


"""simple docstring"""


# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import jax
	import jaxlib

A  :     str    					=    get_logger()

A  :     Optional[dict]    					=    None
class 						_UpperCamelCase						(     TensorFormatter[Mapping, """jax.Array""", Mapping]	):
							'''simple docstring'''







							def __init__(  self					,     __a=None					,     __a=None					,     **__a			):
										super().__init__(features=__a			)
										import jax
										from jaxlib.xla_client import Device

										if isinstance(__a					,     __a			):
													raise ValueError(
													    f"Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` "
													    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
													    "the device with `str()` to get its string identifier that will be internally mapped "
													    "to the actual `jaxlib.xla_extension.Device`."			)
										__lowerCAmelCase       					=    device if isinstance(__a					,     __a			) else str(jax.devices()[0]			)
										# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
										# with `pickle` nor with `dill`, so we need to use a global variable instead
										global DEVICE_MAPPING
										if DEVICE_MAPPING is None:
													__lowerCAmelCase       					=    self._map_devices_to_str()
										if self.device not in list(DEVICE_MAPPING.keys()			):
													logger.warning(
													    f"Device with string identifier {self.device} not listed among the available "
													    f"devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default "
													    f"device: {str(jax.devices()[0]			)}."			)
													__lowerCAmelCase       					=    str(jax.devices()[0]			)
										__lowerCAmelCase       					=    jnp_array_kwargs

							@staticmethod
							def     snake_case       (  ):
										import jax

										return {str(__a			): device for device in jax.devices()}

							def     snake_case       (  self					,     __a			):
										import jax
										import jax.numpy as jnp

										if isinstance(__a					,     __a			) and column:
													if all(
													    isinstance(__a					,     jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
																return jnp.stack(__a					,     axis=0			)
										return column

							def     snake_case       (  self					,     __a			):
										import jax
										import jax.numpy as jnp

										if isinstance(__a					,     (str, bytes, type(__a			))			):
													return value
										elif isinstance(__a					,     (np.character, np.ndarray)			) and np.issubdtype(value.dtype					,     np.character			):
													return value.tolist()

										__lowerCAmelCase       					=    {}

										if isinstance(__a					,     (np.number, np.ndarray)			) and np.issubdtype(value.dtype					,     np.integer			):
													# the default int precision depends on the jax config
													# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
													if jax.config.jax_enable_xaa:
																__lowerCAmelCase       					=    {"dtype": jnp.intaa}
													else:
																__lowerCAmelCase       					=    {"dtype": jnp.intaa}
										elif isinstance(__a					,     (np.number, np.ndarray)			) and np.issubdtype(value.dtype					,     np.floating			):
													__lowerCAmelCase       					=    {"dtype": jnp.floataa}
										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
													import PIL.Image

													if isinstance(__a					,     PIL.Image.Image			):
																__lowerCAmelCase       					=    np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
										global DEVICE_MAPPING
										if DEVICE_MAPPING is None:
													__lowerCAmelCase       					=    self._map_devices_to_str()

										with jax.default_device(DEVICE_MAPPING[self.device]			):
													# calling jnp.array on a np.ndarray does copy the data
													# see https://github.com/google/jax/issues/4486
													return jnp.array(__a					,     **{**default_dtype, **self.jnp_array_kwargs}			)

							def     snake_case       (  self					,     __a			):
										import jax

										# support for torch, tf, jax etc.
										if config.TORCH_AVAILABLE and "torch" in sys.modules:
													import torch

													if isinstance(__a					,     torch.Tensor			):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
										if hasattr(__a					,     "__array__"			) and not isinstance(__a					,     jax.Array			):
													__lowerCAmelCase       					=    data_struct.__array__()
										# support for nested types like struct of list of struct
										if isinstance(__a					,     np.ndarray			):
													if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
										elif isinstance(__a					,     (list, tuple)			):
													return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
										return self._tensorize(__a			)

							def     snake_case       (  self					,     __a			):
										return map_nested(self._recursive_tensorize					,     __a					,     map_list=__a			)

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.numpy_arrow_extractor().extract_row(__a			)
										__lowerCAmelCase       					=    self.python_features_decoder.decode_row(__a			)
										return self.recursive_tensorize(__a			)

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.numpy_arrow_extractor().extract_column(__a			)
										__lowerCAmelCase       					=    self.python_features_decoder.decode_column(__a					,     pa_table.column_names[0]			)
										__lowerCAmelCase       					=    self.recursive_tensorize(__a			)
										__lowerCAmelCase       					=    self._consolidate(__a			)
										return column



							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.numpy_arrow_extractor().extract_batch(__a			)
										__lowerCAmelCase       					=    self.python_features_decoder.decode_batch(__a			)
										__lowerCAmelCase       					=    self.recursive_tensorize(__a			)
										for column_name in batch:
													__lowerCAmelCase       					=    self._consolidate(batch[column_name]			)
										return batch








"""simple docstring"""


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7_68			):
										super().__init__(__a			)
										__lowerCAmelCase       					=    proj_size

										__lowerCAmelCase       					=    CLIPVisionModel(__a			)
										__lowerCAmelCase       					=    PaintByExampleMapper(__a			)
										__lowerCAmelCase       					=    nn.LayerNorm(config.hidden_size			)
										__lowerCAmelCase       					=    nn.Linear(config.hidden_size					,     self.proj_size			)

										# uncondition for scaling
										__lowerCAmelCase       					=    nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)



							def     snake_case       (  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    self.model(pixel_values=__a			)
										__lowerCAmelCase       					=    clip_output.pooler_output
										__lowerCAmelCase       					=    self.mapper(latent_states[:, None]			)
										__lowerCAmelCase       					=    self.final_layer_norm(__a			)
										__lowerCAmelCase       					=    self.proj_out(__a			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a			):
										super().__init__()
										__lowerCAmelCase       					=    (config.num_hidden_layers + 1) // 5
										__lowerCAmelCase       					=    config.hidden_size
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    nn.ModuleList(
										    [
										        BasicTransformerBlock(__a					,     __a					,     __a					,     activation_fn="gelu"					,     attention_bias=__a			)
										        for _ in range(__a			)
										    ]			)



							def     snake_case       (  self					,     __a			):
										for block in self.blocks:
													__lowerCAmelCase       					=    block(__a			)

										return hidden_states




