




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

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
      import torch





class     SCREAMING_SNAKE_CASE__		(TensorFormatter[Mapping, "torch.Tensor", Mapping]			):
     def __init__(						self	:		str       ,						__lowerCamelCase	:		List[str]=None       ,						**__lowerCamelCase	:		Union[str, Any]						):



       """simple docstring"""
       super().__init__(features=__lowerCamelCase						)
       lowerCAmelCase__										=      torch_tensor_kwargs
       import torch  # noqa import torch at initialization
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		Dict						):



       """simple docstring"""
       import torch

       if isinstance(__lowerCamelCase       ,						__lowerCamelCase						) and column:
         if all(
             isinstance(__lowerCamelCase       ,						torch.Tensor						) and x.shape == column[0].shape and x.dtype == column[0].dtype
             for x in column						):
           return torch.stack(__lowerCamelCase						)
       return column
     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Union[str, Any]						):



       """simple docstring"""
       import torch

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

       lowerCAmelCase__										=      {}

       if isinstance(__lowerCamelCase       ,						(np.number, np.ndarray)						) and np.issubdtype(value.dtype       ,						np.integer						):
         lowerCAmelCase__										=      {'''dtype''': torch.intaa}
       elif isinstance(__lowerCamelCase       ,						(np.number, np.ndarray)						) and np.issubdtype(value.dtype       ,						np.floating						):
         lowerCAmelCase__										=      {'''dtype''': torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(__lowerCamelCase       ,						PIL.Image.Image						):
           lowerCAmelCase__										=      np.asarray(__lowerCamelCase						)
       return torch.tensor(__lowerCamelCase       ,						**{**default_dtype, **self.torch_tensor_kwargs}						)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Any						):



       """simple docstring"""
       import torch

       # support for torch, tf, jax etc.
       if hasattr(__lowerCamelCase       ,						'''__array__'''						) and not isinstance(__lowerCamelCase       ,						torch.Tensor						):
         lowerCAmelCase__										=      data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(__lowerCamelCase       ,						np.ndarray						):
         if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
           return self._consolidate([self.recursive_tensorize(__lowerCamelCase						) for substruct in data_struct]						)
       elif isinstance(__lowerCamelCase       ,						(list, tuple)						):
         return self._consolidate([self.recursive_tensorize(__lowerCamelCase						) for substruct in data_struct]						)
       return self._tensorize(__lowerCamelCase						)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		dict						):



       """simple docstring"""
       return map_nested(self._recursive_tensorize       ,						__lowerCamelCase       ,						map_list=__lowerCamelCase						)
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		pa.Table						):



       """simple docstring"""
       lowerCAmelCase__										=      self.numpy_arrow_extractor().extract_row(__lowerCamelCase						)
       lowerCAmelCase__										=      self.python_features_decoder.decode_row(__lowerCamelCase						)
       return self.recursive_tensorize(__lowerCamelCase						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		pa.Table						):



       """simple docstring"""
       lowerCAmelCase__										=      self.numpy_arrow_extractor().extract_column(__lowerCamelCase						)
       lowerCAmelCase__										=      self.python_features_decoder.decode_column(__lowerCamelCase       ,						pa_table.column_names[0]						)
       lowerCAmelCase__										=      self.recursive_tensorize(__lowerCamelCase						)
       lowerCAmelCase__										=      self._consolidate(__lowerCamelCase						)
       return column



     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		pa.Table						):



       """simple docstring"""
       lowerCAmelCase__										=      self.numpy_arrow_extractor().extract_batch(__lowerCamelCase						)
       lowerCAmelCase__										=      self.python_features_decoder.decode_batch(__lowerCamelCase						)
       lowerCAmelCase__										=      self.recursive_tensorize(__lowerCamelCase						)
       for column_name in batch:
         lowerCAmelCase__										=      self._consolidate(batch[column_name]						)
       return batch







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

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


__magic_name__  :  List[str]					=       logging.get_logger(__name__)  # pylint: disable=invalid-name





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[Any]=7_68						):



       """simple docstring"""
       super().__init__(__lowerCamelCase						)
       lowerCAmelCase__										=      proj_size

       lowerCAmelCase__										=      CLIPVisionModel(__lowerCamelCase						)
       lowerCAmelCase__										=      PaintByExampleMapper(__lowerCamelCase						)
       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        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str=False						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model(pixel_values=__lowerCamelCase						)
       lowerCAmelCase__										=      clip_output.pooler_output
       lowerCAmelCase__										=      self.mapper(latent_states[:, None]						)
       lowerCAmelCase__										=      self.final_layer_norm(__lowerCamelCase						)
       lowerCAmelCase__										=      self.proj_out(__lowerCamelCase						)
       if return_uncond_vector:
         return latent_states, self.uncond_vector

       return latent_states





class     SCREAMING_SNAKE_CASE__		(nn.Module			):
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       super().__init__()
       lowerCAmelCase__										=      (config.num_hidden_layers + 1) // 5
       lowerCAmelCase__										=      config.hidden_size
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						activation_fn='''gelu'''       ,						attention_bias=__lowerCamelCase						)
               for _ in range(__lowerCamelCase						)
           ]						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       for block in self.blocks:
         lowerCAmelCase__										=      block(__lowerCamelCase						)

       return hidden_states

