
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil







class  __A				( _SCREAMING_SNAKE_CASE			):
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Dict												=		pa.array(TypedSequence([1, 2, 3]       )       )
   self.assertEqual(arr.type				,   pa.intaa()       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   with self.assertRaises(lowerCamelCase       ):
     __A		:     Optional[int]												=		pa.array(TypedSequence([1, 2, 3]       )				,   type=pa.intaa()       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   with self.assertRaises(lowerCamelCase       ):
     __A		:     List[str]												=		pa.array(TypedSequence([1, 2, 3]				,   try_type=Value("""bool"""       )				,   type=Value("""int64"""       )       )       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Any												=		pa.array(TypedSequence([1, 2, 3]				,   type=Value("""int32"""       )       )       )
   self.assertEqual(arr.type				,   pa.intaa()       )
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
     __A		:     List[Any]												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   type=Value("""int64"""       )       )       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		pa.array(TypedSequence([1, 2, 3]				,   try_type=Value("""int32"""       )       )       )
   self.assertEqual(arr.type				,   pa.intaa()       )
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Tuple												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   try_type=Value("""int64"""       )       )       )
   self.assertEqual(arr.type				,   pa.string()       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     List[Any]												=		pa.array(TypedSequence([[[1, 2, 3]]]				,   type=ArrayaD((1, 3)				,   """int64"""       )       )       )
   self.assertEqual(arr.type				,   ArrayaDExtensionType((1, 3)				,   """int64"""       )       )
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
     __A		:     List[Any]												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   type=ArrayaD((1, 3)				,   """int64"""       )       )       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     List[Any]												=		pa.array(TypedSequence([[[1, 2, 3]]]				,   try_type=ArrayaD((1, 3)				,   """int64"""       )       )       )
   self.assertEqual(arr.type				,   ArrayaDExtensionType((1, 3)				,   """int64"""       )       )
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Any												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   try_type=ArrayaD((1, 3)				,   """int64"""       )       )       )
   self.assertEqual(arr.type				,   pa.string()       )






 @require_pil
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   import PIL.Image

   __A		:     str												=		PIL.Image.fromarray(np.arange(10				,   dtype=np.uinta       ).reshape(2				,   5       )       )
   with patch(
       """datasets.arrow_writer.cast_to_python_objects"""				,   side_effect=lowerCamelCase       ) as mock_cast_to_python_objects:
     __A		:     Optional[int]												=		pa.array(TypedSequence([{"""path""": None, """bytes""": B"""image_bytes"""}, pil_image]				,   type=Image()       )       )
     __A		:     List[Any]												=		mock_cast_to_python_objects.call_args_list[-1]
     self.assertIn("""optimize_list_casting"""				,   lowerCamelCase       )
     self.assertFalse(kwargs["""optimize_list_casting"""]       )







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  int )				->       str:





  """simple docstring"""

  __A		:     int												=		pa.BufferReader(__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE			,     pa.Buffer ) else pa.memory_map(__SCREAMING_SNAKE_CASE )
  __A		:     Dict												=		pa.ipc.open_stream(__SCREAMING_SNAKE_CASE )
  __A		:     pa.Table												=		f.read_all()
  assert len(pa_table.to_batches() ) == expected_num_chunks
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
  del pa_table







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       List[str]:





  """simple docstring"""

  __A		:     Optional[int]												=		pa.BufferOutputStream()
  __A		:     List[str]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1} )
    writer.write({"""col_1""": """bar""", """col_2""": 2} )
    __A		:     str												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     List[Any]												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







def        A_     (						)				->       Optional[Any]:





  """simple docstring"""

  __A		:     List[str]												=		pa.BufferOutputStream()
  __A		:     List[Any]												=		Features({"""labels""": ClassLabel(names=["""neg""", """pos"""] )} )
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     features=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""labels""": 0} )
    writer.write({"""labels""": 1} )
    __A		:     Any												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  __A		:     Tuple												=		pa.BufferReader(output.getvalue() )
  __A		:     List[Any]												=		pa.ipc.open_stream(__SCREAMING_SNAKE_CASE )
  __A		:     pa.Table												=		f.read_all()
  __A		:     Dict												=		pa_table.schema
  assert pa_table.num_rows == 2
  assert schema == features.arrow_schema
  assert schema.metadata == features.arrow_schema.metadata
  assert features == Features.from_arrow_schema(__SCREAMING_SNAKE_CASE )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any] )				->       Dict:





  """simple docstring"""

  __A		:     List[str]												=		pa.BufferOutputStream()
  with ArrowWriter(
      stream=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE			,     hash_salt="""split_name"""			,     check_duplicates=__SCREAMING_SNAKE_CASE			,     ) as writer:
    with pytest.raises(__SCREAMING_SNAKE_CASE ):
      writer.write({"""col_1""": """foo""", """col_2""": 1}			,     key=[1, 2] )
      __A		:     int												=		writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 2, 10] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str] )				->       Optional[Any]:





