






'''simple docstring'''







import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			@parameterized.expand([(None,), ('''foo.json''',)]					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			List[Any]:
							a_		:  List[Any]											=      GenerationConfig(
							    do_sample=__SCREAMING_SNAKE_CASE  ,						temperature=0.7  ,						length_penalty=1.0  ,						bad_words_ids=[[1, 2, 3], [4, 5]]  ,						)
							with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(__SCREAMING_SNAKE_CASE  ,						config_name=__SCREAMING_SNAKE_CASE					)
											a_		:  str											=      GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE  ,						config_name=__SCREAMING_SNAKE_CASE					)

							# Checks parameters that were specified
							self.assertEqual(loaded_config.do_sample  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(loaded_config.temperature  ,						0.7					)
							self.assertEqual(loaded_config.length_penalty  ,						1.0					)
							self.assertEqual(loaded_config.bad_words_ids  ,						[[1, 2, 3], [4, 5]]					)

							# Checks parameters that were not specified (defaults)
							self.assertEqual(loaded_config.top_k  ,						50					)
							self.assertEqual(loaded_config.max_length  ,						20					)
							self.assertEqual(loaded_config.max_time  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			str:
							a_		:  str											=      AutoConfig.from_pretrained('''gpt2'''					)
							a_		:  str											=      GenerationConfig.from_model_config(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      GenerationConfig()

							# The generation config has loaded a few non-default parameters from the model config
							self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							# One of those parameters is eos_token_id -- check if it matches
							self.assertNotEqual(generation_config_from_model.eos_token_id  ,						default_generation_config.eos_token_id					)
							self.assertEqual(generation_config_from_model.eos_token_id  ,						model_config.eos_token_id					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Union[str, Any]:
							a_		:  Tuple											=      GenerationConfig()
							a_		:  List[Any]											=      {
							    '''max_new_tokens''': 1024,
							    '''foo''': '''bar''',
							}
							a_		:  Union[str, Any]											=      copy.deepcopy(__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      generation_config.update(**__SCREAMING_SNAKE_CASE					)

							# update_kwargs was not modified (no side effects)
							self.assertEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(generation_config.max_new_tokens  ,						1024					)

							# `.update()` returns a dictionary of unused kwargs
							self.assertEqual(__SCREAMING_SNAKE_CASE  ,						{'''foo''': '''bar'''}					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							a_		:  Optional[int]											=      GenerationConfig()
							a_		:  List[str]											=      '''bar'''

							with tempfile.TemporaryDirectory('''test-generation-config'''					) as tmp_dir:
											generation_config.save_pretrained(__SCREAMING_SNAKE_CASE					)

											a_		:  Optional[int]											=      GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE					)
							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(new_config.foo  ,						'''bar'''					)

							a_		:  Union[str, Any]											=      GenerationConfig.from_model_config(__SCREAMING_SNAKE_CASE					)
							assert not hasattr(__SCREAMING_SNAKE_CASE  ,						'''foo'''					)  # no new kwargs should be initialized if from config







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[int]:
							a_		:  Dict											=      GenerationConfig()
							self.assertEqual(default_config.temperature  ,						1.0					)
							self.assertEqual(default_config.do_sample  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(default_config.num_beams  ,						1					)

							a_		:  Optional[Any]											=      GenerationConfig(
							    do_sample=__SCREAMING_SNAKE_CASE  ,						temperature=0.7  ,						length_penalty=1.0  ,						bad_words_ids=[[1, 2, 3], [4, 5]]  ,						)
							self.assertEqual(config.temperature  ,						0.7					)
							self.assertEqual(config.do_sample  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(config.num_beams  ,						1					)

							with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(__SCREAMING_SNAKE_CASE					)
											a_		:  Union[str, Any]											=      GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE  ,						temperature=1.0					)

							self.assertEqual(loaded_config.temperature  ,						1.0					)
							self.assertEqual(loaded_config.do_sample  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(loaded_config.num_beams  ,						1					)  # default value





@is_staging_test
class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			Optional[int]					)			->			Union[str, Any]:
							a_		:  List[str]											=      TOKEN
							HfFolder.save_token(__SCREAMING_SNAKE_CASE					)






			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			int					)			->			Union[str, Any]:
							try:
											delete_repo(token=cls._token  ,						repo_id='''test-generation-config'''					)
							except HTTPError:
											pass

							try:
											delete_repo(token=cls._token  ,						repo_id='''valid_org/test-generation-config-org'''					)
							except HTTPError:
											pass






