




"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_				:    List[str]										=     logging.get_logger(__name__)





def 						UpperCAmelCase				(			A__:    int     )  ->     int:
			__lowerCamelCase					:	Any	       =				DPTConfig()

			if "large" in checkpoint_url:
						__lowerCamelCase					:	Optional[Any]	       =				1024
						__lowerCamelCase					:	Tuple	       =				4096
						__lowerCamelCase					:	int	       =				24
						__lowerCamelCase					:	str	       =				16
						__lowerCamelCase					:	Optional[int]	       =				[5, 11, 17, 23]
						__lowerCamelCase					:	List[str]	       =				[256, 512, 1024, 1024]
						__lowerCamelCase					:	Optional[Any]	       =				(1, 384, 384)

			if "ade" in checkpoint_url:
						__lowerCamelCase					:	List[str]	       =				True

						__lowerCamelCase					:	Union[str, Any]	       =				150
						__lowerCamelCase					:	Tuple	       =				'huggingface/label-files'
						__lowerCamelCase					:	Tuple	       =				'ade20k-id2label.json'
						__lowerCamelCase					:	Optional[int]	       =				json.load(open(cached_download(hf_hub_url(A__							,				A__							,				repo_type='dataset'     )     )							,				'r'     )     )
						__lowerCamelCase					:	Tuple	       =				{int(A__     ): v for k, v in idalabel.items()}
						__lowerCamelCase					:	List[Any]	       =				idalabel
						__lowerCamelCase					:	List[str]	       =				{v: k for k, v in idalabel.items()}
						__lowerCamelCase					:	Optional[Any]	       =				[1, 150, 480, 480]

