





import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def      A__       (    )  ->  str:






   """simple docstring"""







   _UpperCAmelCase           =   '''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
   _UpperCAmelCase           =   Image.open(requests.get(SCREAMING_SNAKE_CASE_				,	stream=SCREAMING_SNAKE_CASE_						).raw						).convert('''RGB'''						)

   return image
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]						)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   []
   # fmt: off

   # vision encoder
   rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')						)
   rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')						)
   rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')						)
   rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')						)
   rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')						)
   rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')						)

   for i in range(config.vision_config.num_hidden_layers						):
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')						)
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')						)

   # QFormer
   rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''')						)
   rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''')						)

   # fmt: on
   return rename_keys
def      A__       (    SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]						)  ->  List[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   dct.pop(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   val
def      A__       (    SCREAMING_SNAKE_CASE_   :      Tuple				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  Union[str, Any]:






   """simple docstring"""







   for i in range(config.vision_config.num_hidden_layers						):
      # read in original q and v biases
      _UpperCAmelCase           =   state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''						)
      _UpperCAmelCase           =   state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''						)

      # next, set bias in the state dict
      _UpperCAmelCase           =   torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE_				,	requires_grad=SCREAMING_SNAKE_CASE_						), v_bias)						)
      _UpperCAmelCase           =   qkv_bias
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   3_64 if '''coco''' in model_name else 2_24
   _UpperCAmelCase           =   InstructBlipVisionConfig(image_size=SCREAMING_SNAKE_CASE_						).to_dict()

   # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
   # seems like flan-T5 models don't have bos_token_id properly set?
   if "t5-xl" in model_name:
      _UpperCAmelCase           =   TaConfig.from_pretrained('''google/flan-t5-xl'''				,	dense_act_fn='''gelu'''				,	bos_token_id=1						).to_dict()
   elif "t5-xxl" in model_name:
      _UpperCAmelCase           =   TaConfig.from_pretrained('''google/flan-t5-xxl'''				,	dense_act_fn='''gelu'''				,	bos_token_id=1						).to_dict()
   elif "vicuna-7b" in model_name:
      _UpperCAmelCase           =   LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''				,	vocab_size=3_20_01						).to_dict()
   elif "vicuna-13b" in model_name:
      _UpperCAmelCase           =   LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''				,	vocab_size=3_20_01						).to_dict()
   else:
      raise ValueError('''Model name not supported'''						)

   # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
   _UpperCAmelCase           =   InstructBlipQFormerConfig(vocab_size=3_05_23						).to_dict()
   _UpperCAmelCase           =   InstructBlipConfig(vision_config=SCREAMING_SNAKE_CASE_				,	text_config=SCREAMING_SNAKE_CASE_				,	qformer_config=SCREAMING_SNAKE_CASE_						)

   return config, image_size
@torch.no_grad()
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict				,	SCREAMING_SNAKE_CASE_   :      List[Any]=None				,	SCREAMING_SNAKE_CASE_   :      Dict=False						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   AutoTokenizer.from_pretrained('''bert-base-uncased'''				,	truncation_side='''left'''						)
   qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}						)

   if "t5" in model_name:
      _UpperCAmelCase           =   TaTokenizerFast.from_pretrained('''google/flan-t5-xl'''				,	truncation_side='''left'''						)
   elif "vicuna" in model_name:
      # the following was used in the original implementation:
      # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
      # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      # tokenizer.add_special_tokens({"bos_token": "</s>"})
      # tokenizer.add_special_tokens({"eos_token": "</s>"})
      # tokenizer.add_special_tokens({"unk_token": "</s>"})
      _UpperCAmelCase           =   LlamaTokenizerFast.from_pretrained(
          '''huggyllama/llama-7b'''				,	truncation_side='''left'''				,	bos_token='''</s>'''				,	unk_token='''</s>'''						)
      tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}						)

