

import flax.linen as nn
import jax
import jax.numpy as jnp







class 	lowercase    (				nn.Module							):


		"""simple docstring"""




		a__					       =  42
		a__					       =  jnp.floataa


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						nn.Conv(
			    self.out_channels	,       kernel_size=(3, 3)	,       strides=(1, 1)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )






		def __call__(				self	,       __snake_case):
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		str			=						hidden_states.shape
			_UpperCamelCase   :		Union[str, Any]			=						jax.image.resize(
			    __snake_case	,       shape=(batch, height * 2, width * 2, channels)	,       method='nearest'	,       )
			_UpperCamelCase   :		Dict			=						self.conv(__snake_case)
			return hidden_states







class 	lowercase    (				nn.Module							):


		"""simple docstring"""




		a__					       =  42
		a__					       =  jnp.floataa


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						nn.Conv(
			    self.out_channels	,       kernel_size=(3, 3)	,       strides=(2, 2)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )






		def __call__(				self	,       __snake_case):
			# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
			# hidden_states = jnp.pad(hidden_states, pad_width=pad)
			_UpperCamelCase   :		Any			=						self.conv(__snake_case)
			return hidden_states







class 	lowercase    (				nn.Module							):


		"""simple docstring"""




		a__					       =  42
		a__					       =  None
		a__					       =  0.0
		a__					       =  None
		a__					       =  jnp.floataa


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						self.in_channels if self.out_channels is None else self.out_channels

			_UpperCamelCase   :		Any			=						nn.GroupNorm(num_groups=32	,       epsilon=1e-5)
			_UpperCamelCase   :		Tuple			=						nn.Conv(
			    __snake_case	,       kernel_size=(3, 3)	,       strides=(1, 1)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )

			_UpperCamelCase   :		int			=						nn.Dense(__snake_case	,       dtype=self.dtype)

			_UpperCamelCase   :		Union[str, Any]			=						nn.GroupNorm(num_groups=32	,       epsilon=1e-5)
			_UpperCamelCase   :		Tuple			=						nn.Dropout(self.dropout_prob)
			_UpperCamelCase   :		List[str]			=						nn.Conv(
			    __snake_case	,       kernel_size=(3, 3)	,       strides=(1, 1)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )

			_UpperCamelCase   :		str			=						self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

			_UpperCamelCase   :		str			=						None
			if use_nin_shortcut:
				_UpperCamelCase   :		Optional[int]			=						nn.Conv(
				    __snake_case	,       kernel_size=(1, 1)	,       strides=(1, 1)	,       padding='VALID'	,       dtype=self.dtype	,       )






		def __call__(				self	,       __snake_case	,       __snake_case	,       __snake_case=True):
			_UpperCamelCase   :		str			=						hidden_states
			_UpperCamelCase   :		Optional[Any]			=						self.norma(__snake_case)
			_UpperCamelCase   :		List[Any]			=						nn.swish(__snake_case)
			_UpperCamelCase   :		List[Any]			=						self.conva(__snake_case)

			_UpperCamelCase   :		List[str]			=						self.time_emb_proj(nn.swish(__snake_case))
			_UpperCamelCase   :		Dict			=						jnp.expand_dims(jnp.expand_dims(__snake_case	,       1)	,       1)
			_UpperCamelCase   :		str			=						hidden_states + temb

			_UpperCamelCase   :		Optional[int]			=						self.norma(__snake_case)
			_UpperCamelCase   :		List[Any]			=						nn.swish(__snake_case)
			_UpperCamelCase   :		Optional[Any]			=						self.dropout(__snake_case	,       __snake_case)
			_UpperCamelCase   :		Tuple			=						self.conva(__snake_case)

			if self.conv_shortcut is not None:
				_UpperCamelCase   :		Dict			=						self.conv_shortcut(__snake_case)

			return hidden_states + residual







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="absolute"	,       __snake_case=True	,       __snake_case=None	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

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







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




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