			return config, expected_shape





def 						UpperCAmelCase				(			A__:    str     )  ->     Tuple:
			__lowerCamelCase					:	Union[str, Any]	       =				['pretrained.model.head.weight', 'pretrained.model.head.bias']
			for k in ignore_keys:
						state_dict.pop(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int     )  ->     Optional[int]:
			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						__lowerCamelCase					:	Optional[int]	       =				name.replace('pretrained.model'							,				'dpt.encoder'     )
			if "pretrained.model" in name:
						__lowerCamelCase					:	List[Any]	       =				name.replace('pretrained.model'							,				'dpt.embeddings'     )
			if "patch_embed" in name:
						__lowerCamelCase					:	Union[str, Any]	       =				name.replace('patch_embed'							,				'patch_embeddings'     )
			if "pos_embed" in name:
						__lowerCamelCase					:	Union[str, Any]	       =				name.replace('pos_embed'							,				'position_embeddings'     )
			if "attn.proj" in name:
						__lowerCamelCase					:	Optional[Any]	       =				name.replace('attn.proj'							,				'attention.output.dense'     )
			if "proj" in name and "project" not in name:
						__lowerCamelCase					:	Tuple	       =				name.replace('proj'							,				'projection'     )
			if "blocks" in name:
						__lowerCamelCase					:	Tuple	       =				name.replace('blocks'							,				'layer'     )
			if "mlp.fc1" in name:
						__lowerCamelCase					:	Union[str, Any]	       =				name.replace('mlp.fc1'							,				'intermediate.dense'     )
			if "mlp.fc2" in name:
						__lowerCamelCase					:	List[Any]	       =				name.replace('mlp.fc2'							,				'output.dense'     )
			if "norm1" in name:
						__lowerCamelCase					:	str	       =				name.replace('norm1'							,				'layernorm_before'     )
			if "norm2" in name:
						__lowerCamelCase					:	int	       =				name.replace('norm2'							,				'layernorm_after'     )
			if "scratch.output_conv" in name:
						__lowerCamelCase					:	Any	       =				name.replace('scratch.output_conv'							,				'head'     )
			if "scratch" in name:
						__lowerCamelCase					:	Any	       =				name.replace('scratch'							,				'neck'     )
			if "layer1_rn" in name:
						__lowerCamelCase					:	str	       =				name.replace('layer1_rn'							,				'convs.0'     )
			if "layer2_rn" in name:
						__lowerCamelCase					:	List[Any]	       =				name.replace('layer2_rn'							,				'convs.1'     )
			if "layer3_rn" in name:
						__lowerCamelCase					:	Dict	       =				name.replace('layer3_rn'							,				'convs.2'     )
			if "layer4_rn" in name:
						__lowerCamelCase					:	List[Any]	       =				name.replace('layer4_rn'							,				'convs.3'     )
			if "refinenet" in name:
						__lowerCamelCase					:	Any	       =				int(name[len('neck.refinenet'     ) : len('neck.refinenet'     ) + 1]     )
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						__lowerCamelCase					:	Any	       =				name.replace(f'''refinenet{layer_idx}'''							,				f'''fusion_stage.layers.{abs(layer_idx-4     )}'''     )
			if "out_conv" in name:
						__lowerCamelCase					:	Optional[Any]	       =				name.replace('out_conv'							,				'projection'     )
			if "resConfUnit1" in name:
						__lowerCamelCase					:	Dict	       =				name.replace('resConfUnit1'							,				'residual_layer1'     )
			if "resConfUnit2" in name:
						__lowerCamelCase					:	List[str]	       =				name.replace('resConfUnit2'							,				'residual_layer2'     )
			if "conv1" in name:
						__lowerCamelCase					:	Optional[Any]	       =				name.replace('conv1'							,				'convolution1'     )
			if "conv2" in name:
						__lowerCamelCase					:	Optional[Any]	       =				name.replace('conv2'							,				'convolution2'     )
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						__lowerCamelCase					:	Union[str, Any]	       =				name.replace('pretrained.act_postprocess1.0.project.0'							,				'neck.reassemble_stage.readout_projects.0.0'     )
			if "pretrained.act_postprocess2.0.project.0" in name:
						__lowerCamelCase					:	Dict	       =				name.replace('pretrained.act_postprocess2.0.project.0'							,				'neck.reassemble_stage.readout_projects.1.0'     )
			if "pretrained.act_postprocess3.0.project.0" in name:
						__lowerCamelCase					:	Union[str, Any]	       =				name.replace('pretrained.act_postprocess3.0.project.0'							,				'neck.reassemble_stage.readout_projects.2.0'     )
			if "pretrained.act_postprocess4.0.project.0" in name:
						__lowerCamelCase					:	Optional[Any]	       =				name.replace('pretrained.act_postprocess4.0.project.0'							,				'neck.reassemble_stage.readout_projects.3.0'     )
			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						__lowerCamelCase					:	Dict	       =				name.replace('pretrained.act_postprocess1.3'							,				'neck.reassemble_stage.layers.0.projection'     )
			if "pretrained.act_postprocess1.4" in name:
						__lowerCamelCase					:	int	       =				name.replace('pretrained.act_postprocess1.4'							,				'neck.reassemble_stage.layers.0.resize'     )
			if "pretrained.act_postprocess2.3" in name:
						__lowerCamelCase					:	Union[str, Any]	       =				name.replace('pretrained.act_postprocess2.3'							,				'neck.reassemble_stage.layers.1.projection'     )
			if "pretrained.act_postprocess2.4" in name:
						__lowerCamelCase					:	Union[str, Any]	       =				name.replace('pretrained.act_postprocess2.4'							,				'neck.reassemble_stage.layers.1.resize'     )
			if "pretrained.act_postprocess3.3" in name:
						__lowerCamelCase					:	int	       =				name.replace('pretrained.act_postprocess3.3'							,				'neck.reassemble_stage.layers.2.projection'     )
			if "pretrained.act_postprocess4.3" in name:
						__lowerCamelCase					:	str	       =				name.replace('pretrained.act_postprocess4.3'							,				'neck.reassemble_stage.layers.3.projection'     )
			if "pretrained.act_postprocess4.4" in name:
						__lowerCamelCase					:	Dict	       =				name.replace('pretrained.act_postprocess4.4'							,				'neck.reassemble_stage.layers.3.resize'     )
			if "pretrained" in name:
						__lowerCamelCase					:	Tuple	       =				name.replace('pretrained'							,				'dpt'     )
			if "bn" in name:
						__lowerCamelCase					:	Dict	       =				name.replace('bn'							,				'batch_norm'     )
			if "head" in name:
						__lowerCamelCase					:	Any	       =				name.replace('head'							,				'head.head'     )
			if "encoder.norm" in name:
						__lowerCamelCase					:	str	       =				name.replace('encoder.norm'							,				'layernorm'     )
			if "auxlayer" in name:
						__lowerCamelCase					:	List[Any]	       =				name.replace('auxlayer'							,				'auxiliary_head.head'     )

