


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A  :     Union[str, Any]    					=    {
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     Optional[int]    					=    [
	    "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "XCLIPModel",
	    "XCLIPPreTrainedModel",
	    "XCLIPTextModel",
	    "XCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	A  :     Union[str, Any]    					=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""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




