




from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel





@require_tf
class 			lowerCamelCase_				:


 '''simple docstring'''







 lowercase_							       =       BlenderbotConfig
 lowercase_							       =       {}
 lowercase_							       =       "gelu"



 def __init__(				self    :						List[str]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						List[str]=13							,							_lowerCAmelCase    :						Tuple=7							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						Any=False							,							_lowerCAmelCase    :						Dict=99							,							_lowerCAmelCase    :						Any=32							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Tuple=4							,							_lowerCAmelCase    :						List[str]=37							,							_lowerCAmelCase    :						Optional[Any]=0.1							,							_lowerCAmelCase    :						Dict=0.1							,							_lowerCAmelCase    :						int=20							,							_lowerCAmelCase    :						List[str]=2							,							_lowerCAmelCase    :						List[str]=1							,							_lowerCAmelCase    :						List[Any]=0							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     seq_length
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     use_labels
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     hidden_size
       SCREAMING_SNAKE_CASE_      		=     num_hidden_layers
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     intermediate_size

       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     max_position_embeddings
       SCREAMING_SNAKE_CASE_      		=     eos_token_id
       SCREAMING_SNAKE_CASE_      		=     pad_token_id
       SCREAMING_SNAKE_CASE_      		=     bos_token_id



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size		)
       SCREAMING_SNAKE_CASE_      		=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)							,							1		)
       SCREAMING_SNAKE_CASE_      		=     tf.concat([input_ids, eos_tensor]							,							axis=1		)

       SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size		)

       SCREAMING_SNAKE_CASE_      		=     self.config_cls(
           vocab_size=self.vocab_size							,							d_model=self.hidden_size							,							encoder_layers=self.num_hidden_layers							,							decoder_layers=self.num_hidden_layers							,							encoder_attention_heads=self.num_attention_heads							,							decoder_attention_heads=self.num_attention_heads							,							encoder_ffn_dim=self.intermediate_size							,							decoder_ffn_dim=self.intermediate_size							,							dropout=self.hidden_dropout_prob							,							attention_dropout=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							eos_token_ids=[2]							,							bos_token_id=self.bos_token_id							,							pad_token_id=self.pad_token_id							,							decoder_start_token_id=self.pad_token_id							,							**self.config_updates							,							)
       SCREAMING_SNAKE_CASE_      		=     prepare_blenderbot_inputs_dict(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       return config, inputs_dict




 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     TFBlenderbotModel(config=_lowerCAmelCase		).get_decoder()
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['input_ids']

       SCREAMING_SNAKE_CASE_      		=     input_ids[:1, :]
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['attention_mask'][:1, :]
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['head_mask']
       SCREAMING_SNAKE_CASE_      		=     1

       # first forward pass
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							use_cache=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((self.batch_size, 3)							,							config.vocab_size		)
       SCREAMING_SNAKE_CASE_      		=     tf.cast(ids_tensor((self.batch_size, 3)							,							2		)							,							tf.inta		)

       # append to next input_ids and
       SCREAMING_SNAKE_CASE_      		=     tf.concat([input_ids, next_tokens]							,							axis=-1		)
       SCREAMING_SNAKE_CASE_      		=     tf.concat([attention_mask, next_attn_mask]							,							axis=-1		)

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							past_key_values=_lowerCAmelCase		)[0]

       self.parent.assertEqual(next_tokens.shape[1]							,							output_from_past.shape[1]		)

       # select random slice
       SCREAMING_SNAKE_CASE_      		=     int(ids_tensor((1,)							,							output_from_past.shape[-1]		)		)
       SCREAMING_SNAKE_CASE_      		=     output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE_      		=     output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(_lowerCAmelCase							,							_lowerCAmelCase							,							rtol=1E-3		)


def    UpperCAmelCase_				( __UpperCAmelCase		:      int     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Optional[Any]=None     ,     __UpperCAmelCase		:      List[Any]=None     ,     __UpperCAmelCase		:      str=None     ,     __UpperCAmelCase		:      Optional[Any]=None     ,     __UpperCAmelCase		:      Dict=None     ,     )   ->					str:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.cast(tf.math.not_equal(__UpperCAmelCase     ,     config.pad_token_id				)     ,     tf.inta				)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape     ,     dtype=tf.inta				),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,     config.pad_token_id				)     ,     tf.inta				),
                ]     ,     axis=-1     ,     )
      if head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }





@require_tf
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 lowercase_							       =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 lowercase_							       =       (
     {
         "conversational": TFBlenderbotForConditionalGeneration,
         "feature-extraction": TFBlenderbotModel,
         "summarization": TFBlenderbotForConditionalGeneration,
         "text2text-generation": TFBlenderbotForConditionalGeneration,
         "translation": TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 lowercase_							       =       True
 lowercase_							       =       False
 lowercase_							       =       False



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     TFBlenderbotModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						str		):
       self.config_tester.run_common_tests()




 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)





@require_tokenizers
@require_tf
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       ["My friends are cool but they eat too many carbs."]
 lowercase_							       =       "facebook/blenderbot-400M-distill"



 @cached_property
 def  lowerCAmelCase_    (				self    :						List[str]		):
       return BlenderbotTokenizer.from_pretrained(self.model_name		)



 @cached_property
 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
       return model




 @slow
 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer(self.src_text							,							return_tensors='tf'		)
       SCREAMING_SNAKE_CASE_      		=     self.model.generate(
           model_inputs.input_ids							,							)
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer.batch_decode(generated_ids.numpy()							,							skip_special_tokens=_lowerCAmelCase		)[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )

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()



