


"""simple docstring"""


import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	A  :     Optional[Any]    					=    "platform"
	import jax
	import jax.numpy as jnp
	import numpy as np

	from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 						_UpperCamelCase						:
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[Any]          =PegasusConfig
							__UpperCAmelCase		:					Optional[int]          ={}
							__UpperCAmelCase		:					List[str]          ="""gelu"""

							def __init__(  self					,     __a					,     __a=13					,     __a=7					,     __a=True					,     __a=False					,     __a=99					,     __a=32					,     __a=5					,     __a=4					,     __a=37					,     __a=0.1					,     __a=0.1					,     __a=20					,     __a=2					,     __a=1					,     __a=0					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    seq_length
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size

										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    max_position_embeddings
										__lowerCAmelCase       					=    eos_token_id
										__lowerCAmelCase       					=    pad_token_id
										__lowerCAmelCase       					=    bos_token_id

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ids_tensor([self.batch_size, self.seq_length - 1]					,     self.vocab_size			).clip(3					,     self.vocab_size			)
										__lowerCAmelCase       					=    np.expand_dims(np.array([self.eos_token_id] * self.batch_size			)					,     1			)
										__lowerCAmelCase       					=    np.concatenate([input_ids, eos_tensor]					,     axis=1			)

										__lowerCAmelCase       					=    ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size			)

										__lowerCAmelCase       					=    self.config_cls(
										    vocab_size=self.vocab_size					,     d_model=self.hidden_size					,     encoder_layers=self.num_hidden_layers					,     decoder_layers=self.num_hidden_layers					,     encoder_attention_heads=self.num_attention_heads					,     decoder_attention_heads=self.num_attention_heads					,     encoder_ffn_dim=self.intermediate_size					,     decoder_ffn_dim=self.intermediate_size					,     dropout=self.hidden_dropout_prob					,     attention_dropout=self.attention_probs_dropout_prob					,     max_position_embeddings=self.max_position_embeddings					,     eos_token_ids=[2]					,     bos_token_id=self.bos_token_id					,     pad_token_id=self.pad_token_id					,     decoder_start_token_id=self.pad_token_id					,     **self.config_updates					,     )
										__lowerCAmelCase       					=    prepare_pegasus_inputs_dict(__a					,     __a					,     __a			)
										return config, inputs_dict

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    20
										__lowerCAmelCase       					=    model_class_name(__a			)

										__lowerCAmelCase       					=    model.encode(inputs_dict["input_ids"]			)

										__lowerCAmelCase					,			__lowerCAmelCase       					=    (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										__lowerCAmelCase       					=    model.init_cache(decoder_input_ids.shape[0]					,     __a					,     __a			)
										__lowerCAmelCase       					=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,     dtype="i4"			)

										__lowerCAmelCase       					=    jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]					,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,     )
										__lowerCAmelCase       					=    model.decode(
										    decoder_input_ids[:, :-1]					,     __a					,     decoder_attention_mask=__a					,     past_key_values=__a					,     decoder_position_ids=__a					,     )

										__lowerCAmelCase       					=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,     dtype="i4"			)
										__lowerCAmelCase       					=    model.decode(
										    decoder_input_ids[:, -1:]					,     __a					,     decoder_attention_mask=__a					,     past_key_values=outputs_cache.past_key_values					,     decoder_position_ids=__a					,     )

										__lowerCAmelCase       					=    model.decode(__a					,     __a			)

										__lowerCAmelCase       					=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
										self.parent.assertTrue(diff < 1e-3					,     msg=f"Max diff is {diff}"			)



							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    20
										__lowerCAmelCase       					=    model_class_name(__a			)

										__lowerCAmelCase       					=    model.encode(inputs_dict["input_ids"]			)

										__lowerCAmelCase					,			__lowerCAmelCase       					=    (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										__lowerCAmelCase       					=    jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
										    ]					,     axis=-1					,     )

