






from typing import Mapping

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


A_						:						str						      =					logging.get_logger(__name__)

A_						:						int						      =					{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						int										='''umt5'''
       a							:						Optional[Any]										=['''past_key_values''']





       def __init__(   self    ,     _lowerCamelCase=2_5_0_1_1_2    ,     _lowerCamelCase=5_1_2    ,     _lowerCamelCase=6_4    ,     _lowerCamelCase=1_0_2_4    ,     _lowerCamelCase=8    ,     _lowerCamelCase=None    ,     _lowerCamelCase=6    ,     _lowerCamelCase=3_2    ,     _lowerCamelCase=1_2_8    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=1e-6    ,     _lowerCamelCase=1.0    ,     _lowerCamelCase="gated-gelu"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase="T5Tokenizer"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=0    ,     _lowerCamelCase=1    ,     _lowerCamelCase=0    ,     **_lowerCamelCase    ,     ):
         super().__init__(
             is_encoder_decoder=_lowerCamelCase    ,     tokenizer_class=_lowerCamelCase    ,     tie_word_embeddings=_lowerCamelCase    ,     pad_token_id=_lowerCamelCase    ,     eos_token_id=_lowerCamelCase    ,     decoder_start_token_id=_lowerCamelCase    ,     **_lowerCamelCase    ,     )
         UpperCamelCase_:   str        =							vocab_size
         UpperCamelCase_:   Any        =							d_model
         UpperCamelCase_:   Any        =							d_kv
         UpperCamelCase_:   Optional[Any]        =							d_ff
         UpperCamelCase_:   str        =							num_layers
         UpperCamelCase_:   Tuple        =							(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCamelCase_:   Optional[Any]        =							num_heads
         UpperCamelCase_:   List[str]        =							relative_attention_num_buckets
         UpperCamelCase_:   Union[str, Any]        =							relative_attention_max_distance
         UpperCamelCase_:   List[str]        =							dropout_rate
         UpperCamelCase_:   str        =							layer_norm_epsilon
         UpperCamelCase_:   Dict        =							initializer_factor
         UpperCamelCase_:   Optional[int]        =							feed_forward_proj
         UpperCamelCase_:   List[Any]        =							use_cache

         UpperCamelCase_:   Dict        =							self.feed_forward_proj.split('-'				)
         UpperCamelCase_:   List[str]        =							act_info[-1]
         UpperCamelCase_:   str        =							act_info[0] == 'gated'

         if len(_lowerCamelCase				) > 1 and act_info[0] != "gated" or len(_lowerCamelCase				) > 2:
           raise ValueError(
               f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
               '\'gated-gelu\' or \'relu\''				)

         if feed_forward_proj == "gated-gelu":
           UpperCamelCase_:   int        =							'gelu_new'





       @property
       def      _a       (   self				):
         return self.d_model





       @property
       def      _a       (   self				):
         return self.num_heads





       @property
       def      _a       (   self				):
         return self.num_layers







class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							{
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
           UpperCamelCase_:   Tuple        =							'past_encoder_sequence + sequence'
           UpperCamelCase_:   Any        =							{0: 'batch'}
           UpperCamelCase_:   Optional[int]        =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
           UpperCamelCase_:   Tuple        =							{0: 'batch', 1: 'decoder_sequence'}
           UpperCamelCase_:   Any        =							{0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
           self.fill_with_past_key_values_(_lowerCamelCase    ,     direction='inputs'				)

         return common_inputs





       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def      _a       (   self				):
         return 1_3





       @property
       def      _a       (   self				):
         return 5e-4



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


lowerCAmelCase__    :Dict       =				logging.get_logger(__name__)

lowerCAmelCase__    :Optional[int]       =				{
    '''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 						__a (							UpperCAmelCase     ):
		_a      :    Tuple           =      'perceiver'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=256     ,       _SCREAMING_SNAKE_CASE=1280     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=26     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE="kv"     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=262     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=56     ,       _SCREAMING_SNAKE_CASE=[368, 496]     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=1920     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=[1, 16, 224, 224]     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     num_latents
									_UpperCAmelCase											=     d_latents
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     num_blocks
									_UpperCAmelCase											=     num_self_attends_per_block
									_UpperCAmelCase											=     num_self_attention_heads
									_UpperCAmelCase											=     num_cross_attention_heads
									_UpperCAmelCase											=     qk_channels
									_UpperCAmelCase											=     v_channels
									_UpperCAmelCase											=     cross_attention_shape_for_attention
									_UpperCAmelCase											=     self_attention_widening_factor
									_UpperCAmelCase											=     cross_attention_widening_factor
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     use_query_residual
									# masked language modeling attributes
									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									# image classification attributes
									_UpperCAmelCase											=     image_size
									# flow attributes
									_UpperCAmelCase											=     train_size
									# multimodal autoencoding attributes
									_UpperCAmelCase											=     num_frames
									_UpperCAmelCase											=     audio_samples_per_frame
									_UpperCAmelCase											=     samples_per_patch
									_UpperCAmelCase											=     output_shape



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('inputs', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
		@property
		def       UpperCAmelCase__							(     self		)					->			float:




									"""simple docstring"""





									return 1e-4






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 3     ,       _SCREAMING_SNAKE_CASE = 40     ,       _SCREAMING_SNAKE_CASE = 40     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





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