


"""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	:  List[str]            =     logging.get_logger(__name__)

A	:  Dict            =     {
    "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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 ="""perceiver"""

							def __init__(	self       ,  __a=2_56       ,  __a=12_80       ,  __a=7_68       ,  __a=1       ,  __a=26       ,  __a=8       ,  __a=8       ,  __a=None       ,  __a=None       ,  __a="kv"       ,  __a=1       ,  __a=1       ,  __a="gelu"       ,  __a=0.1       ,  __a=0.0_2       ,  __a=1e-1_2       ,  __a=True       ,  __a=2_62       ,  __a=20_48       ,  __a=56       ,  __a=[3_68, 4_96]       ,  __a=16       ,  __a=19_20       ,  __a=16       ,  __a=[1, 16, 2_24, 2_24]       ,  **__a       ,  ):
										super().__init__(**__a      )

										__lowerCAmelCase							       =     num_latents
										__lowerCAmelCase							       =     d_latents
										__lowerCAmelCase							       =     d_model
										__lowerCAmelCase							       =     num_blocks
										__lowerCAmelCase							       =     num_self_attends_per_block
										__lowerCAmelCase							       =     num_self_attention_heads
										__lowerCAmelCase							       =     num_cross_attention_heads
										__lowerCAmelCase							       =     qk_channels
										__lowerCAmelCase							       =     v_channels
										__lowerCAmelCase							       =     cross_attention_shape_for_attention
										__lowerCAmelCase							       =     self_attention_widening_factor
										__lowerCAmelCase							       =     cross_attention_widening_factor
										__lowerCAmelCase							       =     hidden_act
										__lowerCAmelCase							       =     attention_probs_dropout_prob
										__lowerCAmelCase							       =     initializer_range
										__lowerCAmelCase							       =     layer_norm_eps
										__lowerCAmelCase							       =     use_query_residual
										# masked language modeling attributes
										__lowerCAmelCase							       =     vocab_size
										__lowerCAmelCase							       =     max_position_embeddings
										# image classification attributes
										__lowerCAmelCase							       =     image_size
										# flow attributes
										__lowerCAmelCase							       =     train_size
										# multimodal autoencoding attributes
										__lowerCAmelCase							       =     num_frames
										__lowerCAmelCase							       =     audio_samples_per_frame
										__lowerCAmelCase							       =     samples_per_patch
										__lowerCAmelCase							       =     output_shape
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

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



							def      snake_case							(	self       ,  __a       ,  __a = -1       ,  __a = -1       ,  __a = -1       ,  __a = False       ,  __a = None       ,  __a = 3       ,  __a = 40       ,  __a = 40       ,  ):
										# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

										if isinstance(__a       ,  __a      ):
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													__lowerCAmelCase							       =     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
													__lowerCAmelCase							       =     preprocessor.num_special_tokens_to_add(__a      )
													__lowerCAmelCase							       =     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
													__lowerCAmelCase							       =     [" ".join(["a"]      ) * seq_length] * batch_size
													__lowerCAmelCase							       =     dict(preprocessor(__a       ,  return_tensors=__a      )      )
													__lowerCAmelCase							       =     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
													__lowerCAmelCase							       =     compute_effective_axis_dimension(__a       ,  fixed_dimension=OnnxConfig.default_fixed_batch      )
													__lowerCAmelCase							       =     self._generate_dummy_images(__a       ,  __a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     dict(preprocessor(images=__a       ,  return_tensors=__a      )      )
													__lowerCAmelCase							       =     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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]      )
										__lowerCAmelCase							       =     get_activation("gelu"      )
										self.assertTrue(torch.allclose(gelu_python(__a      )       ,  torch_builtin(__a      )      )      )
										self.assertFalse(torch.allclose(gelu_python(__a      )       ,  gelu_new(__a      )      )      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]      )
										__lowerCAmelCase							       =     get_activation("gelu"      )
										__lowerCAmelCase							       =     get_activation("gelu_10"      )

										__lowerCAmelCase							       =     torch_builtin(__a      )
										__lowerCAmelCase							       =     geluaa(__a      )

										__lowerCAmelCase							       =     torch.where(y_gelu_aa < 1_0.0       ,  1       ,  0      )

										self.assertTrue(torch.max(__a      ).item() == 1_0.0      )
										self.assertTrue(torch.allclose(y_gelu * clipped_mask       ,  y_gelu_aa * clipped_mask      )      )

							def      snake_case							(	self      ):
										get_activation("gelu"      )
										get_activation("gelu_10"      )
										get_activation("gelu_fast"      )
										get_activation("gelu_new"      )
										get_activation("gelu_python"      )
										get_activation("gelu_pytorch_tanh"      )
										get_activation("linear"      )
										get_activation("mish"      )
										get_activation("quick_gelu"      )
										get_activation("relu"      )
										get_activation("sigmoid"      )
										get_activation("silu"      )
										get_activation("swish"      )
										get_activation("tanh"      )
										with self.assertRaises(__a      ):
													get_activation("bogus"      )
										with self.assertRaises(__a      ):
													get_activation(__a      )



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     get_activation("gelu"      )
										__lowerCAmelCase							       =     1
										__lowerCAmelCase							       =     get_activation("gelu"      )
										self.assertEqual(acta.a       ,  1      )
										with self.assertRaises(__a      ):
													__lowerCAmelCase							       =     acta.a




