






"""simple docstring"""







import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


a			:    Any				 =		get_logger(__name__)


a			:    Any				 =		r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""


class    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												for processor in self:
																		lowercase__    :				Optional[Any]=	inspect.signature(processor.__call__      ).parameters
																		if len(snake_case__      ) > 3:
																								if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
																														raise ValueError(
																														    F'''Make sure that all the required parameters: {list(function_args.keys()      )} for '''
																														    F'''{processor.__class__} are passed to the logits processor.'''      )
																								lowercase__    :				Union[str, Any]=	processor(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      )
																		else:
																								lowercase__    :				Dict=	processor(snake_case__	,  snake_case__	,  snake_case__      )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or not (temperature > 0):
																		raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''      )

												lowercase__    :				Any=	temperature




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	scores / self.temperature
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or (top_p < 0 or top_p > 1.0):
																		raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''      )
												if not isinstance(snake_case__	,  snake_case__      ) or (min_tokens_to_keep < 1):
																		raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''      )

												lowercase__    :				int=	top_p
												lowercase__    :				Optional[int]=	filter_value
												lowercase__    :				Tuple=	min_tokens_to_keep




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Dict=	lax.top_k(snake_case__	,  scores.shape[-1]      )

												lowercase__    :				Optional[int]=	jnp.full_like(snake_case__	,  self.filter_value      )
												lowercase__    :				Union[str, Any]=	jax.nn.softmax(snake_case__	,  axis=-1      ).cumsum(axis=-1      )
												lowercase__    :				str=	cumulative_probs < self.top_p

												# include the token that is higher than top_p as well
												lowercase__    :				str=	jnp.roll(snake_case__	,  1      )
												score_mask |= score_mask.at[:, 0].set(snake_case__      )

												# min tokens to keep
												lowercase__    :				Optional[int]=	score_mask.at[:, : self.min_tokens_to_keep].set(snake_case__      )

												lowercase__    :				str=	jnp.where(snake_case__	,  snake_case__	,  snake_case__      )
												lowercase__    :				str=	jax.lax.sort_key_val(snake_case__	,  snake_case__      )[-1]

												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or top_k <= 0:
																		raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''      )

												lowercase__    :				List[Any]=	max(snake_case__	,  snake_case__      )
												lowercase__    :				Dict=	filter_value




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Optional[Any]=	scores.shape
												lowercase__    :				int=	jnp.full(batch_size * vocab_size	,  self.filter_value      )

												lowercase__    :				Dict=	min(self.top_k	,  scores.shape[-1]      )  # Safety check
												lowercase__,     lowercase__    :				List[Any]=	lax.top_k(snake_case__	,  snake_case__      )
												lowercase__    :				Optional[int]=	jnp.broadcast_to((jnp.arange(snake_case__      ) * vocab_size)[:, None]	,  (batch_size, topk)      ).flatten()
												lowercase__    :				str=	topk_scores.flatten()
												lowercase__    :				Any=	topk_indices.flatten() + shift

												lowercase__    :				Optional[Any]=	next_scores_flat.at[topk_indices_flat].set(snake_case__      )
												lowercase__    :				str=	next_scores_flat.reshape(snake_case__	,  snake_case__      )
												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	bos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				int=	1 - jnp.bool_(cur_len - 1      )

												lowercase__    :				int=	jnp.where(snake_case__	,  new_scores.at[:, self.bos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	max_length
												lowercase__    :				str=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				Any=	1 - jnp.bool_(cur_len - self.max_length + 1      )

												lowercase__    :				Optional[int]=	jnp.where(snake_case__	,  new_scores.at[:, self.eos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or min_length < 0:
																		raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''      )

												if not isinstance(snake_case__	,  snake_case__      ) or eos_token_id < 0:
																		raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''      )

												lowercase__    :				List[str]=	min_length
												lowercase__    :				Dict=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# create boolean flag to decide if min length penalty should be applied
												lowercase__    :				Tuple=	1 - jnp.clip(cur_len - self.min_length	,  0	,  1      )

												lowercase__    :				Dict=	jnp.where(snake_case__	,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	list(snake_case__      )
												lowercase__    :				List[Any]=	begin_index




