


"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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  :     Tuple    					=    "platform"

	import jax
	import jax.numpy as jnp

	from transformers import BlenderbotTokenizer
	from transformers.models.blenderbot.modeling_flax_blenderbot import (
	    FlaxBlenderbotForConditionalGeneration,
	    FlaxBlenderbotModel,
	    shift_tokens_right,
	)


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

			'''simple docstring'''




			if attention_mask is None:
						__lowerCAmelCase       					=    np.where(input_ids != config.pad_token_id					,				1					,				0   )
			if decoder_attention_mask is None:
						__lowerCAmelCase       					=    np.where(decoder_input_ids != config.pad_token_id					,				1					,				0   )
			if head_mask is None:
						__lowerCAmelCase       					=    np.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						__lowerCAmelCase       					=    np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						__lowerCAmelCase       					=    np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			}
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=13					,     __a=7					,     __a=True					,     __a=False					,     __a=99					,     __a=16					,     __a=2					,     __a=4					,     __a=4					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=32					,     __a=2					,     __a=1					,     __a=0					,     __a=0.0_2					,     ):
										__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_act
										__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
										__lowerCAmelCase       					=    initializer_range

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,     self.vocab_size			)					,     3					,     self.vocab_size			)
										__lowerCAmelCase       					=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,     dtype=np.intaa			))					,     -1			)

										__lowerCAmelCase       					=    shift_tokens_right(__a					,     1					,     2			)

										__lowerCAmelCase       					=    BlenderbotConfig(
										    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_id=self.eos_token_id					,     bos_token_id=self.bos_token_id					,     pad_token_id=self.pad_token_id					,     initializer_range=self.initializer_range					,     use_cache=__a					,     )
										__lowerCAmelCase       					=    prepare_blenderbot_inputs_dict(__a					,     __a					,     __a			)
										return config, inputs_dict

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										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}"			)
@require_flax
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          =9_9

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    np.array(
										    [
										        [71, 82, 18, 33, 46, 91, 2],
										        [68, 34, 26, 58, 30, 82, 2],
										        [5, 97, 17, 39, 94, 40, 2],
										        [76, 83, 94, 25, 70, 78, 2],
										        [87, 59, 41, 35, 48, 66, 2],
										        [55, 13, 16, 58, 5, 2, 1],  # note padding
										        [64, 27, 31, 51, 12, 75, 2],
										        [52, 64, 86, 17, 83, 39, 2],
										        [48, 61, 9, 24, 71, 82, 2],
										        [26, 1, 60, 48, 22, 13, 2],
										        [21, 5, 62, 28, 14, 76, 2],
										        [45, 98, 37, 86, 59, 48, 2],
										        [70, 70, 50, 9, 28, 0, 2],
										    ]					,     dtype=np.intaa					,     )

										__lowerCAmelCase       					=    input_ids.shape[0]
										__lowerCAmelCase       					=    BlenderbotConfig(
										    vocab_size=self.vocab_size					,     d_model=24					,     encoder_layers=2					,     decoder_layers=2					,     encoder_attention_heads=2					,     decoder_attention_heads=2					,     encoder_ffn_dim=32					,     decoder_ffn_dim=32					,     max_position_embeddings=48					,     eos_token_id=2					,     pad_token_id=1					,     bos_token_id=0					,     )
										return config, input_ids, batch_size

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    self._get_config_and_data()
										__lowerCAmelCase       					=    FlaxBlenderbotForConditionalGeneration(__a			)
										__lowerCAmelCase       					=    lm_model(input_ids=__a			)
										__lowerCAmelCase       					=    (batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    BlenderbotConfig(
										    vocab_size=self.vocab_size					,     d_model=14					,     encoder_layers=2					,     decoder_layers=2					,     encoder_attention_heads=2					,     decoder_attention_heads=2					,     encoder_ffn_dim=8					,     decoder_ffn_dim=8					,     max_position_embeddings=48					,     )
										__lowerCAmelCase       					=    FlaxBlenderbotForConditionalGeneration(__a			)
										__lowerCAmelCase       					=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,     dtype=np.intaa			)
										__lowerCAmelCase       					=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,     dtype=np.intaa			)
										__lowerCAmelCase       					=    lm_model(input_ids=__a					,     decoder_input_ids=__a			)
										__lowerCAmelCase       					=    (*summary.shape, config.vocab_size)
										self.assertEqual(outputs["logits"].shape					,     __a			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,     dtype=np.intaa			)
										__lowerCAmelCase       					=    shift_tokens_right(__a					,     1					,     2			)
										__lowerCAmelCase       					=    np.equal(__a					,     1			).astype(np.floataa			).sum()
										__lowerCAmelCase       					=    np.equal(__a					,     1			).astype(np.floataa			).sum()
										self.assertEqual(shifted.shape					,     input_ids.shape			)
										self.assertEqual(__a					,     n_pad_before - 1			)
										self.assertTrue(np.equal(shifted[:, 0]					,     2			).all()			)
@require_flax
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Dict          =True
							__UpperCAmelCase		:					Dict          =(
							    (
							        FlaxBlenderbotModel,
							        FlaxBlenderbotForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							__UpperCAmelCase		:					Any          =(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    FlaxBlenderbotModelTester(self			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										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 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("facebook/blenderbot-400M-distill"			)
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													__lowerCAmelCase       					=    np.ones((1, 1)			) * model.config.eos_token_id
													__lowerCAmelCase       					=    model(__a			)
													self.assertIsNotNone(__a			)



							@unittest.skipUnless(jax_device != "cpu"					,     "3B test too slow on CPU."			)
							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
										__lowerCAmelCase       					=    {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

										__lowerCAmelCase       					=    FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"					,     from_pt=__a			)
										__lowerCAmelCase       					=    BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"			)

										__lowerCAmelCase       					=    ["Sam"]
										__lowerCAmelCase       					=    tokenizer(__a					,     return_tensors="jax"			)

										__lowerCAmelCase       					=    model.generate(**__a					,     **__a			)
										__lowerCAmelCase       					=    "Sam is a great name. It means \"sun\" in Gaelic."

										__lowerCAmelCase       					=    tokenizer.batch_decode(__a					,     **__a			)
										assert generated_txt[0].strip() == tgt_text








"""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			)