										__lowerCAmelCase       					=    model.init_cache(decoder_input_ids.shape[0]					,     __a					,     __a			)
										__lowerCAmelCase       					=    jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]					,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,     )

										__lowerCAmelCase       					=    model.decode(
										    decoder_input_ids[:, :-1]					,     __a					,     decoder_attention_mask=__a					,     past_key_values=__a					,     decoder_position_ids=__a					,     )
										__lowerCAmelCase       					=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,     dtype="i4"			)
										__lowerCAmelCase       					=    model.decode(
										    decoder_input_ids[:, -1:]					,     __a					,     past_key_values=outputs_cache.past_key_values					,     decoder_attention_mask=__a					,     decoder_position_ids=__a					,     )

										__lowerCAmelCase       					=    model.decode(__a					,     __a					,     decoder_attention_mask=__a			)

										__lowerCAmelCase       					=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
										self.parent.assertTrue(diff < 1e-3					,     msg=f"Max diff is {diff}"			)


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				):

			'''simple docstring'''




			if attention_mask is None:
						__lowerCAmelCase       					=    np.not_equal(_UpperCamelCase					,				config.pad_token_id   ).astype(np.inta   )
			if decoder_attention_mask is None:
						__lowerCAmelCase       					=    np.concatenate(
						    [
						        np.ones(decoder_input_ids[:, :1].shape					,				dtype=np.inta   ),
						        np.not_equal(decoder_input_ids[:, 1:]					,				config.pad_token_id   ).astype(np.inta   ),
						    ]					,				axis=-1					,				)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}


@require_flax
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          =(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							__UpperCAmelCase		:					int          =(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							__UpperCAmelCase		:					Union[str, Any]          =True
							__UpperCAmelCase		:					List[Any]          =False
							__UpperCAmelCase		:					Dict          =False
							__UpperCAmelCase		:					List[Any]          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    FlaxPegasusModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=__a			)

							def     snake_case       (  self			):
										self.config_tester.run_common_tests()

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(__a					,     __a					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(__a					,     __a					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__			):
																__lowerCAmelCase       					=    self._prepare_for_class(__a					,     __a			)
																__lowerCAmelCase       					=    model_class(__a			)

																@jax.jit
																def encode_jitted(__a					,     __a=None					,     **__a			):
																			return model.encode(input_ids=__a					,     attention_mask=__a			)

																with self.subTest("JIT Enabled"			):
																			__lowerCAmelCase       					=    encode_jitted(**__a			).to_tuple()

																with self.subTest("JIT Disabled"			):
																			with jax.disable_jit():
																						__lowerCAmelCase       					=    encode_jitted(**__a			).to_tuple()

																self.assertEqual(len(__a			)					,     len(__a			)			)
																for jitted_output, output in zip(__a					,     __a			):
																			self.assertEqual(jitted_output.shape					,     output.shape			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__			):
																__lowerCAmelCase       					=    model_class(__a			)
																__lowerCAmelCase       					=    model.encode(inputs_dict["input_ids"]					,     inputs_dict["attention_mask"]			)

																__lowerCAmelCase       					=    {
																    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																    "encoder_outputs": encoder_outputs,
																}

																@jax.jit
																def decode_jitted(__a					,     __a					,     __a			):
																			return model.decode(
																			    decoder_input_ids=__a					,     decoder_attention_mask=__a					,     encoder_outputs=__a					,     )

																with self.subTest("JIT Enabled"			):
																			__lowerCAmelCase       					=    decode_jitted(**__a			).to_tuple()

																with self.subTest("JIT Disabled"			):
																			with jax.disable_jit():
																						__lowerCAmelCase       					=    decode_jitted(**__a			).to_tuple()

																self.assertEqual(len(__a			)					,     len(__a			)			)
																for jitted_output, output in zip(__a					,     __a			):
																			self.assertEqual(jitted_output.shape					,     output.shape			)

