


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDebertaVaForMaskedLM,
        TFDebertaVaForQuestionAnswering,
        TFDebertaVaForSequenceClassification,
        TFDebertaVaForTokenClassification,
        TFDebertaVaModel,
    )







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Any					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Any=13					,      __lowerCamelCase							:			Union[str, Any]=7					,      __lowerCamelCase							:			Dict=True					,      __lowerCamelCase							:			Optional[int]=True					,      __lowerCamelCase							:			Dict=True					,      __lowerCamelCase							:			int=True					,      __lowerCamelCase							:			str=99					,      __lowerCamelCase							:			Tuple=32					,      __lowerCamelCase							:			int=2					,      __lowerCamelCase							:			Dict=4					,      __lowerCamelCase							:			int=37					,      __lowerCamelCase							:			Optional[int]="gelu"					,      __lowerCamelCase							:			Union[str, Any]=0.1					,      __lowerCamelCase							:			int=0.1					,      __lowerCamelCase							:			Any=512					,      __lowerCamelCase							:			List[Any]=16					,      __lowerCamelCase							:			List[Any]=2					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			Any=False					,      __lowerCamelCase							:			Any=True					,      __lowerCamelCase							:			Any="None"					,      __lowerCamelCase							:			List[Any]=3					,      __lowerCamelCase							:			Any=4					,      __lowerCamelCase							:			int=None					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  parent
             lowerCamelCase__		:				List[Any]    =  batch_size
             lowerCamelCase__		:				Union[str, Any]    =  seq_length
             lowerCamelCase__		:				List[str]    =  is_training
             lowerCamelCase__		:				Tuple    =  use_input_mask
             lowerCamelCase__		:				str    =  use_token_type_ids
             lowerCamelCase__		:				int    =  use_labels
             lowerCamelCase__		:				str    =  vocab_size
             lowerCamelCase__		:				Union[str, Any]    =  hidden_size
             lowerCamelCase__		:				Any    =  num_hidden_layers
             lowerCamelCase__		:				Tuple    =  num_attention_heads
             lowerCamelCase__		:				Dict    =  intermediate_size
             lowerCamelCase__		:				str    =  hidden_act
             lowerCamelCase__		:				Tuple    =  hidden_dropout_prob
             lowerCamelCase__		:				Union[str, Any]    =  attention_probs_dropout_prob
             lowerCamelCase__		:				int    =  max_position_embeddings
             lowerCamelCase__		:				Union[str, Any]    =  type_vocab_size
             lowerCamelCase__		:				List[str]    =  type_sequence_label_size
             lowerCamelCase__		:				Optional[Any]    =  initializer_range
             lowerCamelCase__		:				List[Any]    =  num_labels
             lowerCamelCase__		:				Tuple    =  num_choices
             lowerCamelCase__		:				List[Any]    =  relative_attention
             lowerCamelCase__		:				Dict    =  position_biased_input
             lowerCamelCase__		:				Dict    =  pos_att_type
             lowerCamelCase__		:				Tuple    =  scope





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size       )

             lowerCamelCase__		:				Optional[Any]    =  None
             if self.use_input_mask:
                   lowerCamelCase__		:				int    =  random_attention_mask([self.batch_size, self.seq_length]       )

             lowerCamelCase__		:				List[str]    =  None
             if self.use_token_type_ids:
                   lowerCamelCase__		:				Tuple    =  ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size       )

             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  None
             lowerCamelCase__		:				List[str]    =  None
             if self.use_labels:
                   lowerCamelCase__		:				Optional[Any]    =  ids_tensor([self.batch_size]					,      self.type_sequence_label_size       )
                   lowerCamelCase__		:				int    =  ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels       )

             lowerCamelCase__		:				int    =  DebertaVaConfig(
                 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					,      relative_attention=self.relative_attention					,      position_biased_input=self.position_biased_input					,      initializer_range=self.initializer_range					,      return_dict=__lowerCamelCase					,      )

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  TFDebertaVaModel(config=__lowerCamelCase       )
             lowerCamelCase__		:				Any    =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

             lowerCamelCase__		:				List[str]    =  [input_ids, input_mask]
             lowerCamelCase__		:				List[str]    =  model(__lowerCamelCase       )

             lowerCamelCase__		:				str    =  model(__lowerCamelCase       )

             self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  TFDebertaVaForMaskedLM(config=__lowerCamelCase       )
             lowerCamelCase__		:				Optional[int]    =  {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
             }
             lowerCamelCase__		:				Union[str, Any]    =  model(__lowerCamelCase       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.num_labels
             lowerCamelCase__		:				Any    =  TFDebertaVaForSequenceClassification(config=__lowerCamelCase       )
             lowerCamelCase__		:				Dict    =  {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
             }

             lowerCamelCase__		:				List[str]    =  model(__lowerCamelCase       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)       )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.num_labels
             lowerCamelCase__		:				Optional[Any]    =  TFDebertaVaForTokenClassification(config=__lowerCamelCase       )
             lowerCamelCase__		:				Optional[Any]    =  {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
             }
             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)       )





