






"""simple docstring"""




import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )






class        UpperCamelCase			(	unittest.TestCase      ):


 def __init__(     self		:						Tuple							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						List[Any]=13							,						UpperCAmelCase__		:						int=7							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						Optional[Any]=True							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Union[str, Any]=True							,						UpperCAmelCase__		:						Optional[Any]=99							,						UpperCAmelCase__		:						Optional[Any]=32							,						UpperCAmelCase__		:						Any=5							,						UpperCAmelCase__		:						Any=4							,						UpperCAmelCase__		:						Any=37							,						UpperCAmelCase__		:						Union[str, Any]="gelu"							,						UpperCAmelCase__		:						Dict=0.1							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Optional[int]=512							,						UpperCAmelCase__		:						Any=16							,						UpperCAmelCase__		:						Optional[Any]=2							,						UpperCAmelCase__		:						Union[str, Any]=0.0_2							,						UpperCAmelCase__		:						List[Any]=4							,						)					->       Dict:
   _a  :       Union[str, Any]             =	parent
   _a  :       Optional[int]             =	batch_size
   _a  :       Dict             =	seq_length
   _a  :       Any             =	is_training
   _a  :       List[str]             =	use_attention_mask
   _a  :       List[Any]             =	use_token_type_ids
   _a  :       int             =	use_labels
   _a  :       Optional[Any]             =	vocab_size
   _a  :       Optional[int]             =	hidden_size
   _a  :       Optional[int]             =	num_hidden_layers
   _a  :       Union[str, Any]             =	num_attention_heads
   _a  :       Dict             =	intermediate_size
   _a  :       Union[str, Any]             =	hidden_act
   _a  :       Dict             =	hidden_dropout_prob
   _a  :       Dict             =	attention_probs_dropout_prob
   _a  :       List[str]             =	max_position_embeddings
   _a  :       Any             =	type_vocab_size
   _a  :       Optional[int]             =	type_sequence_label_size
   _a  :       Union[str, Any]             =	initializer_range
   _a  :       Optional[int]             =	num_choices


 def _lowercase	(     self		:						str							)					->       Union[str, Any]:
   _a  :       Dict             =	ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size							)

   _a  :       int             =	None
   if self.use_attention_mask:
     _a  :       Tuple             =	random_attention_mask([self.batch_size, self.seq_length]							)

   _a  :       Union[str, Any]             =	None
   if self.use_token_type_ids:
     _a  :       Optional[Any]             =	ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size							)

   _a  :       Optional[int]             =	RobertaPreLayerNormConfig(
       vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						is_decoder=__lowercase							,						initializer_range=self.initializer_range							,						)

   return config, input_ids, token_type_ids, attention_mask


 def _lowercase	(     self		:						Dict							)					->       List[Any]:
   _a  :       str             =	self.prepare_config_and_inputs()
   _a  :       List[str]             =	config_and_inputs
   _a  :       int             =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
   return config, inputs_dict







 def _lowercase	(     self		:						Optional[Any]							)					->       str:
   _a  :       Optional[Any]             =	self.prepare_config_and_inputs()
   _a  :       Optional[Any]             =	config_and_inputs

   _a  :       Optional[int]             =	True
   _a  :       str             =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
   _a  :       Dict             =	ids_tensor([self.batch_size, self.seq_length]							,						vocab_size=2							)

   return (
       config,
       input_ids,
       token_type_ids,
       encoder_hidden_states,
       encoder_attention_mask,
   )







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        UpperCamelCase			(	__lowerCamelCase		,     unittest.TestCase      ):
 UpperCamelCase					:       Tuple			=					True

 UpperCamelCase					:       int			=					(
     (
         FlaxRobertaPreLayerNormModel,
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )


 def _lowercase	(     self		:						List[Any]							)					->       List[str]:
   _a  :       List[Any]             =	FlaxRobertaPreLayerNormModelTester(self							)