   _UpperCAmelCase       ,      _UpperCAmelCase           =   get_blipa_config(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   InstructBlipForConditionalGeneration(SCREAMING_SNAKE_CASE_						).eval()

   _UpperCAmelCase           =   {
       '''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
       '''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
       '''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
       '''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
   }

   _UpperCAmelCase       ,      _UpperCAmelCase           =   model_name_to_original[model_name]

   # load original model
   print('''Loading original model...'''						)
   _UpperCAmelCase           =   '''cuda:1''' if torch.cuda.is_available() else '''cpu'''
   _UpperCAmelCase           =   '''cuda:2''' if torch.cuda.is_available() else '''cpu'''
   _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   load_model_and_preprocess(
       name=SCREAMING_SNAKE_CASE_				,	model_type=SCREAMING_SNAKE_CASE_				,	is_eval=SCREAMING_SNAKE_CASE_				,	device=SCREAMING_SNAKE_CASE_						)
   original_model.eval()
   print('''Done!'''						)

   # update state dict keys
   _UpperCAmelCase           =   original_model.state_dict()
   _UpperCAmelCase           =   create_rename_keys(SCREAMING_SNAKE_CASE_						)
   for src, dest in rename_keys:
      rename_key(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      _UpperCAmelCase           =   state_dict.pop(SCREAMING_SNAKE_CASE_						)
      if key.startswith('''Qformer.bert'''						):
         _UpperCAmelCase           =   key.replace('''Qformer.bert'''				,	'''qformer'''						)
      if "attention.self" in key:
         _UpperCAmelCase           =   key.replace('''self'''				,	'''attention'''						)
      if "llm_proj" in key:
         _UpperCAmelCase           =   key.replace('''llm_proj'''				,	'''language_projection'''						)
      if "t5_proj" in key:
         _UpperCAmelCase           =   key.replace('''t5_proj'''				,	'''language_projection'''						)
      if key.startswith('''llm_model'''						):
         _UpperCAmelCase           =   key.replace('''llm_model'''				,	'''language_model'''						)
      if key.startswith('''t5'''						):
         _UpperCAmelCase           =   key.replace('''t5'''				,	'''language'''						)
      _UpperCAmelCase           =   val

   # read in qv biases
   read_in_q_v_bias(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   # note: weights get loaded in torch.float32 by default
   hf_model.load_state_dict(SCREAMING_SNAKE_CASE_				,	strict=SCREAMING_SNAKE_CASE_						)

   _UpperCAmelCase           =   load_demo_image()
   _UpperCAmelCase           =   '''What is unusual about this image?'''

   # create processor
   _UpperCAmelCase           =   BlipImageProcessor(
       size={'''height''': image_size, '''width''': image_size}				,	image_mean=SCREAMING_SNAKE_CASE_				,	image_std=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   InstructBlipProcessor(
       image_processor=SCREAMING_SNAKE_CASE_				,	tokenizer=SCREAMING_SNAKE_CASE_				,	qformer_tokenizer=SCREAMING_SNAKE_CASE_				,	)
   _UpperCAmelCase           =   processor(images=SCREAMING_SNAKE_CASE_				,	text=SCREAMING_SNAKE_CASE_				,	return_tensors='''pt'''						).to(SCREAMING_SNAKE_CASE_						)

   # make sure processor creates exact same pixel values
   _UpperCAmelCase           =   vis_processors['''eval'''](SCREAMING_SNAKE_CASE_						).unsqueeze(0						).to(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device						)				,	SCREAMING_SNAKE_CASE_						)

