

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 A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
      _lowerCAmelCase   :					Optional[int]        =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A_       )
      _lowerCAmelCase   :					str        =    -1

      _lowerCAmelCase   :					Optional[Any]        =    ids_tensor((1, 5)     ,						vocab_size=model.config.vocab_size       ).to(A_       )
      _lowerCAmelCase   :					Tuple        =    model.generate(A_     ,						max_new_tokens=1_0     ,						do_sample=A_       )
      _lowerCAmelCase   :					int        =    tokenizer.decode(greedy_ids[0]       )

      with CaptureStdout() as cs:
           _lowerCAmelCase   :					int        =    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
      _lowerCAmelCase   :					Union[str, Any]        =    cs.out[:-1]

      self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
      _lowerCAmelCase   :					Union[str, Any]        =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A_       )
      _lowerCAmelCase   :					Optional[int]        =    -1

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

      _lowerCAmelCase   :					int        =    TextIteratorStreamer(A_       )
      _lowerCAmelCase   :					int        =    {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
      _lowerCAmelCase   :					str        =    Thread(target=model.generate     ,						kwargs=A_       )
      thread.start()
      _lowerCAmelCase   :					Any        =    ""
      for new_text in streamer:
           streamer_text += new_text

      self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
      _lowerCAmelCase   :					List[str]        =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A_       )
      _lowerCAmelCase   :					List[str]        =    -1

      _lowerCAmelCase   :					str        =    ids_tensor((1, 5)     ,						vocab_size=model.config.vocab_size       ).to(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    model.generate(A_     ,						max_new_tokens=1_0     ,						do_sample=A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    greedy_ids[:, input_ids.shape[1] :]
      _lowerCAmelCase   :					Dict        =    tokenizer.decode(new_greedy_ids[0]       )

      with CaptureStdout() as cs:
           _lowerCAmelCase   :					Optional[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
      _lowerCAmelCase   :					Tuple        =    cs.out[:-1]

      self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    AutoTokenizer.from_pretrained("distilgpt2"       )
      _lowerCAmelCase   :					Tuple        =    AutoModelForCausalLM.from_pretrained("distilgpt2"       ).to(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    -1

      _lowerCAmelCase   :					List[Any]        =    torch.ones((1, 5)     ,						device=A_       ).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
           _lowerCAmelCase   :					Union[str, 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
      _lowerCAmelCase   :					Optional[Any]        =    cs.out[:-1]  # Remove the final "\n"
      _lowerCAmelCase   :					Union[str, Any]        =    tokenizer(A_     ,						return_tensors="pt"       )
      self.assertEqual(streamer_text_tokenized.input_ids.shape     ,						(1, 1)       )


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
      _lowerCAmelCase   :					Dict        =    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(A_       )
      _lowerCAmelCase   :					List[Any]        =    -1

      _lowerCAmelCase   :					Union[str, Any]        =    ids_tensor((1, 5)     ,						vocab_size=model.config.vocab_size       ).to(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    TextIteratorStreamer(A_     ,						timeout=0.001       )
      _lowerCAmelCase   :					Union[str, Any]        =    {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
      _lowerCAmelCase   :					List[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_       ):
           _lowerCAmelCase   :					str        =    ""
           for new_text in streamer:
                streamer_text += new_text









from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[str]      =						''''''
 _lowercase     :					Optional[Any]      =						'''hf-legacy'''  # "hf://"" is reserved for hffs

 def __init__(		self :	Dict     ,						A_ :	Optional[DatasetInfo] = None     ,						A_ :	Optional[str] = None     ,						**A_ :	int     ,						):






      '''simple docstring'''




      super().__init__(self     ,						**A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    repo_info
      _lowerCAmelCase   :					Optional[int]        =    token
      _lowerCAmelCase   :					str        =    None

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      if self.dir_cache is None:
           _lowerCAmelCase   :					int        =    {}
           for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _lowerCAmelCase   :					Optional[int]        =    {
                    "name": hf_file.rfilename,
                    "size": None,
                    "type": "file",
                }
                self.dir_cache.update(
                    {
                        str(A_       ): {"name": str(A_       ), "size": None, "type": "directory"}
                        for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
                    }       )

 def    __magic_name__       (		self :	Dict     ,						A_ :	str     ,						A_ :	str = "rb"     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      if not isinstance(self.repo_info     ,						A_       ):
           raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''       )
      _lowerCAmelCase   :					str        =    hf_hub_url(self.repo_info.id     ,						A_     ,						revision=self.repo_info.sha       )
      return fsspec.open(
          A_     ,						mode=A_     ,						headers=get_authentication_headers_for_url(A_     ,						use_auth_token=self.token       )     ,						client_kwargs={"trust_env": True}     ,						).open()

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      self._get_dirs()
      _lowerCAmelCase   :					Optional[int]        =    self._strip_protocol(A_       )
      if path in self.dir_cache:
           return self.dir_cache[path]
      else:
           raise FileNotFoundError(A_       )


 def    __magic_name__       (		self :	Any     ,						A_ :	str     ,						A_ :	Any=False     ,						**A_ :	Dict       ):






      '''simple docstring'''




      self._get_dirs()
      _lowerCAmelCase   :					Dict        =    PurePosixPath(path.strip("/"       )       )
      _lowerCAmelCase   :					str        =    {}
      for p, f in self.dir_cache.items():
           _lowerCAmelCase   :					List[str]        =    PurePosixPath(p.strip("/"       )       )
           _lowerCAmelCase   :					Tuple        =    p.parent
           if root == path:
                _lowerCAmelCase   :					Union[str, Any]        =    f
      _lowerCAmelCase   :					List[str]        =    list(paths.values()       )
      if detail:
           return out
      else:
           return sorted(f["name"] for f in out       )






