




"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


a_				:    Optional[int]										=     logging.get_logger(__name__)

a_				:    Dict										=     {
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						int     		= 'bloom'
					__a						:						Optional[Any]     		= ['past_key_values']
					__a						:						Union[str, Any]     		= {
					    'num_hidden_layers': 'n_layer',
					    'num_attention_heads': 'n_head',
					}
					def __init__(    self						,	__a=250880						,	__a=64						,	__a=2						,	__a=8						,	__a=1E-5						,	__a=0.02						,	__a=True						,	__a=1						,	__a=2						,	__a=False						,	__a=0.0						,	__a=0.0						,	__a=1						,	__a=False						,	**__a						,	):
								__lowerCamelCase					:	Union[str, Any]	       =				vocab_size
								# Backward compatibility with n_embed kwarg
								__lowerCamelCase					:	List[str]	       =				kwargs.pop('n_embed'						,	__a     )
								__lowerCamelCase					:	Optional[Any]	       =				hidden_size if n_embed is None else n_embed
								__lowerCamelCase					:	Any	       =				n_layer
								__lowerCamelCase					:	str	       =				n_head
								__lowerCamelCase					:	Tuple	       =				layer_norm_epsilon
								__lowerCamelCase					:	Optional[Any]	       =				initializer_range
								__lowerCamelCase					:	Any	       =				use_cache
								__lowerCamelCase					:	str	       =				pretraining_tp
								__lowerCamelCase					:	Optional[Any]	       =				apply_residual_connection_post_layernorm
								__lowerCamelCase					:	Optional[int]	       =				hidden_dropout
								__lowerCamelCase					:	Any	       =				attention_dropout

								__lowerCamelCase					:	str	       =				bos_token_id
								__lowerCamelCase					:	Tuple	       =				eos_token_id
								__lowerCamelCase					:	Optional[Any]	       =				slow_but_exact

								super().__init__(bos_token_id=__a						,	eos_token_id=__a						,	**__a     )



class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[int]     		= version.parse('1.12'				)
					def __init__(    self						,	__a						,	__a = "default"						,	__a = None						,	__a = False						,	):
								super().__init__(__a						,	task=__a						,	patching_specs=__a						,	use_past=__a     )
								if not getattr(self._config						,	'pad_token_id'						,	__a     ):
											# TODO: how to do that better?
											__lowerCamelCase					:	List[str]	       =				0
					@property
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}     )
								if self.use_past:
											# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
											self.fill_with_past_key_values_(__a						,	direction='inputs'						,	inverted_values_shape=__a     )
											__lowerCamelCase					:	int	       =				{0: 'batch', 1: 'past_sequence + sequence'}
								else:
											__lowerCamelCase					:	int	       =				{0: 'batch', 1: 'sequence'}

								return common_inputs
					@property
					def     snake_case_				(    self     ):
								return self._config.n_layer
					@property
					def     snake_case_				(    self     ):
								return self._config.n_head
					@property
					def     snake_case_				(    self     ):
								return 1E-3
					def     snake_case_				(    self						,	__a						,	__a = -1						,	__a = -1						,	__a = False						,	__a = None						,	):
								__lowerCamelCase					:	Optional[Any]	       =				super(__a						,	self     ).generate_dummy_inputs(
								    __a						,	batch_size=__a						,	seq_length=__a						,	is_pair=__a						,	framework=__a     )

								# We need to order the input in the way they appears in the forward()
								__lowerCamelCase					:	int	       =				OrderedDict({'input_ids': common_inputs['input_ids']}     )

								# Need to add the past_keys
								if self.use_past:
											if not is_torch_available():
														raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
											else:
														import torch

