"""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 						a__							(       unittest.TestCase		):



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

								# Checks parameters that were specified
								self.assertEqual(loaded_config.do_sample				,						UpperCAmelCase   )
								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				,						5_0   )
								self.assertEqual(loaded_config.max_length				,						2_0   )
								self.assertEqual(loaded_config.max_time				,						UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       AutoConfig.from_pretrained('gpt2'   )
								__a		 =       GenerationConfig.from_model_config(UpperCAmelCase   )
								__a		 =       GenerationConfig()

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

								# 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]:
								__a		 =       GenerationConfig()
								__a		 =       {
								    'max_new_tokens': 1_0_2_4,
								    'foo': 'bar',
								}
								__a		 =       copy.deepcopy(UpperCAmelCase   )
								__a		 =       generation_config.update(**UpperCAmelCase   )

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

								# update_kwargs was used to update the config on valid attributes
								self.assertEqual(generation_config.max_new_tokens				,						1_0_2_4   )

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       GenerationConfig()
								__a		 =       'bar'

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

													__a		 =       GenerationConfig.from_pretrained(UpperCAmelCase   )
								# update_kwargs was used to update the config on valid attributes
								self.assertEqual(new_config.foo				,						'bar'   )

								__a		 =       GenerationConfig.from_model_config(UpperCAmelCase   )
								assert not hasattr(UpperCAmelCase				,						'foo'   )  # no new kwargs should be initialized if from config







			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       GenerationConfig()
								self.assertEqual(default_config.temperature				,						1.0   )
								self.assertEqual(default_config.do_sample				,						UpperCAmelCase   )
								self.assertEqual(default_config.num_beams				,						1   )

								__a		 =       GenerationConfig(
								    do_sample=UpperCAmelCase				,						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				,						UpperCAmelCase   )
								self.assertEqual(config.num_beams				,						1   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													config.save_pretrained(UpperCAmelCase   )
													__a		 =       GenerationConfig.from_pretrained(UpperCAmelCase				,						temperature=1.0   )

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






@is_staging_test
class 						a__							(       unittest.TestCase		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls   )			->   Tuple:
								__a		 =       TOKEN
								HfFolder.save_token(UpperCAmelCase   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls   )			->   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   )			->   Any:
								__a		 =       GenerationConfig(
								    do_sample=UpperCAmelCase				,						temperature=0.7				,						length_penalty=1.0				,						)
								config.push_to_hub('test-generation-config'				,						use_auth_token=self._token   )

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

        # 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(
													    UpperCAmelCase				,						repo_id='test-generation-config'				,						push_to_hub=UpperCAmelCase				,						use_auth_token=self._token   )

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







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

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

        # 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(
													    UpperCAmelCase				,						repo_id='valid_org/test-generation-config-org'				,						push_to_hub=UpperCAmelCase				,						use_auth_token=self._token   )

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[Any]							=	{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	[
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




