


'''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		=logging.get_logger(__name__)

__A		={
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}


class 						_snake_case   (       a__		):
					lowerCAmelCase :List[str]						=   '''imagegpt'''
					lowerCAmelCase :str						=   ['''past_key_values''']
					lowerCAmelCase :List[Any]						=   {
					    '''hidden_size''': '''n_embd''',
					    '''max_position_embeddings''': '''n_positions''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}




					def __init__( self							,							_lowerCamelCase=512 + 1							,							_lowerCamelCase=32 * 32							,							_lowerCamelCase=512							,							_lowerCamelCase=24							,							_lowerCamelCase=8							,							_lowerCamelCase=None							,							_lowerCamelCase="quick_gelu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=1e-5							,							_lowerCamelCase=0.02							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase=False							,							_lowerCamelCase=False							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       vocab_size
								UpperCAmelCase__					:   List[str]       				=       n_positions
								UpperCAmelCase__					:   Any       				=       n_embd
								UpperCAmelCase__					:   Tuple       				=       n_layer
								UpperCAmelCase__					:   Optional[Any]       				=       n_head
								UpperCAmelCase__					:   str       				=       n_inner
								UpperCAmelCase__					:   List[Any]       				=       activation_function
								UpperCAmelCase__					:   Dict       				=       resid_pdrop
								UpperCAmelCase__					:   Optional[Any]       				=       embd_pdrop
								UpperCAmelCase__					:   Tuple       				=       attn_pdrop
								UpperCAmelCase__					:   Any       				=       layer_norm_epsilon
								UpperCAmelCase__					:   Optional[int]       				=       initializer_range
								UpperCAmelCase__					:   Dict       				=       scale_attn_weights
								UpperCAmelCase__					:   int       				=       use_cache
								UpperCAmelCase__					:   List[str]       				=       scale_attn_by_inverse_layer_idx
								UpperCAmelCase__					:   List[str]       				=       reorder_and_upcast_attn
								UpperCAmelCase__					:   Tuple       				=       tie_word_embeddings

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


class 						_snake_case   (       a__		):




					@property
					def 			snake_case__  ( self):
								return OrderedDict(
								    [
								        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
								    ])




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = 1							,							_lowerCamelCase = -1							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 32							,							):
								UpperCAmelCase__					:   Dict       				=       self._generate_dummy_images(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       dict(preprocessor(images=_lowerCamelCase							,							return_tensors=_lowerCamelCase))

								return inputs



'''simple docstring'''



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       self.tokenizer(
								    text=_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							stride=_lowerCamelCase							,							pad_to_multiple_of=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							return_overflowing_tokens=_lowerCamelCase							,							return_special_tokens_mask=_lowerCamelCase							,							return_offsets_mapping=_lowerCamelCase							,							return_length=_lowerCamelCase							,							verbose=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase							,							)
								# add pixel_values + pixel_mask
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))