





"""simple docstring"""






from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


A	     =					logging.get_logger(__name__)

A	     =					{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 			__lowercase     (     UpperCamelCase_							):







     '''simple docstring'''




     __lowerCAmelCase					=					"""perceiver"""







     def __init__(  self		,     _UpperCAmelCase=256		,     _UpperCAmelCase=1280		,     _UpperCAmelCase=768		,     _UpperCAmelCase=1		,     _UpperCAmelCase=26		,     _UpperCAmelCase=8		,     _UpperCAmelCase=8		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase="kv"		,     _UpperCAmelCase=1		,     _UpperCAmelCase=1		,     _UpperCAmelCase="gelu"		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=1e-1_2		,     _UpperCAmelCase=True		,     _UpperCAmelCase=262		,     _UpperCAmelCase=2048		,     _UpperCAmelCase=56		,     _UpperCAmelCase=[368, 496]		,     _UpperCAmelCase=16		,     _UpperCAmelCase=1920		,     _UpperCAmelCase=16		,     _UpperCAmelCase=[1, 16, 224, 224]		,     **_UpperCAmelCase		,     ):
         super().__init__(**_a       )

         __a	:  Optional[Any]         =					num_latents
         __a	:  Tuple         =					d_latents
         __a	:  Optional[int]         =					d_model
         __a	:  List[Any]         =					num_blocks
         __a	:  str         =					num_self_attends_per_block
         __a	:  str         =					num_self_attention_heads
         __a	:  Dict         =					num_cross_attention_heads
         __a	:  Dict         =					qk_channels
         __a	:  List[Any]         =					v_channels
         __a	:  Any         =					cross_attention_shape_for_attention
         __a	:  List[Any]         =					self_attention_widening_factor
         __a	:  Tuple         =					cross_attention_widening_factor
         __a	:  Tuple         =					hidden_act
         __a	:  Dict         =					attention_probs_dropout_prob
         __a	:  List[str]         =					initializer_range
         __a	:  str         =					layer_norm_eps
         __a	:  int         =					use_query_residual
         # masked language modeling attributes
         __a	:  int         =					vocab_size
         __a	:  Optional[int]         =					max_position_embeddings
         # image classification attributes
         __a	:  str         =					image_size
         # flow attributes
         __a	:  int         =					train_size
         # multimodal autoencoding attributes
         __a	:  Optional[Any]         =					num_frames
         __a	:  Optional[int]         =					audio_samples_per_frame
         __a	:  Union[str, Any]         =					samples_per_patch
         __a	:  Optional[int]         =					output_shape






class 			__lowercase     (     UpperCamelCase_							):







     '''simple docstring'''




     @property
     def 	_lowerCamelCase						(  self       ):


         if self.task == "multiple-choice":
             __a	:  str         =					{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
             __a	:  List[Any]         =					{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ('''inputs''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]       )







     @property
     def 	_lowerCamelCase						(  self       ):
         return 1e-4







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = -1		,     _UpperCAmelCase = -1		,     _UpperCAmelCase = -1		,     _UpperCAmelCase = False		,     _UpperCAmelCase = None		,     _UpperCAmelCase = 3		,     _UpperCAmelCase = 40		,     _UpperCAmelCase = 40		,     ):


         if isinstance(_a		,     _a       ):
             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             __a	:  Optional[int]         =					compute_effective_axis_dimension(
                 _a		,     fixed_dimension=OnnxConfig.default_fixed_batch		,     num_token_to_add=0       )
             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             __a	:  List[str]         =					preprocessor.num_special_tokens_to_add(_a       )
             __a	:  Union[str, Any]         =					compute_effective_axis_dimension(
                 _a		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=_a       )
             # Generate dummy inputs according to compute batch and sequence
             __a	:  str         =					[""" """.join(['''a''']       ) * seq_length] * batch_size
             __a	:  Optional[int]         =					dict(preprocessor(_a		,     return_tensors=_a       )       )
             __a	:  List[str]         =					inputs.pop('''input_ids'''       )
             return inputs
         elif isinstance(_a		,     _a       ) and preprocessor.model_input_names[0] == "pixel_values":
             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             __a	:  Dict         =					compute_effective_axis_dimension(_a		,     fixed_dimension=OnnxConfig.default_fixed_batch       )
             __a	:  Any         =					self._generate_dummy_images(_a		,     _a		,     _a		,     _a       )
             __a	:  Optional[Any]         =					dict(preprocessor(images=_a		,     return_tensors=_a       )       )
             __a	:  Dict         =					inputs.pop('''pixel_values'''       )
             return inputs
         else:
             raise ValueError(
                 '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''       )






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					['''image_processor''', '''tokenizer''']
					__lowerCAmelCase					=					'''CLIPImageProcessor'''
					__lowerCAmelCase					=					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')







					def __init__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									__a	:  Optional[Any]         =					None
									if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''		,     _UpperCAmelCase		,     )
													__a	:  Any         =					kwargs.pop('''feature_extractor'''       )

									__a	:  Optional[Any]         =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
													raise ValueError('''You need to specify an `image_processor`.'''       )
									if tokenizer is None:
													raise ValueError('''You need to specify a `tokenizer`.'''       )

									super().__init__(_UpperCAmelCase		,     _UpperCAmelCase       )







					def __call__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):

									if text is None and images is None:
													raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

									if text is not None:
													__a	:  Any         =					self.tokenizer(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if images is not None:
													__a	:  List[str]         =					self.image_processor(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if text is not None and images is not None:
													__a	:  Optional[int]         =					image_features.pixel_values
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**_UpperCAmelCase       )		,     tensor_type=_UpperCAmelCase       )







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







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







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.tokenizer.model_input_names
									__a	:  Dict         =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )