

import math
import sys





def  _snake_case     (    UpperCAmelCase_  :					str			):
     A__										=  """"""
     try:
          with open(UpperCAmelCase_					,   """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  _snake_case     (    UpperCAmelCase_  :					str			):
     A__										=  {"""0""": """0""", """1""": """1"""}
     A__			,				A__										=  """""", """"""
     A__										=  len(UpperCAmelCase_			)

     for i in range(len(UpperCAmelCase_			)			):
          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(UpperCAmelCase_			).is_integer():
               A__										=  {}
               for curr_key in list(UpperCAmelCase_			):
                    A__										=  lexicon.pop(UpperCAmelCase_			)
               A__										=  new_lex

          A__										=  last_match_id + """1"""
          index += 1
          A__										=  """"""
     return result





def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					str			):
     A__										=  8
     try:
          with open(UpperCAmelCase_					,   """wb"""			) as opened_file:
               A__										=  [
                   to_write[i : i + byte_length]
                   for i in range(0					,   len(UpperCAmelCase_			)					,   UpperCAmelCase_			)
               ]

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





def  _snake_case     (    UpperCAmelCase_  :					str			):
     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  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					str			):
     A__										=  read_file_binary(UpperCAmelCase_			)
     A__										=  remove_prefix(UpperCAmelCase_			)
     A__										=  decompress_data(UpperCAmelCase_			)
     write_file_binary(UpperCAmelCase_					,   UpperCAmelCase_			)


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









"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
 import torch



@is_pipeline_test
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         A__										=  TextaTextGenerationPipeline(model=UpperCamelCase    ,						tokenizer=UpperCamelCase       )
         return generator, ["Something to write", "Something else"]

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

         A__										=  generator(["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         A__										=  generator(
             ["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						batch_size=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         with self.assertRaises(UpperCamelCase       ):
              generator(4       )

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""pt"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
             ]    ,						)





    @require_tf
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