   original_model.to(SCREAMING_SNAKE_CASE_						)
   hf_model.to(SCREAMING_SNAKE_CASE_						)
   with torch.no_grad():
      if "vicuna" in model_name:
         _UpperCAmelCase           =   original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}						).logits
         _UpperCAmelCase           =   hf_model(**SCREAMING_SNAKE_CASE_						).logits
      else:
         _UpperCAmelCase           =   original_model(
             {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}						).logits
         _UpperCAmelCase           =   tokenizer('''\n'''				,	return_tensors='''pt'''						).input_ids.to(SCREAMING_SNAKE_CASE_						)
         _UpperCAmelCase           =   label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id				,	-1_00						)
         _UpperCAmelCase           =   hf_model(**SCREAMING_SNAKE_CASE_				,	labels=SCREAMING_SNAKE_CASE_						).logits

   print('''First values of original logits:'''				,	original_logits[0, :3, :3]						)
   print('''First values of HF logits:'''				,	logits[0, :3, :3]						)

   # assert values
   assert original_logits.shape == logits.shape
   _UpperCAmelCase           =   1E-4 if '''vicuna''' in model_name else 1E-5
   assert torch.allclose(original_logits.to(logits.device						)				,	SCREAMING_SNAKE_CASE_				,	atol=SCREAMING_SNAKE_CASE_						)
   print('''Looks ok!'''						)

   print('''Generating with original model...'''						)
   _UpperCAmelCase           =   original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt}				,	num_beams=5						)

   # important: we need to cast the weights of the HF model to the appropriate type
   print('''Generating with HF model...'''						)
   _UpperCAmelCase           =   hf_model.generate(
       **SCREAMING_SNAKE_CASE_				,	do_sample=SCREAMING_SNAKE_CASE_				,	num_beams=5				,	max_length=2_56				,	min_length=1				,	top_p=0.9				,	repetition_penalty=1.5				,	length_penalty=1.0				,	temperature=1				,	)
   if "vicuna" in model_name:
      # convert output id 0 to 2 (eos_token_id)
      # TODO add this in the generate method?
      _UpperCAmelCase           =   2
   print('''Original generation:'''				,	SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   processor.batch_decode(SCREAMING_SNAKE_CASE_				,	skip_special_tokens=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   [text.strip() for text in output_text]
   print('''HF generation:'''				,	SCREAMING_SNAKE_CASE_						)

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(SCREAMING_SNAKE_CASE_						)
      hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)

   if push_to_hub:
      processor.push_to_hub(F'''Salesforce/{model_name}'''						)
      hf_model.push_to_hub(F'''Salesforce/{model_name}'''						)


if __name__ == "__main__":
   UpperCAmelCase_           =	argparse.ArgumentParser()
   UpperCAmelCase_           =	[
       "instructblip-vicuna-7b",
       "instructblip-vicuna-13b",
       "instructblip-flan-t5-xl",
       "instructblip-flan-t5-xxl",
   ]
   parser.add_argument(
       "--model_name",
       default="instructblip-flan-t5-xl",
       choices=choices,
       type=str,
       help="Path to hf config.json of model to convert",
   )
   parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   parser.add_argument(
       "--push_to_hub",
       action="store_true",
       help="Whether to push the model and processor to the hub after converting",
   )

   UpperCAmelCase_           =	parser.parse_args()

   convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   import PIL.Image

   from .features import FeatureType


lowercase__		:     Optional[List[str]]			=							None
lowercase__		:     List[Any]			=							'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowercase__		:     int			=							[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]
@dataclass
class 							__lowerCAmelCase	:






       """simple docstring"""
       _snake_case      :	bool									=       True
       _snake_case      :	Optional[str]									=       None
       # Automatically constructed
       _snake_case      :	ClassVar[str]									=       "PIL.Image.Image"
       _snake_case      :	ClassVar[Any]									=       pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
       _snake_case      :	str									=       field(default='Image'		,		init=__magic_name__		,		repr=__magic_name__					)





       def __call__(				self  :						Any	)       ->	str:

            '''simple docstring'''




            return self.pa_type





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]	)       ->	dict:

            '''simple docstring'''




            if config.PIL_AVAILABLE:
                 import PIL.Image
            else:
                 raise ImportError('''To support encoding images, please install \'Pillow\'.'''	)