							@slow
							def     snake_case       (  self			):
										for model_class_name in self.all_model_classes:
													__lowerCAmelCase       					=    model_class_name.from_pretrained("google/pegasus-large"					,     from_pt=__a			)
													__lowerCAmelCase       					=    np.ones((1, 1)			)
													__lowerCAmelCase       					=    model(__a			)
													self.assertIsNotNone(__a			)



							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"			)
										__lowerCAmelCase       					=    PegasusTokenizer.from_pretrained("google/pegasus-xsum"			)

										__lowerCAmelCase       					=    [
										    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
										    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
										]

										__lowerCAmelCase       					=    [
										    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
										    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
										]

										__lowerCAmelCase       					=    tokenizer(__a					,     return_tensors="np"					,     truncation=__a					,     max_length=5_12					,     padding=__a			)
										__lowerCAmelCase       					=    model.generate(**__a					,     num_beams=2			).sequences
										__lowerCAmelCase       					=    tokenizer.batch_decode(__a					,     skip_special_tokens=__a			)
										assert tgt_text == decoded








"""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


A  :     Dict    					=    {
    "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": 1_2_8,
    "add_cross_attention": True,
    "tie_encoder_decoder": True,
    "max_length": 5_0,
    "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": 1_0,
    "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": 1_0,
    "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 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							@classmethod
							def     snake_case       (  cls			):
										__lowerCAmelCase       					=    TOKEN
										HfFolder.save_token(__a			)

							@classmethod
							def     snake_case       (  cls			):
										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			):
										__lowerCAmelCase       					=    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			)

										__lowerCAmelCase       					=    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			)

										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    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			)

										__lowerCAmelCase       					=    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			)

										__lowerCAmelCase       					=    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			):
										CustomConfig.register_for_auto_class()
										__lowerCAmelCase       					=    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"}			)

										__lowerCAmelCase       					=    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 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    GPTaConfig()

										# attempt to modify each of int/float/bool/str config records and verify they were updated
										__lowerCAmelCase       					=    c.n_embd + 1  # int
										__lowerCAmelCase       					=    c.resid_pdrop + 1.0  # float
										__lowerCAmelCase       					=    not c.scale_attn_weights  # bool
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    PretrainedConfig()
										__lowerCAmelCase       					=    [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"]			)
										__lowerCAmelCase       					=    [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			):
										with self.assertRaises(__a			):
													# config is in subfolder, the following should not work without specifying the subfolder
													__lowerCAmelCase       					=    BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"			)

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

										self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										# A mock response for an HTTP head request to emulate server down
										__lowerCAmelCase       					=    mock.Mock()
										__lowerCAmelCase       					=    5_00
										__lowerCAmelCase       					=    {}
										__lowerCAmelCase       					=    HTTPError
										__lowerCAmelCase       					=    {}

										# Download this model to make sure it's in the cache.
										__lowerCAmelCase       					=    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:
													__lowerCAmelCase       					=    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			):
										# This test is for deprecated behavior and can be removed in v5
										__lowerCAmelCase       					=    BertConfig.from_pretrained(
										    "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    AutoConfig.from_pretrained("bert-base-cased"			)
										__lowerCAmelCase       					=    ["config.4.0.0.json"]

										with tempfile.TemporaryDirectory() as tmp_dir:
													configuration.save_pretrained(__a			)
													__lowerCAmelCase       					=    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
													__lowerCAmelCase       					=    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
													__lowerCAmelCase       					=    ["config.42.0.0.json"]
													__lowerCAmelCase       					=    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"			)			)
													__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
													self.assertEqual(new_configuration.hidden_size					,     7_68			)



							def     snake_case       (  self			):
										# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
										__lowerCAmelCase       					=    "hf-internal-testing/test-two-configs"

										import transformers as new_transformers

										__lowerCAmelCase       					=    "v4.0.0"
										__lowerCAmelCase					,			__lowerCAmelCase       					=    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

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




