



from collections import OrderedDict
from typing import Mapping

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


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'camembert'







							def __init__(   self,			__a=3_0522,			__a=768,			__a=12,			__a=12,			__a=3072,			__a="gelu",			__a=0.1,			__a=0.1,			__a=512,			__a=2,			__a=0.02,			__a=1E-12,			__a=1,			__a=0,			__a=2,			__a="absolute",			__a=True,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(pad_token_id=__a,			bos_token_id=__a,			eos_token_id=__a,			**__a)

														_lowerCAmelCase     :							Optional[Any]    =    vocab_size
														_lowerCAmelCase     :							Optional[int]    =    hidden_size
														_lowerCAmelCase     :							Union[str, Any]    =    num_hidden_layers
														_lowerCAmelCase     :							Optional[int]    =    num_attention_heads
														_lowerCAmelCase     :							Tuple    =    hidden_act
														_lowerCAmelCase     :							Tuple    =    intermediate_size
														_lowerCAmelCase     :							Optional[Any]    =    hidden_dropout_prob
														_lowerCAmelCase     :							Tuple    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							List[Any]    =    max_position_embeddings
														_lowerCAmelCase     :							Optional[Any]    =    type_vocab_size
														_lowerCAmelCase     :							str    =    initializer_range
														_lowerCAmelCase     :							List[Any]    =    layer_norm_eps
														_lowerCAmelCase     :							int    =    position_embedding_type
														_lowerCAmelCase     :							Dict    =    use_cache
														_lowerCAmelCase     :							Dict    =    classifier_dropout



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														if self.task == "multiple-choice":
																					_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "choice", 2: "sequence"}
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    {0: "batch", 1: "sequence"}
														return OrderedDict(
														    [
														        ("input_ids", dynamic_axis),
														        ("attention_mask", dynamic_axis),
														    ])




import flax.linen as nn
import jax
import jax.numpy as jnp
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             a_					:		Dict							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Optional[int]:
             a_							,		a_							,		a_							,		a_					:		Union[str, Any]							=  hidden_states.shape
             a_					:		List[str]							=  jax.image.resize(
                 SCREAMING_SNAKE_CASE__     ,							shape=(batch, height * 2, width * 2, channels)     ,							method='nearest'     ,							)
             a_					:		Any							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[int]:
             a_					:		Optional[int]							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(2, 2)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[int]:
             # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
             # hidden_states = jnp.pad(hidden_states, pad_width=pad)
             a_					:		str							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     int        =						None
      snake_case__				:     float        =						0.0
      snake_case__				:     bool        =						None
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             a_					:		List[str]							=  self.in_channels if self.out_channels is None else self.out_channels

             a_					:		Optional[int]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		Any							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		Optional[int]							=  nn.Dense(SCREAMING_SNAKE_CASE__     ,							dtype=self.dtype     )

             a_					:		Union[str, Any]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		int							=  nn.Dropout(self.dropout_prob     )
             a_					:		Optional[Any]							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		List[str]							=  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

             a_					:		List[Any]							=  None
             if use_nin_shortcut:
                    a_					:		Union[str, Any]							=  nn.Conv(
                        SCREAMING_SNAKE_CASE__     ,							kernel_size=(1, 1)     ,							strides=(1, 1)     ,							padding='VALID'     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     )		->			int:
             a_					:		List[Any]							=  hidden_states
             a_					:		Any							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  self.time_emb_proj(nn.swish(SCREAMING_SNAKE_CASE__     )     )
             a_					:		List[str]							=  jnp.expand_dims(jnp.expand_dims(SCREAMING_SNAKE_CASE__     ,							1     )     ,							1     )
             a_					:		Optional[int]							=  hidden_states + temb

             a_					:		List[str]							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  self.dropout(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             if self.conv_shortcut is not None:
                    a_					:		List[str]							=  self.conv_shortcut(SCREAMING_SNAKE_CASE__     )

             return hidden_states + residual


