





"""simple docstring"""





import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

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

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402


lowerCAmelCase__     			=			{
    '''return_dict''': False,
    '''output_hidden_states''': True,
    '''output_attentions''': True,
    '''torchscript''': True,
    '''torch_dtype''': '''float16''',
    '''use_bfloat16''': True,
    '''tf_legacy_loss''': True,
    '''pruned_heads''': {'''a''': 1},
    '''tie_word_embeddings''': False,
    '''is_decoder''': True,
    '''cross_attention_hidden_size''': 128,
    '''add_cross_attention''': True,
    '''tie_encoder_decoder''': True,
    '''max_length''': 50,
    '''min_length''': 3,
    '''do_sample''': True,
    '''early_stopping''': True,
    '''num_beams''': 3,
    '''num_beam_groups''': 3,
    '''diversity_penalty''': 0.5,
    '''temperature''': 2.0,
    '''top_k''': 10,
    '''top_p''': 0.7,
    '''typical_p''': 0.2,
    '''repetition_penalty''': 0.8,
    '''length_penalty''': 0.8,
    '''no_repeat_ngram_size''': 5,
    '''encoder_no_repeat_ngram_size''': 5,
    '''bad_words_ids''': [1, 2, 3],
    '''num_return_sequences''': 3,
    '''chunk_size_feed_forward''': 5,
    '''output_scores''': True,
    '''return_dict_in_generate''': True,
    '''forced_bos_token_id''': 2,
    '''forced_eos_token_id''': 3,
    '''remove_invalid_values''': True,
    '''architectures''': ['''BertModel'''],
    '''finetuning_task''': '''translation''',
    '''id2label''': {0: '''label'''},
    '''label2id''': {'''label''': '''0'''},
    '''tokenizer_class''': '''BertTokenizerFast''',
    '''prefix''': '''prefix''',
    '''bos_token_id''': 6,
    '''pad_token_id''': 7,
    '''eos_token_id''': 8,
    '''sep_token_id''': 9,
    '''decoder_start_token_id''': 10,
    '''exponential_decay_length_penalty''': (5, 1.01),
    '''suppress_tokens''': [0, 1],
    '''begin_suppress_tokens''': 2,
    '''task_specific_params''': {'''translation''': '''some_params'''},
    '''problem_type''': '''regression''',
}





@is_staging_test
class        _lowerCamelCase      (       unittest.TestCase ):


						@classmethod
						def     snake_case_   (cls		) ->   List[str]:
								UpperCamelCase   =	TOKEN
								HfFolder.save_token(__a		)


						@classmethod
						def     snake_case_   (cls		) ->   List[Any]:
								try:
										delete_repo(token=cls._token   ,							repo_id="test-config"		)
								except HTTPError:
										pass

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

								try:
										delete_repo(token=cls._token   ,							repo_id="test-dynamic-config"		)
								except HTTPError:
										pass


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	BertConfig(
								    vocab_size=99   ,							hidden_size=32   ,							num_hidden_layers=5   ,							num_attention_heads=4   ,							intermediate_size=37		)
								config.push_to_hub("test-config"   ,							use_auth_token=self._token		)

								UpperCamelCase   =	BertConfig.from_pretrained(F"{USER}/test-config"		)
								for k, v in config.to_dict().items():
										if k != "transformers_version":
												self.assertEqual(__a   ,							getattr(__a   ,							__a		)		)

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

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

								UpperCamelCase   =	BertConfig.from_pretrained(F"{USER}/test-config"		)
								for k, v in config.to_dict().items():
										if k != "transformers_version":
												self.assertEqual(__a   ,							getattr(__a   ,							__a		)		)


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	BertConfig(
								    vocab_size=99   ,							hidden_size=32   ,							num_hidden_layers=5   ,							num_attention_heads=4   ,							intermediate_size=37		)
								config.push_to_hub("valid_org/test-config-org"   ,							use_auth_token=self._token		)

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

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

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

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




						def     snake_case_   (self		) ->   Optional[int]:
								CustomConfig.register_for_auto_class()
								UpperCamelCase   =	CustomConfig(attribute=42		)

								config.push_to_hub("test-dynamic-config"   ,							use_auth_token=self._token		)

								# This has added the proper auto_map field to the config
								self.assertDictEqual(config.auto_map   ,							{"AutoConfig": "custom_configuration.CustomConfig"}		)

								UpperCamelCase   =	AutoConfig.from_pretrained(F"{USER}/test-dynamic-config"   ,							trust_remote_code=__a		)
								# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
								self.assertEqual(new_config.__class__.__name__   ,							"CustomConfig"		)
								self.assertEqual(new_config.attribute   ,							42		)





class        _lowerCamelCase      (       unittest.TestCase ):


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	GPTaConfig()

