


"""simple docstring"""


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


A  :     int    					=    logging.get_logger(__name__)


A  :     Union[str, Any]    					=    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A  :     Optional[Any]    					=    {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

A  :     Union[str, Any]    					=    {
    "allenai/led-base-16384": 1_6_3_8_4,
}
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Union[str, Any]          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					Optional[Any]          =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					Optional[int]          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					Union[str, Any]          =LEDTokenizer
							__UpperCAmelCase		:					Optional[Any]          =["""input_ids""", """attention_mask"""]

							def __init__(  self					,     __a=None					,     __a=None					,     __a=None					,     __a="replace"					,     __a="<s>"					,     __a="</s>"					,     __a="</s>"					,     __a="<s>"					,     __a="<unk>"					,     __a="<pad>"					,     __a="<mask>"					,     __a=False					,     __a=True					,     **__a					,     ):
										super().__init__(
										    __a					,     __a					,     tokenizer_file=__a					,     errors=__a					,     bos_token=__a					,     eos_token=__a					,     sep_token=__a					,     cls_token=__a					,     unk_token=__a					,     pad_token=__a					,     mask_token=__a					,     add_prefix_space=__a					,     trim_offsets=__a					,     **__a					,     )

										__lowerCAmelCase       					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
										if pre_tok_state.get("add_prefix_space"					,     __a			) != add_prefix_space:
													__lowerCAmelCase       					=    getattr(__a					,     pre_tok_state.pop("type"			)			)
													__lowerCAmelCase       					=    add_prefix_space
													__lowerCAmelCase       					=    pre_tok_class(**__a			)

										__lowerCAmelCase       					=    add_prefix_space

										# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
										__lowerCAmelCase       					=    "post_processor"
										__lowerCAmelCase       					=    getattr(self.backend_tokenizer					,     __a					,     __a			)
										if tokenizer_component_instance:
													__lowerCAmelCase       					=    json.loads(tokenizer_component_instance.__getstate__()			)

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																__lowerCAmelCase       					=    tuple(state["sep"]			)
													if "cls" in state:
																__lowerCAmelCase       					=    tuple(state["cls"]			)

													__lowerCAmelCase       					=    False

													if state.get("add_prefix_space"					,     __a			) != add_prefix_space:
																__lowerCAmelCase       					=    add_prefix_space
																__lowerCAmelCase       					=    True

													if state.get("trim_offsets"					,     __a			) != trim_offsets:
																__lowerCAmelCase       					=    trim_offsets
																__lowerCAmelCase       					=    True

													if changes_to_apply:
																__lowerCAmelCase       					=    getattr(__a					,     state.pop("type"			)			)
																__lowerCAmelCase       					=    component_class(**__a			)
																setattr(self.backend_tokenizer					,     __a					,     __a			)

							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def     snake_case       (  self			):
										if self._mask_token is None:
													if self.verbose:
																logger.error("Using mask_token, but it is not set yet."			)
													return None
										return str(self._mask_token			)

							@mask_token.setter
							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    AddedToken(__a					,     lstrip=__a					,     rstrip=__a			) if isinstance(__a					,     __a			) else value
										__lowerCAmelCase       					=    value

							def     snake_case       (  self					,     *__a					,     **__a			):
										__lowerCAmelCase       					=    kwargs.get("is_split_into_words"					,     __a			)

										if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
													    "to use it with pretokenized inputs."			)

										return super()._batch_encode_plus(*__a					,     **__a			)

							def     snake_case       (  self					,     *__a					,     **__a			):
										__lowerCAmelCase       					=    kwargs.get("is_split_into_words"					,     __a			)

										if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
													    "to use it with pretokenized inputs."			)

										return super()._encode_plus(*__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    self._tokenizer.model.save(__a					,     name=__a			)
										return tuple(__a			)

							def     snake_case       (  self					,     __a					,     __a=None			):
										__lowerCAmelCase       					=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
													return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    [self.sep_token_id]
										__lowerCAmelCase       					=    [self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep			) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]



							def     snake_case       (  self					,     __a					,     __a = None					,     __a = PaddingStrategy.DO_NOT_PAD					,     __a = None					,     __a = None					,     ):
										__lowerCAmelCase       					=    super()._pad(
										    encoded_inputs=__a					,     max_length=__a					,     padding_strategy=__a					,     pad_to_multiple_of=__a					,     return_attention_mask=__a					,     )

										# Load from model defaults
										if return_attention_mask is None:
													__lowerCAmelCase       					=    "attention_mask" in self.model_input_names

										if return_attention_mask and "global_attention_mask" in encoded_inputs:
													__lowerCAmelCase       					=    encoded_inputs[self.model_input_names[0]]
													# `global_attention_mask` need to have the same length as other (sequential) inputs.
													__lowerCAmelCase       					=    len(encoded_inputs["global_attention_mask"]			) != len(__a			)

													if needs_to_be_padded:
																__lowerCAmelCase       					=    len(__a			) - len(encoded_inputs["global_attention_mask"]			)

																if self.padding_side == "right":
																			# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																			__lowerCAmelCase       					=    (
																			    encoded_inputs["global_attention_mask"] + [-1] * difference
																			)
																elif self.padding_side == "left":
																			__lowerCAmelCase       					=    [-1] * difference + encoded_inputs[
																			    "global_attention_mask"
																			]
																else:
																			raise ValueError("Invalid padding strategy:" + str(self.padding_side			)			)

										return encoded_inputs








"""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			)




