



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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A:     int										=	logging.get_logger(__name__)

A:     Tuple										=	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		'imagegpt'
   __lowerCAmelCase						:     int		=		['past_key_values']
   __lowerCAmelCase						:     Dict		=		{
       'hidden_size': 'n_embd',
       'max_position_embeddings': 'n_positions',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=512 + 1   ,  _SCREAMING_SNAKE_CASE=32 * 32   ,  _SCREAMING_SNAKE_CASE=512   ,  _SCREAMING_SNAKE_CASE=24   ,  _SCREAMING_SNAKE_CASE=8   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="quick_gelu"   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=1E-5   ,  _SCREAMING_SNAKE_CASE=0.02   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   str:





     '''simple docstring'''




     UpperCAmelCase			:      Tuple			=			vocab_size
     UpperCAmelCase			:      Union[str, Any]			=			n_positions
     UpperCAmelCase			:      Union[str, Any]			=			n_embd
     UpperCAmelCase			:      str			=			n_layer
     UpperCAmelCase			:      int			=			n_head
     UpperCAmelCase			:      Any			=			n_inner
     UpperCAmelCase			:      Union[str, Any]			=			activation_function
     UpperCAmelCase			:      List[Any]			=			resid_pdrop
     UpperCAmelCase			:      Union[str, Any]			=			embd_pdrop
     UpperCAmelCase			:      str			=			attn_pdrop
     UpperCAmelCase			:      List[str]			=			layer_norm_epsilon
     UpperCAmelCase			:      List[str]			=			initializer_range
     UpperCAmelCase			:      Dict			=			scale_attn_weights
     UpperCAmelCase			:      List[str]			=			use_cache
     UpperCAmelCase			:      Any			=			scale_attn_by_inverse_layer_idx
     UpperCAmelCase			:      str			=			reorder_and_upcast_attn
     UpperCAmelCase			:      int			=			tie_word_embeddings

     super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Mapping[str, Mapping[int, str]]:





     '''simple docstring'''




     return OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """sequence"""}),
         ]		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 1   ,  _SCREAMING_SNAKE_CASE = -1   ,  _SCREAMING_SNAKE_CASE = False   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 3   ,  _SCREAMING_SNAKE_CASE = 32   ,  _SCREAMING_SNAKE_CASE = 32   ,  )						->   Mapping[str, Any]:





     '''simple docstring'''




     UpperCAmelCase			:      str			=			self._generate_dummy_images(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Optional[Any]			=			dict(preprocessor(images=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE		)		)

     return inputs





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files"""    ,	[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Optional[Any]		):
 UpperCAmelCase			:      List[Any]			=			tmp_path_factory.mktemp("""dset_infos_dir"""		)
 if "full:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""		)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write(""""""		)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / """dataset_infos.json"""    ,	"""w"""		) as f:
   f.write("""{\"default\": {\"dataset_size\": 42}}"""		)
 UpperCAmelCase			:      str			=			DatasetInfosDict.from_directory(UpperCamelCase		)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info"""    ,	[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	DatasetInfo		):
 UpperCAmelCase			:      List[Any]			=			str(UpperCamelCase		)
 dataset_info.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			DatasetInfo.from_directory(UpperCamelCase		)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(UpperCamelCase    ,	"""dataset_info.json"""		)		)

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo(
     description="""foo"""    ,	citation="""bar"""    ,	homepage="""https://foo.bar"""    ,	license="""CC0"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	post_processed={}    ,	supervised_keys=()    ,	task_templates=[]    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train""", """num_examples""": 42}]    ,	download_checksums={}    ,	download_size=1337    ,	post_processing_size=442    ,	dataset_size=1234    ,	size_in_bytes=1337 + 442 + 1234    ,	)
 UpperCAmelCase			:      str			=			dataset_info._to_yaml_dict()
 assert sorted(UpperCamelCase		) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML		)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]    ,	(list, dict, int, str)		)
 UpperCAmelCase			:      Union[str, Any]			=			yaml.safe_dump(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			yaml.safe_load(UpperCamelCase		)
 assert dataset_info_yaml_dict == reloaded

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo()
 UpperCAmelCase			:      Union[str, Any]			=			dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    """dataset_infos_dict"""    ,	[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}		),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}		),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	)
            }		),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42		),
                """v2""": DatasetInfo(dataset_size=1337		),
            }		),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	DatasetInfosDict		):
 UpperCAmelCase			:      List[str]			=			str(UpperCamelCase		)
 dataset_infos_dict.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			DatasetInfosDict.from_directory(UpperCamelCase		)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  UpperCAmelCase			:      Union[str, Any]			=			config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  UpperCAmelCase			:      Optional[Any]			=			DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()		)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(UpperCamelCase    ,	"""README.md"""		)		)