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	1 - jnp.bool_(cur_len - self.begin_index      )

												lowercase__    :				str=	jnp.where(snake_case__	,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	list(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	dict(snake_case__      )
												# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
												# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
												# Indexes without forced tokens will have a negative value.
												lowercase__    :				List[Any]=	jnp.ones((max(force_token_map.keys()      ) + 1)	,  dtype=jnp.intaa      ) * -1
												for index, token in force_token_map.items():
																		if token is not None:
																								lowercase__    :				List[Any]=	force_token_array.at[index].set(snake_case__      )
												lowercase__    :				int=	jnp.intaa(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												def _force_token(snake_case__      ):
																		lowercase__    :				Dict=	scores.shape[0]
																		lowercase__    :				Any=	self.force_token_array[generation_idx]

																		lowercase__    :				List[Any]=	jnp.ones_like(snake_case__	,  dtype=scores.dtype      ) * -float("inf"      )
																		lowercase__    :				List[Any]=	jnp.zeros((batch_size, 1)	,  dtype=scores.dtype      )
																		lowercase__    :				List[str]=	lax.dynamic_update_slice(snake_case__	,  snake_case__	,  (0, current_token)      )
																		return new_scores

												lowercase__    :				Dict=	lax.cond(
												    cur_len >= self.force_token_array.shape[0]	,  lambda: scores	,  lambda: lax.cond(
												        self.force_token_array[cur_len] >= 0	,  lambda: _force_token(snake_case__      )	,  lambda: scores	,  )	,  )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	generate_config.eos_token_id
												lowercase__    :				Optional[int]=	generate_config.no_timestamps_token_id
												lowercase__    :				Dict=	generate_config.no_timestamps_token_id + 1

												lowercase__    :				List[Any]=	decoder_input_length + 1

												if generate_config.is_multilingual:
																		# room for language token and task token
																		self.begin_index += 2
												if hasattr(snake_case__	,  "max_initial_timestamp_index"      ):
																		lowercase__    :				int=	generate_config.max_initial_timestamp_index
												else:
																		lowercase__    :				Dict=	model_config.vocab_size
												if self.max_initial_timestamp_index is None:
																		lowercase__    :				str=	model_config.vocab_size




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# suppress <|notimestamps|> which is handled by without_timestamps
												lowercase__    :				int=	scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

												def handle_pairs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Union[str, Any]=	jnp.where((cur_len - self.begin_index) >= 1	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Tuple=	jnp.where(
																		    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  snake_case__	,  )

																		lowercase__    :				int=	jnp.where((cur_len - self.begin_index) < 2	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Optional[int]=	jnp.where(
																		    input_ids_k[cur_len - 2] >= self.timestamp_begin	,  snake_case__	,  snake_case__	,  )

																		return jnp.where(
																		    snake_case__	,  jnp.where(
																		        penultimate_was_timestamp > 0	,  scores_k.at[self.timestamp_begin :].set(-float("inf"      )      )	,  scores_k.at[: self.eos_token_id].set(-float("inf"      )      )	,  )	,  snake_case__	,  )

												lowercase__    :				List[str]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												lowercase__    :				str=	jnp.where(cur_len == self.begin_index	,  snake_case__	,  snake_case__      )
												lowercase__    :				List[Any]=	jnp.where(
												    self.max_initial_timestamp_index is not None	,  True and apply_max_initial_timestamp	,  snake_case__	,  )

												lowercase__    :				Any=	self.timestamp_begin + self.max_initial_timestamp_index

												lowercase__    :				str=	jnp.where(
												    snake_case__	,  scores.at[:, last_allowed + 1 :].set(-float("inf"      )      )	,  snake_case__	,  )

												# if sum of probability over timestamps is above any other token, sample timestamp
												lowercase__    :				str=	jax.nn.log_softmax(snake_case__	,  axis=-1      )

												def handle_cumulative_probs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Dict=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1      )
																		lowercase__    :				Union[str, Any]=	jnp.max(logprobs_k[: self.timestamp_begin]      )
																		return jnp.where(
																		    timestamp_logprob > max_text_token_logprob	,  scores_k.at[: self.timestamp_begin].set(-float("inf"      )      )	,  snake_case__	,  )

												lowercase__    :				Optional[int]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												return scores














"""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			:    List[str]				 =		{
    """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    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						@classmethod
						def 		UpperCAmelCase_	(       cls      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	TOKEN
												HfFolder.save_token(snake_case__      )




						@classmethod
						def 		UpperCAmelCase_	(       cls      ):
												'''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 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	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      )

												lowercase__    :				List[Any]=	BertConfig.from_pretrained(F'''{USER}/test-config'''      )
												for k, v in config.to_dict().items():
																		if k != "transformers_version":
																								self.assertEqual(snake_case__	,  getattr(snake_case__	,  snake_case__      )      )

        # 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(snake_case__	,  repo_id="test-config"	,  push_to_hub=snake_case__	,  use_auth_token=self._token      )

