


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  :     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 						_UpperCamelCase						(     __lowercase	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[str]          ='''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						(     __lowercase	):
							'''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 flax.linen as nn
import jax
import jax.numpy as jnp
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    hidden_states.shape
										__lowerCAmelCase       					=    jax.image.resize(
										    __a					,     shape=(batch, height * 2, width * 2, channels)					,     method="nearest"					,     )
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(2, 2)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
										# hidden_states = jnp.pad(hidden_states, pad_width=pad)
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					int          =None
							__UpperCAmelCase		:					float          =0.0
							__UpperCAmelCase		:					bool          =None
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.in_channels if self.out_channels is None else self.out_channels

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    nn.Dense(__a					,     dtype=self.dtype			)

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Dropout(self.dropout_prob			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

										__lowerCAmelCase       					=    None
										if use_nin_shortcut:
													__lowerCAmelCase       					=    nn.Conv(
													    __a					,     kernel_size=(1, 1)					,     strides=(1, 1)					,     padding="VALID"					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a					,     __a					,     __a=True			):
										__lowerCAmelCase       					=    hidden_states
										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										__lowerCAmelCase       					=    self.time_emb_proj(nn.swish(__a			)			)
										__lowerCAmelCase       					=    jnp.expand_dims(jnp.expand_dims(__a					,     1			)					,     1			)
										__lowerCAmelCase       					=    hidden_states + temb

										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.dropout(__a					,     __a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										if self.conv_shortcut is not None:
													__lowerCAmelCase       					=    self.conv_shortcut(__a			)

										return hidden_states + residual