  """simple docstring"""

  __A		:     int												=		pa.BufferOutputStream()
  with ArrowWriter(
      stream=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE			,     hash_salt="""split_name"""			,     check_duplicates=__SCREAMING_SNAKE_CASE			,     ) as writer:
    with pytest.raises(__SCREAMING_SNAKE_CASE ):
      writer.write({"""col_1""": """foo""", """col_2""": 1}			,     key=10 )
      writer.write({"""col_1""": """bar""", """col_2""": 2}			,     key=10 )
      __A		:     str												=		writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 2, 10] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       int:





  """simple docstring"""

  __A		:     Union[str, Any]												=		pa.BufferOutputStream()
  with ArrowWriter(
      stream=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE			,     hash_salt="""split_name"""			,     check_duplicates=__SCREAMING_SNAKE_CASE			,     ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}			,     key=1 )
    writer.write({"""col_1""": """bar""", """col_2""": 2}			,     key=2 )
    __A		:     Tuple												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  Any )				->       List[str]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		pa.BufferOutputStream()
  __A		:     List[Any]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
    writer.write_batch({"""col_1""": [], """col_2""": []} )
    __A		:     List[str]												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     Any												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Optional[Any]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		pa.BufferOutputStream()
  __A		:     Optional[int]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} ) )
    __A		:     Optional[Any]												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     Union[str, Any]												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Optional[int]:





  """simple docstring"""

  __A		:     Any												=		pa.BufferOutputStream()
  __A		:     Optional[Any]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]} ) )
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]} ) )
    __A		:     Optional[int]												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     Tuple												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







def        A_     (						)				->       str:





  """simple docstring"""

  with tempfile.TemporaryDirectory() as tmp_dir:
    __A		:     Optional[Any]												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
    __A		:     List[Any]												=		os.path.join(__SCREAMING_SNAKE_CASE			,     """test.arrow""" )
    with ArrowWriter(path=__SCREAMING_SNAKE_CASE			,     schema=pa.schema(__SCREAMING_SNAKE_CASE ) ) as writer:
      writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
      __A		:     Any												=		writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
    _check_output(__SCREAMING_SNAKE_CASE			,     1 )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int] )				->       int:





  """simple docstring"""

  if pa.types.is_list(__SCREAMING_SNAKE_CASE ):
    return get_base_dtype(arr_type.value_type )
  else:
    return arr_type







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Tuple:





  """simple docstring"""

  if isinstance(lst[0]			,     __SCREAMING_SNAKE_CASE ):
    change_first_primitive_element_in_list(lst[0]			,     __SCREAMING_SNAKE_CASE )
  else:
    __A		:     int												=		value







@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""			,     [(None, pa.intaa()), (Value("""int32""" ), pa.intaa())] )
@pytest.mark.parametrize("""sequence"""			,     [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Union[str, Any]:





  """simple docstring"""

  __A		:     List[str]												=		pa.array(TypedSequence(__SCREAMING_SNAKE_CASE			,     optimized_int_type=__SCREAMING_SNAKE_CASE ) )
  assert get_base_dtype(arr.type ) == expected_dtype







@pytest.mark.parametrize(
    """col, expected_dtype"""			,     [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]			,     )
@pytest.mark.parametrize("""sequence"""			,     [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Union[str, Any]:





  """simple docstring"""

  __A		:     int												=		pa.array(OptimizedTypedSequence(__SCREAMING_SNAKE_CASE			,     col=__SCREAMING_SNAKE_CASE ) )
  assert get_base_dtype(arr.type ) == expected_dtype

  # not in range
  if col != "other":
    # avoids errors due to in-place modifications
    __A		:     Optional[Any]												=		copy.deepcopy(__SCREAMING_SNAKE_CASE )
    __A		:     List[Any]												=		np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
    change_first_primitive_element_in_list(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    __A		:     Dict												=		pa.array(OptimizedTypedSequence(__SCREAMING_SNAKE_CASE			,     col=__SCREAMING_SNAKE_CASE ) )
    assert get_base_dtype(arr.type ) == pa.intaa()







@pytest.mark.parametrize("""raise_exception"""			,     [False, True] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Tuple:





  """simple docstring"""