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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	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      )

												lowercase__    :				Optional[Any]=	BertConfig.from_pretrained("valid_org/test-config-org"      )
												for k, v in config.to_dict().items():
																		if k != "transformers_version":
																								self.assertEqual(snake_case__	,  getattr(snake_case__	,  snake_case__      )      )

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

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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												CustomConfig.register_for_auto_class()
												lowercase__    :				Union[str, Any]=	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"}      )

												lowercase__    :				List[str]=	AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''	,  trust_remote_code=snake_case__      )
												# 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 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	GPTaConfig()

												# attempt to modify each of int/float/bool/str config records and verify they were updated
												lowercase__    :				str=	c.n_embd + 1  # int
												lowercase__    :				Tuple=	c.resid_pdrop + 1.0  # float
												lowercase__    :				Union[str, Any]=	not c.scale_attn_weights  # bool
												lowercase__    :				Optional[Any]=	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(snake_case__	,  c.n_embd	,  "mismatch for key: n_embd"      )
												self.assertEqual(snake_case__	,  c.resid_pdrop	,  "mismatch for key: resid_pdrop"      )
												self.assertEqual(snake_case__	,  c.scale_attn_weights	,  "mismatch for key: scale_attn_weights"      )
												self.assertEqual(snake_case__	,  c.summary_type	,  "mismatch for key: summary_type"      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	PretrainedConfig()
												lowercase__    :				List[str]=	[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(
												    snake_case__	,  ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]      )
												lowercase__    :				Tuple=	[key for key, value in config_common_kwargs.items() if value == getattr(snake_case__	,  snake_case__      )]
												if len(snake_case__      ) > 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(snake_case__      )}.'''      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												with self.assertRaises(snake_case__      ):
																		# config is in subfolder, the following should not work without specifying the subfolder
																		lowercase__    :				Optional[int]=	BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"      )

												lowercase__    :				Optional[Any]=	BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"	,  subfolder="bert"      )

												self.assertIsNotNone(snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# A mock response for an HTTP head request to emulate server down
												lowercase__    :				str=	mock.Mock()
												lowercase__    :				Optional[Any]=	500
												lowercase__    :				Any=	{}
												lowercase__    :				Tuple=	HTTPError
												lowercase__    :				List[Any]=	{}

												# Download this model to make sure it's in the cache.
												lowercase__    :				Any=	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=snake_case__      ) as mock_head:
																		lowercase__    :				Any=	BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"      )
																		# This check we did call the fake head request
																		mock_head.assert_called()




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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				int=	AutoConfig.from_pretrained("bert-base-cased"      )
												lowercase__    :				Optional[int]=	["config.4.0.0.json"]

												with tempfile.TemporaryDirectory() as tmp_dir:
																		configuration.save_pretrained(snake_case__      )
																		lowercase__    :				List[Any]=	2
																		json.dump(configuration.to_dict()	,  open(os.path.join(snake_case__	,  "config.4.0.0.json"      )	,  "w"      )      )

																		# This should pick the new configuration file as the version of Transformers is > 4.0.0
																		lowercase__    :				int=	AutoConfig.from_pretrained(snake_case__      )
																		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
																		lowercase__    :				Optional[int]=	["config.42.0.0.json"]
																		lowercase__    :				int=	768
																		configuration.save_pretrained(snake_case__      )
																		shutil.move(os.path.join(snake_case__	,  "config.4.0.0.json"      )	,  os.path.join(snake_case__	,  "config.42.0.0.json"      )      )
																		lowercase__    :				Optional[Any]=	AutoConfig.from_pretrained(snake_case__      )
																		self.assertEqual(new_configuration.hidden_size	,  768      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
												lowercase__    :				Optional[Any]=	"hf-internal-testing/test-two-configs"

												import transformers as new_transformers

												lowercase__    :				Optional[Any]=	"v4.0.0"
												lowercase__,     lowercase__    :				str=	new_transformers.models.auto.AutoConfig.from_pretrained(
												    snake_case__	,  return_unused_kwargs=snake_case__      )
												self.assertEqual(new_configuration.hidden_size	,  2      )
												# This checks `_configuration_file` ia not kept in the kwargs by mistake.
												self.assertDictEqual(snake_case__	,  {}      )

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

												lowercase__    :				Dict=	"v3.0.0"
												lowercase__    :				Tuple=	old_transformers.models.auto.AutoConfig.from_pretrained(snake_case__      )
												self.assertEqual(old_configuration.hidden_size	,  768      )