								# attempt to modify each of int/float/bool/str config records and verify they were updated
								UpperCamelCase   =	c.n_embd + 1  # int
								UpperCamelCase   =	c.resid_pdrop + 1.0  # float
								UpperCamelCase   =	not c.scale_attn_weights  # bool
								UpperCamelCase   =	c.summary_type + "foo"  # str
								c.update_from_string(
								    F"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"		)
								self.assertEqual(__a   ,							c.n_embd   ,							"mismatch for key: n_embd"		)
								self.assertEqual(__a   ,							c.resid_pdrop   ,							"mismatch for key: resid_pdrop"		)
								self.assertEqual(__a   ,							c.scale_attn_weights   ,							"mismatch for key: scale_attn_weights"		)
								self.assertEqual(__a   ,							c.summary_type   ,							"mismatch for key: summary_type"		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	PretrainedConfig()
								UpperCamelCase   =	[key for key in base_config.__dict__ if key not in config_common_kwargs]
								# If this part of the test fails, you have arguments to addin config_common_kwargs above.
								self.assertListEqual(
								    __a   ,							["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]		)
								UpperCamelCase   =	[key for key, value in config_common_kwargs.items() if value == getattr(__a   ,							__a		)]
								if len(__a		) > 0:
										raise ValueError(
										    "The following keys are set with the default values in"
										    " `test_configuration_common.config_common_kwargs` pick another value for them:"
										    F" {', '.join(__a		)}."		)


						def     snake_case_   (self		) ->   Tuple:
								with self.assertRaises(__a		):
										# config is in subfolder, the following should not work without specifying the subfolder
										UpperCamelCase   =	BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"		)

								UpperCamelCase   =	BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"   ,							subfolder="bert"		)

								self.assertIsNotNone(__a		)


						def     snake_case_   (self		) ->   Tuple:
								# A mock response for an HTTP head request to emulate server down
								UpperCamelCase   =	mock.Mock()
								UpperCamelCase   =	5_00
								UpperCamelCase   =	{}
								UpperCamelCase   =	HTTPError
								UpperCamelCase   =	{}

								# Download this model to make sure it's in the cache.
								UpperCamelCase   =	BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"		)

								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch("requests.Session.request"   ,							return_value=__a		) as mock_head:
										UpperCamelCase   =	BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"		)
										# This check we did call the fake head request
										mock_head.assert_called()


						def     snake_case_   (self		) ->   Optional[int]:
								# This test is for deprecated behavior and can be removed in v5
								UpperCamelCase   =	BertConfig.from_pretrained(
								    "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"		)


						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	AutoConfig.from_pretrained("bert-base-cased"		)
								UpperCamelCase   =	["config.4.0.0.json"]

								with tempfile.TemporaryDirectory() as tmp_dir:
										configuration.save_pretrained(__a		)
										UpperCamelCase   =	2
										json.dump(configuration.to_dict()   ,							open(os.path.join(__a   ,							"config.4.0.0.json"		)   ,							"w"		)		)

										# This should pick the new configuration file as the version of Transformers is > 4.0.0
										UpperCamelCase   =	AutoConfig.from_pretrained(__a		)
										self.assertEqual(new_configuration.hidden_size   ,							2		)

										# Will need to be adjusted if we reach v42 and this test is still here.
										# Should pick the old configuration file as the version of Transformers is < 4.42.0
										UpperCamelCase   =	["config.42.0.0.json"]
										UpperCamelCase   =	7_68
										configuration.save_pretrained(__a		)
										shutil.move(os.path.join(__a   ,							"config.4.0.0.json"		)   ,							os.path.join(__a   ,							"config.42.0.0.json"		)		)
										UpperCamelCase   =	AutoConfig.from_pretrained(__a		)
										self.assertEqual(new_configuration.hidden_size   ,							7_68		)




						def     snake_case_   (self		) ->   List[str]:
								# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
								UpperCamelCase   =	"hf-internal-testing/test-two-configs"

								import transformers as new_transformers

								UpperCamelCase   =	"v4.0.0"
								UpperCamelCase   ,				UpperCamelCase   =	new_transformers.models.auto.AutoConfig.from_pretrained(
								    __a   ,							return_unused_kwargs=__a		)
								self.assertEqual(new_configuration.hidden_size   ,							2		)
								# This checks `_configuration_file` ia not kept in the kwargs by mistake.
								self.assertDictEqual(__a   ,							{}		)

								# Testing an older version by monkey-patching the version in the module it's used.
								import transformers as old_transformers

								UpperCamelCase   =	"v3.0.0"
								UpperCamelCase   =	old_transformers.models.auto.AutoConfig.from_pretrained(__a		)
								self.assertEqual(old_configuration.hidden_size   ,							7_68		)













"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase__     			=			{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			['''BlenderbotTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BlenderbotForCausalLM''',
						    '''BlenderbotForConditionalGeneration''',
						    '''BlenderbotModel''',
						    '''BlenderbotPreTrainedModel''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''TFBlenderbotForConditionalGeneration''',
						    '''TFBlenderbotModel''',
						    '''TFBlenderbotPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''FlaxBlenderbotForConditionalGeneration''',
						    '''FlaxBlenderbotModel''',
						    '''FlaxBlenderbotPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_blenderbot import (
						    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    BlenderbotConfig,
						    BlenderbotOnnxConfig,
						)
						from .tokenization_blenderbot import BlenderbotTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blenderbot import (
												    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlenderbotForCausalLM,
												    BlenderbotForConditionalGeneration,
												    BlenderbotModel,
												    BlenderbotPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blenderbot import (
												    TFBlenderbotForConditionalGeneration,
												    TFBlenderbotModel,
												    TFBlenderbotPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_blenderbot import (
												    FlaxBlenderbotForConditionalGeneration,
												    FlaxBlenderbotModel,
												    FlaxBlenderbotPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