														__lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				common_inputs['input_ids'].shape
														# Not using the same length for past_key_values
														__lowerCamelCase					:	Tuple	       =				seqlen + 2
														__lowerCamelCase					:	str	       =				self._config.hidden_size // self.num_attention_heads
														__lowerCamelCase					:	List[Any]	       =				(
														    batch * self.num_attention_heads,
														    head_dim,
														    past_key_values_length,
														)
														__lowerCamelCase					:	List[str]	       =				(
														    batch * self.num_attention_heads,
														    past_key_values_length,
														    head_dim,
														)
														__lowerCamelCase					:	int	       =				[
														    (torch.zeros(__a     ), torch.zeros(__a     )) for _ in range(self.num_layers     )
														]

								__lowerCamelCase					:	List[Any]	       =				common_inputs['attention_mask']
								if self.use_past:
											__lowerCamelCase					:	Optional[Any]	       =				ordered_inputs['attention_mask'].dtype
											__lowerCamelCase					:	List[str]	       =				torch.cat(
											    [ordered_inputs['attention_mask'], torch.ones(__a						,	__a						,	dtype=__a     )]						,	dim=1     )

								return ordered_inputs





					@property
					def     snake_case_				(    self     ):
								return 13









"""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_				:    Optional[int]										=     {
    '''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_28,
    '''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, 1.0_1),
    '''suppress_tokens''': [0, 1],
    '''begin_suppress_tokens''': 2,
    '''task_specific_params''': {'''translation''': '''some_params'''},
    '''problem_type''': '''regression''',
}

@is_staging_test
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					@classmethod
					def     snake_case_				(    cls     ):
								__lowerCamelCase					:	Tuple	       =				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					:	List[str]	       =				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					:	Any	       =				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					:	Any	       =				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					:	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     )

								__lowerCamelCase					:	Tuple	       =				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					:	Tuple	       =				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					:	Optional[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'}     )

								__lowerCamelCase					:	Tuple	       =				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 __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				GPTaConfig()

								# attempt to modify each of int/float/bool/str config records and verify they were updated
								__lowerCamelCase					:	List[str]	       =				c.n_embd + 1  # int
								__lowerCamelCase					:	Dict	       =				c.resid_pdrop + 1.0  # float
								__lowerCamelCase					:	int	       =				not c.scale_attn_weights  # bool
								__lowerCamelCase					:	Optional[int]	       =				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					:	Tuple	       =				PretrainedConfig()
								__lowerCamelCase					:	Dict	       =				[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					:	int	       =				[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					:	int	       =				BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'     )

								__lowerCamelCase					:	Optional[int]	       =				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					:	List[str]	       =				mock.Mock()
								__lowerCamelCase					:	Tuple	       =				500
								__lowerCamelCase					:	Tuple	       =				{}
								__lowerCamelCase					:	Optional[Any]	       =				HTTPError
								__lowerCamelCase					:	str	       =				{}

								# Download this model to make sure it's in the cache.
								__lowerCamelCase					:	Optional[int]	       =				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					:	Union[str, Any]	       =				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					:	Optional[Any]	       =				BertConfig.from_pretrained(
								    'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				AutoConfig.from_pretrained('bert-base-cased'     )
								__lowerCamelCase					:	str	       =				['config.4.0.0.json']

								with tempfile.TemporaryDirectory() as tmp_dir:
											configuration.save_pretrained(__a     )
											__lowerCamelCase					:	Optional[int]	       =				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					:	Any	       =				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					:	Any	       =				['config.42.0.0.json']
											__lowerCamelCase					:	Tuple	       =				768
											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					:	Dict	       =				AutoConfig.from_pretrained(__a     )
											self.assertEqual(new_configuration.hidden_size						,	768     )





					def     snake_case_				(    self     ):
								# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
								__lowerCamelCase					:	List[str]	       =				'hf-internal-testing/test-two-configs'

								import transformers as new_transformers

								__lowerCamelCase					:	Tuple	       =				'v4.0.0'
								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				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					:	Union[str, Any]	       =				'v3.0.0'
								__lowerCamelCase					:	Optional[int]	       =				old_transformers.models.auto.AutoConfig.from_pretrained(__a     )
								self.assertEqual(old_configuration.hidden_size						,	768     )



