"""simple docstring"""


import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      ""
       SCREAMING_SNAKE_CASE_			=      (
           None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
       )
       SCREAMING_SNAKE_CASE_			=      None  # compression type in fsspec. ex: "gzip"
       SCREAMING_SNAKE_CASE_			=      None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







       def __init__( self,							lowerCAmelCase__ = "",							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							**lowerCAmelCase__)      ->      Optional[Any]:
            super().__init__(self,							**lowerCAmelCase__)
            # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
            snake_case_					       =					fsspec.open(
                lowerCAmelCase__,							mode='rb',							protocol=lowerCAmelCase__,							compression=self.compression,							client_kwargs={
                    'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
                    'trust_env': True,  # Enable reading proxy env variables.
                    **(target_options or {}).pop('client_kwargs',							{}),  # To avoid issues if it was already passed.
                },							**(target_options or {}),							)
            snake_case_					       =					os.path.basename(self.file.path.split('::')[0])
            snake_case_					       =					(
                self.compressed_name[: self.compressed_name.rindex('.')]
                if '''.''' in self.compressed_name
                else self.compressed_name
            )
            snake_case_					       =					None







       @classmethod
       def 		a_						( cls,							lowerCAmelCase__)      ->      Tuple:
            # compressed file paths are always relative to the archive root
            return super()._strip_protocol(lowerCAmelCase__).lstrip('/')







       def 		a_						( self)      ->      Any:
            if self.dir_cache is None:
                 snake_case_					       =					{**self.file.fs.info(self.file.path), '''name''': self.uncompressed_name}
                 snake_case_					       =					{f['''name''']: f}







       def 		a_						( self,							lowerCAmelCase__)      ->      Tuple:
            return self.file.open().read()







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = "rb",							lowerCAmelCase__=None,							lowerCAmelCase__=True,							lowerCAmelCase__=None,							**lowerCAmelCase__,							)      ->      List[Any]:
            snake_case_					       =					self._strip_protocol(lowerCAmelCase__)
            if mode != "rb":
                 raise ValueError(f'Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'')
            return self.file.open()




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "bz2"
       SCREAMING_SNAKE_CASE_			=      "bz2"
       SCREAMING_SNAKE_CASE_			=      ".bz2"




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "gzip"
       SCREAMING_SNAKE_CASE_			=      "gzip"
       SCREAMING_SNAKE_CASE_			=      ".gz"




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "lz4"
       SCREAMING_SNAKE_CASE_			=      "lz4"
       SCREAMING_SNAKE_CASE_			=      ".lz4"




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "xz"
       SCREAMING_SNAKE_CASE_			=      "xz"
       SCREAMING_SNAKE_CASE_			=      ".xz"




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "zstd"
       SCREAMING_SNAKE_CASE_			=      "zstd"
       SCREAMING_SNAKE_CASE_			=      ".zst"







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__ = "rb",							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = DEFAULT_BLOCK_SIZE,							**lowerCAmelCase__,							)      ->      str:
            super().__init__(
                fo=lowerCAmelCase__,							mode=lowerCAmelCase__,							target_protocol=lowerCAmelCase__,							target_options=lowerCAmelCase__,							block_size=lowerCAmelCase__,							**lowerCAmelCase__,							)
            # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
            #
            # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
            #     out.close = close
            # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
            #
            # see https://github.com/intake/filesystem_spec/issues/725
            snake_case_					       =					self.file.__enter__




            class  UpperCamelCase					:







                   def __init__( self,							lowerCAmelCase__)      ->      Optional[Any]:
                        snake_case_					       =					file_







                   def __enter__( self)      ->      List[str]:
                        self._file.__enter__()
                        return self







                   def __exit__( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      Union[str, Any]:
                        self._file.__exit__(*lowerCAmelCase__,							**lowerCAmelCase__)







                   def __iter__( self)      ->      Any:
                        return iter(self._file)







                   def 		a_						( self)      ->      List[str]:
                        return next(self._file)







                   def __getattr__( self,							lowerCAmelCase__)      ->      List[Any]:
                        return getattr(self._file,							lowerCAmelCase__)

            def fixed_enter(*lowerCAmelCase__,							**lowerCAmelCase__):
                 return WrappedFile(_enter(*lowerCAmelCase__,							**lowerCAmelCase__))

            snake_case_					       =					fixed_enter






"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