            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			np.array(lowerCAmelCase__	)

            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 return {"path": value, "bytes": None}
            elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 return {"path": None, "bytes": value}
            elif isinstance(lowerCAmelCase__			,					np.ndarray	):
                 # convert the image array to PNG/TIFF bytes
                 return encode_np_array(lowerCAmelCase__	)
            elif isinstance(lowerCAmelCase__			,					PIL.Image.Image	):
                 # convert the PIL image to bytes (default format is PNG/TIFF)
                 return encode_pil_image(lowerCAmelCase__	)
            elif value.get('''path'''	) is not None and os.path.isfile(value['''path''']	):
                 # we set "bytes": None to not duplicate the data if they're already available locally
                 return {"bytes": None, "path": value.get('''path'''	)}
            elif value.get('''bytes'''	) is not None or value.get('''path'''	) is not None:
                 # store the image bytes, and path is used to infer the image format using the file extension
                 return {"bytes": value.get('''bytes'''	), "path": value.get('''path'''	)}
            else:
                 raise ValueError(
                     f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""	)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						dict			,					lowerCAmelCase__  :						Dict=None	)       ->	"PIL.Image.Image":

            '''simple docstring'''




            if not self.decode:
                 raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''	)

            if config.PIL_AVAILABLE:
                 import PIL.Image
            else:
                 raise ImportError('''To support decoding images, please install \'Pillow\'.'''	)

            if token_per_repo_id is None:
                 _UpperCamelCase							       =			{}

            _UpperCamelCase       ,							_UpperCamelCase							       =			value['''path'''], value['''bytes''']
            if bytes_ is None:
                 if path is None:
                      raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}."""	)
                 else:
                      if is_local_path(lowerCAmelCase__	):
                           _UpperCamelCase							       =			PIL.Image.open(lowerCAmelCase__	)
                      else:
                           _UpperCamelCase							       =			path.split('''::'''	)[-1]
                           try:
                                _UpperCamelCase							       =			string_to_dict(lowerCAmelCase__			,					config.HUB_DATASETS_URL	)['''repo_id''']
                                _UpperCamelCase							       =			token_per_repo_id.get(lowerCAmelCase__	)
                           except ValueError:
                                _UpperCamelCase							       =			None
                           with xopen(lowerCAmelCase__			,					'''rb'''			,					use_auth_token=lowerCAmelCase__	) as f:
                                _UpperCamelCase							       =			BytesIO(f.read()	)
                           _UpperCamelCase							       =			PIL.Image.open(bytes_	)
            else:
                 _UpperCamelCase							       =			PIL.Image.open(BytesIO(bytes_	)	)
            image.load()  # to avoid "Too many open files" errors
            return image





       def snake_case__		(				self  :						List[str]	)       ->	Union["FeatureType", Dict[str, "FeatureType"]]:

            '''simple docstring'''




            from .features import Value

            return (
                self
                if self.decode
                else {
                    "bytes": Value('''binary'''	),
                    "path": Value('''string'''	),
                }
            )





       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Union[pa.StringArray, pa.StructArray, pa.ListArray]	)       ->	pa.StructArray:

            '''simple docstring'''




            if pa.types.is_string(storage.type	):
                 _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.binary()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays([bytes_array, storage]			,					['''bytes''', '''path''']			,					mask=storage.is_null()	)
            elif pa.types.is_binary(storage.type	):
                 _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.string()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays([storage, path_array]			,					['''bytes''', '''path''']			,					mask=storage.is_null()	)
            elif pa.types.is_struct(storage.type	):
                 if storage.type.get_field_index('''bytes'''	) >= 0:
                      _UpperCamelCase							       =			storage.field('''bytes'''	)
                 else:
                      _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.binary()	)
                 if storage.type.get_field_index('''path'''	) >= 0:
                      _UpperCamelCase							       =			storage.field('''path'''	)
                 else:
                      _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.string()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays([bytes_array, path_array]			,					['''bytes''', '''path''']			,					mask=storage.is_null()	)
            elif pa.types.is_list(storage.type	):
                 _UpperCamelCase							       =			pa.array(
                     [encode_np_array(np.array(lowerCAmelCase__	)	)['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]			,					type=pa.binary()			,					)
                 _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.string()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays(
                     [bytes_array, path_array]			,					['''bytes''', '''path''']			,					mask=bytes_array.is_null()	)
            return array_cast(lowerCAmelCase__			,					self.pa_type	)







