



import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  Union[str, Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  int=None						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  List[Any]=None						, **__UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											A      						=    parent
											A      						=    config_class
											A      						=    has_text_modality
											A      						=    kwargs
											A      						=    common_properties


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Tuple:
											A      						=    self.config_class(**self.inputs_dict       )
											A      						=    (
											    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
											    if self.common_properties is None
											    else self.common_properties
											)

											# Add common fields for text models
											if self.has_text_modality:
																		common_properties.extend(['vocab_size']       )

											# Test that config has the common properties as getters
											for prop in common_properties:
																		self.parent.assertTrue(hasattr(__UpperCamelCase						, __UpperCamelCase       )						, msg=f'''`{prop}` does not exist'''       )

											# Test that config has the common properties as setter
											for idx, name in enumerate(__UpperCamelCase       ):
																		try:
																									setattr(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )
																									self.parent.assertEqual(
																									    getattr(__UpperCamelCase						, __UpperCamelCase       )						, __UpperCamelCase						, msg=f'''`{name} value {idx} expected, but was {getattr(__UpperCamelCase						, __UpperCamelCase       )}'''       )
																		except NotImplementedError:
																									# Some models might not be able to implement setters for common_properties
																									# In that case, a NotImplementedError is raised
																									pass

        # Test if config class can be called with Config(prop_name=..)
											for idx, name in enumerate(__UpperCamelCase       ):
																		try:
																									A      						=    self.config_class(**{name: idx}       )
																									self.parent.assertEqual(
																									    getattr(__UpperCamelCase						, __UpperCamelCase       )						, __UpperCamelCase						, msg=f'''`{name} value {idx} expected, but was {getattr(__UpperCamelCase						, __UpperCamelCase       )}'''       )
																		except NotImplementedError:
																									# Some models might not be able to implement setters for common_properties
																									# In that case, a NotImplementedError is raised
																									pass


				def __UpperCamelCase						(							self      :  List[Any]       )		->     str:
											A      						=    self.config_class(**self.inputs_dict       )
											A      						=    json.loads(config.to_json_string()       )
											for key, value in self.inputs_dict.items():
																		self.parent.assertEqual(obj[key]						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[int]:
											A      						=    self.config_class(**self.inputs_dict       )

											with tempfile.TemporaryDirectory() as tmpdirname:
																		A      						=    os.path.join(__UpperCamelCase						, 'config.json'       )
																		config_first.to_json_file(__UpperCamelCase       )
																		A      						=    self.config_class.from_json_file(__UpperCamelCase       )

											self.parent.assertEqual(config_second.to_dict()						, config_first.to_dict()       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     str:
											A      						=    self.config_class(**self.inputs_dict       )

											with tempfile.TemporaryDirectory() as tmpdirname:
																		config_first.save_pretrained(__UpperCamelCase       )
																		A      						=    self.config_class.from_pretrained(__UpperCamelCase       )

											self.parent.assertEqual(config_second.to_dict()						, config_first.to_dict()       )


				def __UpperCamelCase						(							self      :  Dict       )		->     Optional[Any]:
											A      						=    self.config_class(**self.inputs_dict       )

											A      						=    'test'
											with tempfile.TemporaryDirectory() as tmpdirname:
																		A      						=    os.path.join(__UpperCamelCase						, __UpperCamelCase       )
																		config_first.save_pretrained(__UpperCamelCase       )
																		A      						=    self.config_class.from_pretrained(__UpperCamelCase						, subfolder=__UpperCamelCase       )

											self.parent.assertEqual(config_second.to_dict()						, config_first.to_dict()       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Tuple:
											A      						=    self.config_class(**self.inputs_dict						, num_labels=5       )
											self.parent.assertEqual(len(config.idalabel       )						, 5       )
											self.parent.assertEqual(len(config.labelaid       )						, 5       )

											A      						=    3
											self.parent.assertEqual(len(config.idalabel       )						, 3       )
											self.parent.assertEqual(len(config.labelaid       )						, 3       )


				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											if self.config_class.is_composition:
																		return
											A      						=    self.config_class()
											self.parent.assertIsNotNone(__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Dict:
											A      						=    copy.deepcopy(__UpperCamelCase       )
											A      						=    self.config_class(**__UpperCamelCase       )
											A      						=    []
											for key, value in config_common_kwargs.items():
																		if key == "torch_dtype":
																									if not is_torch_available():
																																continue
																									else:
																																import torch

																																if config.torch_dtype != torch.floataa:
																																							wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)       )
																		elif getattr(__UpperCamelCase						, __UpperCamelCase       ) != value:
																									wrong_values.append((key, getattr(__UpperCamelCase						, __UpperCamelCase       ), value)       )

											if len(__UpperCamelCase       ) > 0:
																		A      						=    '\n'.join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]       )
																		raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[int]:
											self.create_and_test_config_common_properties()
											self.create_and_test_config_to_json_string()
											self.create_and_test_config_to_json_file()
											self.create_and_test_config_from_and_save_pretrained()
											self.create_and_test_config_from_and_save_pretrained_subfolder()
											self.create_and_test_config_with_num_labels()
											self.check_config_can_be_init_without_params()
											self.check_config_arguments_init()




from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  int						, __UpperCamelCase      :  Callable						, __UpperCamelCase      :  Optional[Features] = None						, __UpperCamelCase      :  str = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  Optional[dict] = None						, __UpperCamelCase      :  Optional[int] = None						, **__UpperCamelCase      :  str						, )		->     List[str]:
											super().__init__(
											    features=__UpperCamelCase						, cache_dir=__UpperCamelCase						, keep_in_memory=__UpperCamelCase						, streaming=__UpperCamelCase						, num_proc=__UpperCamelCase						, **__UpperCamelCase						, )
											A      						=    Generator(
											    cache_dir=__UpperCamelCase						, features=__UpperCamelCase						, generator=__UpperCamelCase						, gen_kwargs=__UpperCamelCase						, **__UpperCamelCase						, )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											# Build iterable dataset
											if self.streaming:
																		A      						=    self.builder.as_streaming_dataset(split='train'       )
											# Build regular (map-style) dataset
											else:
																		A      						=    None
																		A      						=    None
																		A      						=    None
																		A      						=    None

																		self.builder.download_and_prepare(
																		    download_config=__UpperCamelCase						, download_mode=__UpperCamelCase						, verification_mode=__UpperCamelCase						, base_path=__UpperCamelCase						, num_proc=self.num_proc						, )
																		A      						=    self.builder.as_dataset(
																		    split='train'						, verification_mode=__UpperCamelCase						, in_memory=self.keep_in_memory       )
											return dataset