 @slow
 def _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   for model_class_name in self.all_model_classes:
     _a  :       Union[str, Any]             =	model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,						from_pt=__lowercase							)
     _a  :       Union[str, Any]             =	model(np.ones((1, 1)							)							)
     self.assertIsNotNone(__lowercase							)







@require_flax
class        UpperCamelCase			(	unittest.TestCase      ):


 @slow
 def _lowercase	(     self		:						Union[str, Any]							)					->       Dict:
   _a  :       str             =	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,						from_pt=__lowercase							)

   _a  :       Union[str, Any]             =	np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,						dtype=jnp.intaa							)
   _a  :       Optional[Any]             =	model(__lowercase							)[0]
   _a  :       List[Any]             =	[1, 11, 50265]
   self.assertEqual(list(output.shape							)							,						__lowercase							)
   # compare the actual values for a slice.
   _a  :       Union[str, Any]             =	np.array(
       [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]							,						dtype=np.floataa							)
   self.assertTrue(np.allclose(output[:, :3, :3]							,						__lowercase							,						atol=1E-4							)							)







 @slow
 def _lowercase	(     self		:						int							)					->       Optional[Any]:
   _a  :       str             =	FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,						from_pt=__lowercase							)

   _a  :       List[str]             =	np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,						dtype=jnp.intaa							)
   _a  :       List[Any]             =	model(__lowercase							)[0]
   # compare the actual values for a slice.
   _a  :       Optional[Any]             =	np.array(
       [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]							,						dtype=np.floataa							)
   self.assertTrue(np.allclose(output[:, :3, :3]							,						__lowercase							,						atol=1E-4							)							)






import math
import sys



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	''''''
				try:
								with open(__lowerCAmelCase  ,  '''rb'''      ) as binary_file:
												snake_case__	:      int     		=	binary_file.read()
								for dat in data:
												snake_case__	:      Any     		=	f"""{dat:08b}"""
												result += curr_byte
								return result
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	{'''0''': '''0''', '''1''': '''1'''}
				snake_case__		, snake_case__	:      List[Any]     		=	'''''', ''''''
				snake_case__	:      Tuple     		=	len(__lowerCAmelCase      )

				for i in range(len(__lowerCAmelCase      )      ):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								snake_case__	:      Tuple     		=	lexicon[curr_string]
								result += last_match_id
								snake_case__	:      Any     		=	last_match_id + '''0'''

								if math.loga(__lowerCAmelCase      ).is_integer():
												snake_case__	:      Tuple     		=	{}
												for curr_key in list(__lowerCAmelCase      ):
																snake_case__	:      Union[str, Any]     		=	lexicon.pop(__lowerCAmelCase      )
												snake_case__	:      Optional[Any]     		=	new_lex

								snake_case__	:      Tuple     		=	last_match_id + '''1'''
								index += 1
								snake_case__	:      Dict     		=	''''''
				return result



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Dict     		=	8
				try:
								with open(__lowerCAmelCase  ,  '''wb'''      ) as opened_file:
												snake_case__	:      Union[str, Any]     		=	[
												    to_write[i : i + byte_length]
												    for i in range(0  ,  len(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
												]

												if len(result_byte_array[-1]      ) % byte_length == 0:
																result_byte_array.append('''10000000'''      )
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]      ) - 1
																)

												for elem in result_byte_array[:-1]:
																opened_file.write(int(__lowerCAmelCase  ,  2      ).to_bytes(1  ,  byteorder='''big'''      )      )
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Any     		=	0
				for letter in data_bits:
								if letter == "1":
												break
								counter += 1

				snake_case__	:      Optional[int]     		=	data_bits[counter:]
				snake_case__	:      int     		=	data_bits[counter + 1 :]
				return data_bits



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	read_file_binary(__lowerCAmelCase      )
				snake_case__	:      List[str]     		=	remove_prefix(__lowerCAmelCase      )
				snake_case__	:      Any     		=	decompress_data(__lowerCAmelCase      )
				write_file_binary(__lowerCAmelCase  ,  __lowerCAmelCase      )


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])

