



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        lowerCAmelCase__					(  unittest.TestCase       ):


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


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[int]:
											A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size       )

											A      						=    None
											if self.use_attention_mask:
																		A      						=    random_attention_mask([self.batch_size, self.seq_length]       )

											A      						=    None
											if self.use_token_type_ids:
																		A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size       )

											A      						=    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=__UpperCamelCase						, initializer_range=self.initializer_range						, )

											return config, input_ids, token_type_ids, attention_mask


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[Any]:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A						,   A      						=    config_and_inputs
											A      						=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A						,   A      						=    config_and_inputs

											A      						=    True
											A      						=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
											A      						=    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        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Tuple								=     True

				A_   :     Union[str, Any]								=     (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def __UpperCamelCase						(							self      :  List[str]       )		->     List[str]:
											A      						=    FlaxRobertaPreLayerNormModelTester(self       )


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     Optional[Any]:
											for model_class_name in self.all_model_classes:
																		A      						=    model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )
																		A      						=    model(np.ones((1, 1)       )       )
																		self.assertIsNotNone(__UpperCamelCase       )





@require_flax
class        lowerCAmelCase__					(  unittest.TestCase       ):


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )

											A      						=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						, dtype=jnp.intaa       )
											A      						=    model(__UpperCamelCase       )[0]
											A      						=    [1, 11, 50_265]
											self.assertEqual(list(output.shape       )						, __UpperCamelCase       )
											# compare the actual values for a slice.
											A      						=    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]						, __UpperCamelCase						, atol=1e-4       )       )


				@slow
				def __UpperCamelCase						(							self      :  Dict       )		->     List[str]:
											A      						=    FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )

											A      						=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						, dtype=jnp.intaa       )
											A      						=    model(__UpperCamelCase       )[0]
											# compare the actual values for a slice.
											A      						=    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]						, __UpperCamelCase						, atol=1e-4       )       )




import math
import sys






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    ''
							try:
														with open(lowerCAmelCase__			,	'rb'				) as binary_file:
																					A      						=    binary_file.read()
														for dat in data:
																					A      						=    F'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    {'0': '0', '1': '1'}
							A						,   A      						=    '', ''
							A      						=    len(lowerCAmelCase__				)

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

														A      						=    lexicon[curr_string]
														result += last_match_id
														A      						=    last_match_id + '0'

														if math.loga(lowerCAmelCase__				).is_integer():
																					A      						=    {}
																					for curr_key in list(lowerCAmelCase__				):
																												A      						=    lexicon.pop(lowerCAmelCase__				)
																					A      						=    new_lex

														A      						=    last_match_id + '1'
														index += 1
														A      						=    ''
							return result






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    8
							try:
														with open(lowerCAmelCase__			,	'wb'				) as opened_file:
																					A      						=    [
																					    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				)       ->							str:

							'''simple docstring'''






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

							A      						=    data_bits[counter:]
							A      						=    data_bits[counter + 1 :]
							return data_bits






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    read_file_binary(lowerCAmelCase__				)
							A      						=    remove_prefix(lowerCAmelCase__				)
							A      						=    decompress_data(lowerCAmelCase__				)
							write_file_binary(lowerCAmelCase__			,	lowerCAmelCase__				)


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