       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						pa.StructArray	)       ->	pa.StructArray:

            '''simple docstring'''




            @no_op_if_value_is_null
            def path_to_bytes(lowerCAmelCase__  :						Optional[Any]	):
                 with xopen(lowerCAmelCase__			,					'''rb'''	) as f:
                      _UpperCamelCase							       =			f.read()
                 return bytes_

            _UpperCamelCase							       =			pa.array(
                [
                    (path_to_bytes(x['''path''']	) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                    for x in storage.to_pylist()
                ]			,					type=pa.binary()			,					)
            _UpperCamelCase							       =			pa.array(
                [os.path.basename(lowerCAmelCase__	) if path is not None else None for path in storage.field('''path'''	).to_pylist()]			,					type=pa.string()			,					)
            _UpperCamelCase							       =			pa.StructArray.from_arrays([bytes_array, path_array]			,					['''bytes''', '''path''']			,					mask=bytes_array.is_null()	)
            return array_cast(lowerCAmelCase__			,					self.pa_type	)







def    a__							(							)						->						List[str]:





     """simple docstring"""

     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          _UpperCamelCase							       =			list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
     return _IMAGE_COMPRESSION_FORMATS







def    a__							(							lowercase   :	"PIL.Image.Image" )						->						bytes:





     """simple docstring"""

     _UpperCamelCase							       =			BytesIO()
     if image.format in list_image_compression_formats():
          _UpperCamelCase							       =			image.format
     else:
          _UpperCamelCase							       =			'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
     image.save(lowercase,   format=lowercase )
     return buffer.getvalue()







def    a__							(							lowercase   :	"PIL.Image.Image" )						->						dict:





     """simple docstring"""

     if hasattr(lowercase,   '''filename''' ) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(lowercase )}







def    a__							(							lowercase   :	np.ndarray )						->						dict:





     """simple docstring"""

     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     _UpperCamelCase							       =			array.dtype
     _UpperCamelCase							       =			dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
     _UpperCamelCase							       =			dtype.kind
     _UpperCamelCase							       =			dtype.itemsize

     _UpperCamelCase							       =			None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          _UpperCamelCase							       =			np.dtype('''|u1''' )
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
          if dtype is not dest_dtype:
               warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          _UpperCamelCase							       =			dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               _UpperCamelCase							       =			dtype_byteorder + dtype_kind + str(lowercase )
               _UpperCamelCase							       =			np.dtype(lowercase )
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
     _UpperCamelCase							       =			PIL.Image.fromarray(array.astype(lowercase ) )
     return {"path": None, "bytes": image_to_bytes(lowercase )}







def    a__							(							lowercase   :	Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] )						->						List[dict]:





     """simple docstring"""

     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     if objs:
          _UpperCamelCase       ,							_UpperCamelCase							       =			first_non_null_value(lowercase )
          if isinstance(lowercase,   lowercase ):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(lowercase,   np.ndarray ):
               _UpperCamelCase							       =			no_op_if_value_is_null(lowercase )
               return [obj_to_image_dict_func(lowercase ) for obj in objs]
          elif isinstance(lowercase,   PIL.Image.Image ):
               _UpperCamelCase							       =			no_op_if_value_is_null(lowercase )
               return [obj_to_image_dict_func(lowercase ) for obj in objs]
          else:
               return objs
     else:
          return objs