			return name





def 						UpperCAmelCase				(			A__:    int							,				A__:    List[Any]     )  ->     List[Any]:
			for i in range(config.num_hidden_layers     ):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						__lowerCamelCase					:	List[str]	       =				state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''     )
						__lowerCamelCase					:	List[Any]	       =				state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''     )
						# next, add query, keys and values (in that order) to the state dict
						__lowerCamelCase					:	Optional[int]	       =				in_proj_weight[: config.hidden_size, :]
						__lowerCamelCase					:	int	       =				in_proj_bias[: config.hidden_size]
						__lowerCamelCase					:	str	       =				in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__lowerCamelCase					:	str	       =				in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__lowerCamelCase					:	Union[str, Any]	       =				in_proj_weight[
						    -config.hidden_size :, :
						]
						__lowerCamelCase					:	str	       =				in_proj_bias[-config.hidden_size :]





def 						UpperCAmelCase				(			)  ->     Union[str, Any]:
			__lowerCamelCase					:	Any	       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
			__lowerCamelCase					:	List[Any]	       =				Image.open(requests.get(A__							,				stream=A__     ).raw     )
			return im





@torch.no_grad()
def 						UpperCAmelCase				(			A__:    Dict							,				A__:    Tuple							,				A__:    Union[str, Any]							,				A__:    List[Any]     )  ->     Any:
			__lowerCamelCase					:	Union[str, Any]	       =				get_dpt_config(A__     )
			# load original state_dict from URL
			__lowerCamelCase					:	Any	       =				torch.hub.load_state_dict_from_url(A__							,				map_location='cpu'     )
			# remove certain keys
			remove_ignore_keys_(A__     )
			# rename keys
			for key in state_dict.copy().keys():
						__lowerCamelCase					:	str	       =				state_dict.pop(A__     )
						__lowerCamelCase					:	Any	       =				val
			# read in qkv matrices
			read_in_q_k_v(A__							,				A__     )

			# load HuggingFace model
			__lowerCamelCase					:	List[Any]	       =				DPTForSemanticSegmentation(A__     ) if 'ade' in checkpoint_url else DPTForDepthEstimation(A__     )
			model.load_state_dict(A__     )
			model.eval()

			# Check outputs on an image
			__lowerCamelCase					:	List[Any]	       =				480 if 'ade' in checkpoint_url else 384
			__lowerCamelCase					:	Optional[int]	       =				DPTImageProcessor(size=A__     )

			__lowerCamelCase					:	List[str]	       =				prepare_img()
			__lowerCamelCase					:	Any	       =				image_processor(A__							,				return_tensors='pt'     )

			# forward pass
			__lowerCamelCase					:	Optional[Any]	       =				model(**A__     ).logits if 'ade' in checkpoint_url else model(**A__     ).predicted_depth

			# Assert logits
			__lowerCamelCase					:	Any	       =				torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]     )
			if "ade" in checkpoint_url:
						__lowerCamelCase					:	str	       =				torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]     )
			assert outputs.shape == torch.Size(A__     )
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]							,				A__							,				atol=1E-4     )
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]							,				A__     )
			)

			Path(A__     ).mkdir(exist_ok=A__     )
			print(f'''Saving model to {pytorch_dump_folder_path}'''     )
			model.save_pretrained(A__     )
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''     )
			image_processor.save_pretrained(A__     )

			if push_to_hub:
						print('Pushing model to hub...'     )
						model.push_to_hub(
						    repo_path_or_name=Path(A__							,				A__     )							,				organization='nielsr'							,				commit_message='Add model'							,				use_temp_dir=A__							,				)
						image_processor.push_to_hub(
						    repo_path_or_name=Path(A__							,				A__     )							,				organization='nielsr'							,				commit_message='Add image processor'							,				use_temp_dir=A__							,				)


if __name__ == "__main__":
						a_				:    Any										=     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
						    type=str,
						    help='''URL of the original DPT checkpoint you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						)
						parser.add_argument(
						    '''--model_name''',
						    default='''dpt-large''',
						    type=str,
						    help='''Name of the model, in case you\'re pushing to the hub.''',
						)

						a_				:    Optional[Any]										=     parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









"""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     )



