

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


lowercase_       		=  {
    "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  A						(							unittest.TestCase ):







    """simple docstring"""





    @classmethod
    def 						snake_case__     (      cls :       Optional[Any]		)->    str:





        '''simple docstring'''







        A__          =   TOKEN
        HfFolder.save_token(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Optional[int]		)->    Union[str, Any]:





        '''simple docstring'''







        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 :       Any		)->    str:





        '''simple docstring'''







        A__          =   BertConfig(
            vocab_size=9_9,hidden_size=3_2,num_hidden_layers=5,num_attention_heads=4,intermediate_size=3_7		)
        config.push_to_hub('test-config',use_auth_token=self._token		)

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

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

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


    def 						snake_case__     (      self :       str		)->    Dict:





        '''simple docstring'''







        A__          =   BertConfig(
            vocab_size=9_9,hidden_size=3_2,num_hidden_layers=5,num_attention_heads=4,intermediate_size=3_7		)
        config.push_to_hub('valid_org/test-config-org',use_auth_token=self._token		)

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

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

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


    def 						snake_case__     (      self :       str		)->    Union[str, Any]:





        '''simple docstring'''







        CustomConfig.register_for_auto_class()
        A__          =   CustomConfig(attribute=4_2		)

        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'}		)

        A__          =   AutoConfig.from_pretrained(F'{USER}/test-dynamic-config',trust_remote_code=lowercase_		)
        # 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,4_2		)




class  A						(							unittest.TestCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       Tuple		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   GPTaConfig()

        # attempt to modify each of int/float/bool/str config records and verify they were updated
        A__          =   c.n_embd + 1  # int
        A__          =   c.resid_pdrop + 1.0  # float
        A__          =   not c.scale_attn_weights  # bool
        A__          =   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(lowercase_,c.n_embd,'mismatch for key: n_embd'		)
        self.assertEqual(lowercase_,c.resid_pdrop,'mismatch for key: resid_pdrop'		)
        self.assertEqual(lowercase_,c.scale_attn_weights,'mismatch for key: scale_attn_weights'		)
        self.assertEqual(lowercase_,c.summary_type,'mismatch for key: summary_type'		)


    def 						snake_case__     (      self :       Any		)->    Optional[int]:





        '''simple docstring'''







        A__          =   PretrainedConfig()
        A__          =   [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(
            lowercase_,['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']		)
        A__          =   [key for key, value in config_common_kwargs.items() if value == getattr(lowercase_,lowercase_		)]
        if len(lowercase_		) > 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(lowercase_		)}.'		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[int]:





        '''simple docstring'''







        with self.assertRaises(lowercase_		):
            # config is in subfolder, the following should not work without specifying the subfolder
            A__          =   BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'		)

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

        self.assertIsNotNone(lowercase_		)


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        A__          =   mock.Mock()
        A__          =   5_0_0
        A__          =   {}
        A__          =   HTTPError
        A__          =   {}

        # Download this model to make sure it's in the cache.
        A__          =   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=lowercase_		) as mock_head:
            A__          =   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 :       int		)->    Tuple:





        '''simple docstring'''







        A__          =   BertConfig.from_pretrained(
            'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'		)


    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        A__          =   AutoConfig.from_pretrained('bert-base-cased'		)
        A__          =   ['config.4.0.0.json']

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

            # This should pick the new configuration file as the version of Transformers is > 4.0.0
            A__          =   AutoConfig.from_pretrained(lowercase_		)
            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
            A__          =   ['config.42.0.0.json']
            A__          =   7_6_8
            configuration.save_pretrained(lowercase_		)
            shutil.move(os.path.join(lowercase_,'config.4.0.0.json'		),os.path.join(lowercase_,'config.42.0.0.json'		)		)
            A__          =   AutoConfig.from_pretrained(lowercase_		)
            self.assertEqual(new_configuration.hidden_size,7_6_8		)


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        A__          =   'hf-internal-testing/test-two-configs'

        import transformers as new_transformers

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

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

        A__          =   'v3.0.0'
        A__          =   old_transformers.models.auto.AutoConfig.from_pretrained(lowercase_		)
        self.assertEqual(old_configuration.hidden_size,7_6_8		)



"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