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  TFDebertaVaForQuestionAnswering(config=__lowerCamelCase       )
             lowerCamelCase__		:				Dict    =  {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
             }

             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase       )
             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 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )    ,   (
                 lowerCamelCase__
             )    ,   (
                 lowerCamelCase__
             )    ,   (
                 lowerCamelCase__
             )    ,   (
                 lowerCamelCase__
             )    ,   (
                 lowerCamelCase__
             )    ,   (
                 lowerCamelCase__
             )    ,   
             )		:				Union[str, Any]    =  config_and_inputs
             lowerCamelCase__		:				Tuple    =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict







@require_tf
class 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(
           (
               TFDebertaVaModel,
               TFDebertaVaForMaskedLM,
               TFDebertaVaForQuestionAnswering,
               TFDebertaVaForSequenceClassification,
               TFDebertaVaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       A__											=			(
           {
               "feature-extraction": TFDebertaVaModel,
               "fill-mask": TFDebertaVaForMaskedLM,
               "question-answering": TFDebertaVaForQuestionAnswering,
               "text-classification": TFDebertaVaForSequenceClassification,
               "token-classification": TFDebertaVaForTokenClassification,
               "zero-shot": TFDebertaVaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       A__											=			False
       A__											=			False





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  TFDebertaVaModelTester(self       )
             lowerCamelCase__		:				int    =  ConfigTester(self					,      config_class=__lowerCamelCase					,      hidden_size=37       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase       )





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


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase       )





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


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"       )
             self.assertIsNotNone(__lowerCamelCase       )







@require_tf
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       @unittest.skip(reason="Model not available yet"       )
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             pass





       @slow
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"       )
             lowerCamelCase__		:				int    =  tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       )
             lowerCamelCase__		:				Optional[Any]    =  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
             lowerCamelCase__		:				Tuple    =  model(__lowerCamelCase					,      attention_mask=__lowerCamelCase       )[0]

             lowerCamelCase__		:				Tuple    =  tf.constant(
                 [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]       )
             tf.debugging.assert_near(output[:, 1:4, 1:4]					,      __lowerCamelCase					,      atol=1E-4       )






import math
import os
import sys

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  ""
      try:
            with open(_A    ,					"rb"						) as binary_file:
                  lowerCamelCase__		:				Union[str, Any]    =  binary_file.read()
            for dat in data:
                  lowerCamelCase__		:				Optional[Any]    =  F"{dat:08b}"
                  result += curr_byte
            return result
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    dict[str, str]    ,					_A			:    str    ,					_A			:    int    ,					_A			:    str						):

      """simple docstring"""




      lexicon.pop(_A						)
      lowerCamelCase__		:				List[Any]    =  last_match_id

      if math.loga(_A						).is_integer():
            for curr_key in lexicon:
                  lowerCamelCase__		:				Any    =  "0" + lexicon[curr_key]

      lowerCamelCase__		:				Tuple    =  bin(_A						)[2:]

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  {"0": "0", "1": "1"}
      lowerCamelCase__    ,   lowerCamelCase__		:				int    =  "", ""
      lowerCamelCase__		:				int    =  len(_A						)

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

            lowerCamelCase__		:				Optional[Any]    =  lexicon[curr_string]
            result += last_match_id
            add_key_to_lexicon(_A    ,					_A    ,					_A    ,					_A						)
            index += 1
            lowerCamelCase__		:				Dict    =  ""

      while curr_string != "" and curr_string not in lexicon:
            curr_string += "0"

      if curr_string != "":
            lowerCamelCase__		:				int    =  lexicon[curr_string]
            result += last_match_id

      return result

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  os.path.getsize(_A						)
      lowerCamelCase__		:				Dict    =  bin(_A						)[2:]
      lowerCamelCase__		:				List[Any]    =  len(_A						)

      return "0" * (length_length - 1) + file_length_binary + compressed

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  8
      try:
            with open(_A    ,					"wb"						) as opened_file:
                  lowerCamelCase__		:				Any    =  [
                      to_write[i : i + byte_length]
                      for i in range(0    ,					len(_A						)    ,					_A						)
                  ]

                  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:
                        opened_file.write(int(_A    ,					2						).to_bytes(1    ,					byteorder="big"						)						)
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  read_file_binary(_A						)
      lowerCamelCase__		:				List[Any]    =  compress_data(_A						)
      lowerCamelCase__		:				str    =  add_file_length(_A    ,					_A						)
      write_file_binary(_A    ,					_A						)


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


