
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml




class     __snake_case		(   yaml.SafeLoader):
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= [self.constructed_objects[key_node] for key_node, _ in node.value]
									lowerCamelCase					:			Tuple									= [tuple(A      ) if isinstance(A,    A      ) else key for key in keys]
									lowerCamelCase					:			List[Any]									= Counter(A      )
									lowerCamelCase					:			int									= [key for key in counter if counter[key] > 1]
									if duplicate_keys:
													raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''      )




					def     UpperCAmelCase_							(     self,    A,    A=False      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= super().construct_mapping(A,    deep=A      )
									self._check_no_duplicates_on_constructed_node(A      )
									return mapping





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= list(readme_content.splitlines())
				if full_content and full_content[0] == "---" and "---" in full_content[1:]:
								lowerCamelCase					:			Optional[Any]									= full_content[1:].index('---') + 1
								lowerCamelCase					:			str									= '\n'.join(full_content[1:sep_idx])
								return yamlblock, "\n".join(full_content[sep_idx + 1 :])

				return None, "\n".join(UpperCAmelCase__)




class     __snake_case		(   a__):
					# class attributes
					_lowerCAmelCase			     =	{'''train_eval_index'''}  # train-eval-index in the YAML metadata
					@classmethod
					def     UpperCAmelCase_							(     cls,    A      ):

									"""simple docstring"""


									with open(A,    encoding='utf-8'      ) as readme_file:
													lowerCamelCase					:			Tuple									= _split_yaml_from_readme(readme_file.read()      )
									if yaml_string is not None:
													return cls.from_yaml_string(A      )
									else:
													return cls()
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if path.exists():
													with open(A,    encoding='utf-8'      ) as readme_file:
																	lowerCamelCase					:			Union[str, Any]									= readme_file.read()
									else:
													lowerCamelCase					:			Union[str, Any]									= None
									lowerCamelCase					:			List[str]									= self._to_readme(A      )
									with open(A,    'w',    encoding='utf-8'      ) as readme_file:
													readme_file.write(A      )
					def     UpperCAmelCase_							(     self,    A = None      ):

									"""simple docstring"""


									if readme_content is not None:
													lowerCamelCase					:			Optional[int]									= _split_yaml_from_readme(A      )
													lowerCamelCase					:			List[Any]									= '---\n' + self.to_yaml_string() + '---\n' + content
									else:
													lowerCamelCase					:			Optional[int]									= '---\n' + self.to_yaml_string() + '---\n'
									return full_content
					@classmethod
					def     UpperCAmelCase_							(     cls,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= yaml.load(A,    Loader=_NoDuplicateSafeLoader      ) or {}

									# Convert the YAML keys to DatasetMetadata fields
									lowerCamelCase					:			Union[str, Any]									= {
									    (key.replace('-',    '_'      ) if key.replace('-',    '_'      ) in cls._FIELDS_WITH_DASHES else key): value
									    for key, value in metadata_dict.items()
									}
									return cls(**A      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return yaml.safe_dump(
									    {
									        (key.replace('_',    '-'      ) if key in self._FIELDS_WITH_DASHES else key): value
									        for key, value in self.items()
									    },    sort_keys=A,    allow_unicode=A,    encoding='utf-8',    ).decode('utf-8'      )





A			      =      {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
					from argparse import ArgumentParser

					A			      =      ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
					ap.add_argument('readme_filepath')
					A			      =      ap.parse_args()

					A			      =      Path(args.readme_filepath)
					A			      =      DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)


'''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     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									lowerCamelCase					:			Optional[Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			List[Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= tokenizer.decode(greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Optional[int]									= TextStreamer(A      )
													model.generate(A,    max_new_tokens=10,    do_sample=A,    streamer=A      )
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									lowerCamelCase					:			int									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= 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					:			Optional[int]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			Any									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			Optional[int]									= tokenizer.decode(greedy_ids[0]      )

									lowerCamelCase					:			Union[str, Any]									= TextIteratorStreamer(A      )
									lowerCamelCase					:			Tuple									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			List[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()
									lowerCamelCase					:			List[Any]									= ''
									for new_text in streamer:
													streamer_text += new_text

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			int									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Optional[Any]									= -1

									lowerCamelCase					:			Union[str, Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= greedy_ids[:, input_ids.shape[1] :]
									lowerCamelCase					:			Dict									= tokenizer.decode(new_greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Tuple									= TextStreamer(A,    skip_prompt=A      )
													model.generate(A,    max_new_tokens=10,    do_sample=A,    streamer=A      )
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									lowerCamelCase					:			Optional[int]									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= AutoTokenizer.from_pretrained('distilgpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('distilgpt2'      ).to(A      )
									lowerCamelCase					:			Dict									= -1

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, 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					:			Any									= -1

									lowerCamelCase					:			int									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			int									= TextIteratorStreamer(A,    timeout=0.001      )
									lowerCamelCase					:			Any									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			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      ):
													lowerCamelCase					:			Dict									= ''
													for new_text in streamer:
																	streamer_text += new_text