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Optional[int]:
							a_		:  List[Any]											=      GenerationConfig(
							    do_sample=__SCREAMING_SNAKE_CASE  ,						temperature=0.7  ,						length_penalty=1.0  ,						)
							config.push_to_hub('''test-generation-config'''  ,						use_auth_token=self._token					)

							a_		:  int											=      GenerationConfig.from_pretrained(f'{USER}/test-generation-config'					)
							for k, v in config.to_dict().items():
											if k != "transformers_version":
															self.assertEqual(__SCREAMING_SNAKE_CASE  ,						getattr(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)

        # Reset repo
							delete_repo(token=self._token  ,						repo_id='''test-generation-config'''					)

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(
											    __SCREAMING_SNAKE_CASE  ,						repo_id='''test-generation-config'''  ,						push_to_hub=__SCREAMING_SNAKE_CASE  ,						use_auth_token=self._token					)

							a_		:  List[Any]											=      GenerationConfig.from_pretrained(f'{USER}/test-generation-config'					)
							for k, v in config.to_dict().items():
											if k != "transformers_version":
															self.assertEqual(__SCREAMING_SNAKE_CASE  ,						getattr(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Tuple:
							a_		:  Optional[int]											=      GenerationConfig(
							    do_sample=__SCREAMING_SNAKE_CASE  ,						temperature=0.7  ,						length_penalty=1.0  ,						)
							config.push_to_hub('''valid_org/test-generation-config-org'''  ,						use_auth_token=self._token					)

							a_		:  str											=      GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''					)
							for k, v in config.to_dict().items():
											if k != "transformers_version":
															self.assertEqual(__SCREAMING_SNAKE_CASE  ,						getattr(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)

        # Reset repo
							delete_repo(token=self._token  ,						repo_id='''valid_org/test-generation-config-org'''					)

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(
											    __SCREAMING_SNAKE_CASE  ,						repo_id='''valid_org/test-generation-config-org'''  ,						push_to_hub=__SCREAMING_SNAKE_CASE  ,						use_auth_token=self._token					)

							a_		:  int											=      GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''					)
							for k, v in config.to_dict().items():
											if k != "transformers_version":
															self.assertEqual(__SCREAMING_SNAKE_CASE  ,						getattr(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)












'''simple docstring'''







import torch

from transformers import AutoModel





class 	SCREAMING_SNAKE_CASE					(					torch.nn.Module       ):






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int="sayef/fsner-bert-base-uncased"					)			->			str:
							super(__SCREAMING_SNAKE_CASE  ,						self					).__init__()

							a_		:  str											=      AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      torch.nn.CosineSimilarity(3  ,						1e-08					)
							a_		:  Dict											=      torch.nn.Softmax(dim=1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							return self.bert(**__SCREAMING_SNAKE_CASE					).last_hidden_state






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							return token_embeddings.sum(2  ,						keepdim=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=1					)			->			Dict:
							return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							a_		:  Dict											=      W_supports['''sizes'''].tolist()
							a_		:  Tuple											=      W_supports['''start_token_id'''].item()
							a_		:  List[Any]											=      W_supports['''end_token_id'''].item()

							del W_supports["sizes"]
							del W_supports["start_token_id"]
							del W_supports["end_token_id"]

							a_		:  int											=      self.BERT(**__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.BERT(**__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      None
							a_		:  Tuple											=      None

							a_		:  List[str]											=      W_supports['''input_ids'''] == start_token_id
							a_		:  Dict											=      W_supports['''input_ids'''] == end_token_id

							for i, size in enumerate(__SCREAMING_SNAKE_CASE					):
											if i == 0:
															a_		:  str											=      0
											else:
															a_		:  str											=      support_sizes[i - 1]

											a_		:  Union[str, Any]											=      S[s : s + size][start_token_masks[s : s + size]]
											a_		:  Tuple											=      S[s : s + size][end_token_masks[s : s + size]]

											a_		:  Tuple											=      torch.matmul(q[i]  ,						s_start.T					).sum(1					).softmax(0					)
											a_		:  Optional[Any]											=      torch.matmul(q[i]  ,						s_end.T					).sum(1					).softmax(0					)

											if p_starts is not None:
															a_		:  Any											=      torch.vstack((p_starts, p_start)					)
															a_		:  Dict											=      torch.vstack((p_ends, p_end)					)
											else:
															a_		:  Optional[int]											=      p_start
															a_		:  List[Any]											=      p_end

							return p_starts, p_ends




