsweringSimple(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)
											snake_case_          =   outputs
											self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)	)
											self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnswering(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       p_mask=snake_case				,       )

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       )

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											self.parent.assertEqual(result_with_labels.loss.shape				,       ()	)
											self.parent.assertEqual(result.start_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(result.start_top_index.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(
											    result.end_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(
											    result.end_top_index.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(result.cls_logits.shape				,       (self.batch_size,)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForSequenceClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)
											snake_case_          =   model(snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_labels
											snake_case_          =   XLMForTokenClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       attention_mask=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_choices
											snake_case_          =   XLMForMultipleChoice(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   input_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   token_type_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   input_mask.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   model(
											    snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=snake_case				,       )
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)	)




				def    a							( self	):
											snake_case_          =   self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   
											)          =   config_and_inputs
											snake_case_          =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
											return config, inputs_dict







@require_torch
class 			lowercase    (	lowercase_     ,  lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      (
				    (
				        XLMModel,
				        XLMWithLMHeadModel,
				        XLMForQuestionAnswering,
				        XLMForSequenceClassification,
				        XLMForQuestionAnsweringSimple,
				        XLMForTokenClassification,
				        XLMForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE :			Tuple	      =      (
				    (XLMWithLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				__SCREAMING_SNAKE_CASE :			int	      =      (
				    {
				        '''feature-extraction''': XLMModel,
				        '''fill-mask''': XLMWithLMHeadModel,
				        '''question-answering''': XLMForQuestionAnsweringSimple,
				        '''text-classification''': XLMForSequenceClassification,
				        '''text-generation''': XLMWithLMHeadModel,
				        '''token-classification''': XLMForTokenClassification,
				        '''zero-shot''': XLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											if (
											    pipeline_test_casse_name == "QAPipelineTests"
											    and tokenizer_name is not None
											    and not tokenizer_name.endswith('Fast'	)
											):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

											return False




				def    a							( self				,       snake_case				,       snake_case				,       snake_case=False	):
											snake_case_          =   super()._prepare_for_class(snake_case				,       snake_case				,       return_labels=snake_case	)

											if return_labels:
																		if model_class.__name__ == "XLMForQuestionAnswering":
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)

											return inputs_dict




				def    a							( self	):
											snake_case_          =   XLMModelTester(self	)
											snake_case_          =   ConfigTester(self				,       config_class=snake_case				,       emb_dim=37	)




				def    a							( self	):
											self.config_tester.run_common_tests()




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_model(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_lm_head(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_simple_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_sequence_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_token_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_attentions in attentions]				,       [True] * len(snake_case	)	)
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_attentions in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   min_length + idx + 1

																		snake_case_          =   (
																		    batch_size * num_beam_groups,
																		    config.num_attention_heads,
																		    tgt_len,
																		    src_len,
																		)
																		# check attn size
																		self.assertListEqual(
																		    [layer_attention.shape for layer_attention in iter_attentions]				,       [expected_shape] * len(snake_case	)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_hidden_states in hidden_states]				,       [True] * len(snake_case	)				,       )
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_hidden_states in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
																		# check hidden size
																		self.assertListEqual(
																		    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,       [expected_shape] * len(snake_case	)				,       )
											pass




				@slow
				def    a							( self	):
											for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_          =   XLMModel.from_pretrained(snake_case	)
																		self.assertIsNotNone(snake_case	)







@require_torch
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'	)
											model.to(snake_case	)
											snake_case_          =   torch.tensor([[14, 447]]				,       dtype=torch.long				,       device=snake_case	)  # the president
											snake_case_          =   [
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											]  # the president the president the president the president the president the president the president the president the president the president
											# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
											snake_case_          =   model.generate(snake_case				,       do_sample=snake_case	)
											self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,       snake_case	)




