



"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    '''files'''     ,       [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]     ,       )
def  lowerCamelCase_						(UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  List[Any]											=    tmp_path_factory.mktemp('''dset_infos_dir'''							)
    if "full:README.md" in files:
        with open(dataset_infos_dir / '''README.md'''     ,       '''w'''							) as f:
            f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''							)
    if "empty:README.md" in files:
        with open(dataset_infos_dir / '''README.md'''     ,       '''w'''							) as f:
            f.write(''''''							)
    # we want to support dataset_infos.json for backward compatibility
    if "dataset_infos.json" in files:
        with open(dataset_infos_dir / '''dataset_infos.json'''     ,       '''w'''							) as f:
            f.write('''{"default": {"dataset_size": 42}}'''							)
    _UpperCAmelCase							:  Any											=    DatasetInfosDict.from_directory(UpperCamelCase__							)
    assert dataset_infos
    assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    '''dataset_info'''     ,       [
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''     ,       features=Features({'''a''': Value('''int32'''							)}							)     ,       builder_name='''builder'''     ,       config_name='''config'''     ,       version='''1.0.0'''     ,       splits=[{'''name''': '''train'''}]     ,       download_size=42     ,       ),
    ]     ,       )
def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		DatasetInfo							):
    _UpperCAmelCase							:  List[Any]											=    str(UpperCamelCase__							)
    dataset_info.write_to_directory(UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[int]											=    DatasetInfo.from_directory(UpperCamelCase__							)
    assert dataset_info == reloaded
    assert os.path.exists(os.path.join(UpperCamelCase__     ,       '''dataset_info.json'''							)							)





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Optional[int]											=    DatasetInfo(
        description='''foo'''     ,       citation='''bar'''     ,       homepage='''https://foo.bar'''     ,       license='''CC0'''     ,       features=Features({'''a''': Value('''int32'''							)}							)     ,       post_processed={}     ,       supervised_keys=()     ,       task_templates=[]     ,       builder_name='''builder'''     ,       config_name='''config'''     ,       version='''1.0.0'''     ,       splits=[{'''name''': '''train''', '''num_examples''': 42}]     ,       download_checksums={}     ,       download_size=1337     ,       post_processing_size=442     ,       dataset_size=1234     ,       size_in_bytes=1337 + 442 + 1234     ,       )
    _UpperCAmelCase							:  Union[str, Any]											=    dataset_info._to_yaml_dict()
    assert sorted(UpperCamelCase__							) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML							)
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        assert key in dataset_info_yaml_dict
        assert isinstance(dataset_info_yaml_dict[key]     ,       (list, dict, int, str)							)
    _UpperCAmelCase							:  Any											=    yaml.safe_dump(UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[int]											=    yaml.safe_load(UpperCamelCase__							)
    assert dataset_info_yaml_dict == reloaded





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Any											=    DatasetInfo()
    _UpperCAmelCase							:  Optional[int]											=    dataset_info._to_yaml_dict()
    assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    '''dataset_infos_dict'''     ,       [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}							),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}							),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''     ,       features=Features({'''a''': Value('''int32'''							)}							)     ,       builder_name='''builder'''     ,       config_name='''config'''     ,       version='''1.0.0'''     ,       splits=[{'''name''': '''train'''}]     ,       download_size=42     ,       )
            }							),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42							),
                '''v2''': DatasetInfo(dataset_size=1337							),
            }							),
    ]     ,       )
def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		DatasetInfosDict							):
    _UpperCAmelCase							:  Union[str, Any]											=    str(UpperCamelCase__							)
    dataset_infos_dict.write_to_directory(UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    DatasetInfosDict.from_directory(UpperCamelCase__							)

    # the config_name of the dataset_infos_dict take over the attribute
    for config_name, dataset_info in dataset_infos_dict.items():
        _UpperCAmelCase							:  Any											=    config_name
        # the yaml representation doesn't include fields like description or citation
        # so we just test that we can recover what we can from the yaml
        _UpperCAmelCase							:  str											=    DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()							)
    assert dataset_infos_dict == reloaded

    if dataset_infos_dict:
        assert os.path.exists(os.path.join(UpperCamelCase__     ,       '''README.md'''							)							)







"""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