  __A		:     int												=		str(tmp_path / """dataset-train.arrow""" )
  try:
    with ArrowWriter(path=__SCREAMING_SNAKE_CASE ) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Tuple												=		"""mock://dataset-train.arrow"""
  with ArrowWriter(path=__SCREAMING_SNAKE_CASE			,     storage_options=mockfs.storage_options ) as writer:
    assert isinstance(writer._fs			,     type(__SCREAMING_SNAKE_CASE ) )
    assert writer._fs.storage_options == mockfs.storage_options
    writer.write({"""col_1""": """foo""", """col_2""": 1} )
    writer.write({"""col_1""": """bar""", """col_2""": 2} )
    __A		:     Any												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert mockfs.exists(__SCREAMING_SNAKE_CASE )







def        A_     (						)				->       Dict:





  """simple docstring"""

  __A		:     List[str]												=		pa.BufferOutputStream()
  with ParquetWriter(stream=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1} )
    writer.write({"""col_1""": """bar""", """col_2""": 2} )
    __A		:     Tuple												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  __A		:     Dict												=		pa.BufferReader(output.getvalue() )
  __A		:     pa.Table												=		pq.read_table(__SCREAMING_SNAKE_CASE )
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}







@require_pil
@pytest.mark.parametrize("""embed_local_files"""			,     [False, True] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Optional[Any]:





  """simple docstring"""

  import PIL.Image

  __A		:     Optional[int]												=		str(tmp_path / """test_image_rgb.jpg""" )
  PIL.Image.fromarray(np.zeros((5, 5)			,     dtype=np.uinta ) ).save(__SCREAMING_SNAKE_CASE			,     format="""png""" )
  __A		:     Any												=		pa.BufferOutputStream()
  with ParquetWriter(
      stream=__SCREAMING_SNAKE_CASE			,     features=Features({"""image""": Image()} )			,     embed_local_files=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""image""": image_path} )
    writer.finalize()
  __A		:     str												=		pa.BufferReader(output.getvalue() )
  __A		:     pa.Table												=		pq.read_table(__SCREAMING_SNAKE_CASE )
  __A		:     Optional[Any]												=		pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["""image"""][0]["""path"""]			,     __SCREAMING_SNAKE_CASE )
    with open(__SCREAMING_SNAKE_CASE			,     """rb""" ) as f:
      assert out["image"][0]["bytes"] == f.read()
  else:
    assert out["image"][0]["path"] == image_path
    assert out["image"][0]["bytes"] is None







def        A_     (						)				->       List[Any]:





  """simple docstring"""

  __A		:     Any												=		pa.schema([pa.field("""col_1"""			,     pa.string()			,     nullable=__SCREAMING_SNAKE_CASE )] )
  __A		:     List[Any]												=		pa.BufferOutputStream()
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE ) as writer:
    writer._build_writer(inferred_schema=__SCREAMING_SNAKE_CASE )
    assert writer._schema == pa.schema([pa.field("""col_1"""			,     pa.string() )] )





'''simple docstring'''
import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       int:





  """simple docstring"""

  __A		:     Optional[int]												=		"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
  __A		:     Tuple												=		Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw ).convert("""RGB""" )

  __A		:     Optional[Any]												=		transforms.Compose(
      [
          transforms.Resize((image_size, image_size)			,     interpolation=InterpolationMode.BICUBIC ),
          transforms.ToTensor(),
          transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)			,     (0.26_862_954, 0.26_130_258, 0.27_577_711) ),
      ] )
  __A		:     Optional[int]												=		transform(__SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(__SCREAMING_SNAKE_CASE )
  return image







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       Optional[int]:





  """simple docstring"""

  if "visual_encoder" in key:
    __A		:     Dict												=		re.sub("""visual_encoder*"""			,     """vision_model.encoder"""			,     __SCREAMING_SNAKE_CASE )
  if "blocks" in key:
    __A		:     Dict												=		re.sub(R"""blocks"""			,     """layers"""			,     __SCREAMING_SNAKE_CASE )
  if "attn" in key:
    __A		:     Union[str, Any]												=		re.sub(R"""attn"""			,     """self_attn"""			,     __SCREAMING_SNAKE_CASE )
  if "norm1" in key:
    __A		:     str												=		re.sub(R"""norm1"""			,     """layer_norm1"""			,     __SCREAMING_SNAKE_CASE )
  if "norm2" in key:
    __A		:     List[Any]												=		re.sub(R"""norm2"""			,     """layer_norm2"""			,     __SCREAMING_SNAKE_CASE )
  if "encoder.norm" in key:
    __A		:     Optional[Any]												=		re.sub(R"""encoder.norm"""			,     """post_layernorm"""			,     __SCREAMING_SNAKE_CASE )
  if "encoder.patch_embed.proj" in key:
    __A		:     Optional[int]												=		re.sub(R"""encoder.patch_embed.proj"""			,     """embeddings.patch_embedding"""			,     __SCREAMING_SNAKE_CASE )

  if "encoder.pos_embed" in key:
    __A		:     Union[str, Any]												=		re.sub(R"""encoder.pos_embed"""			,     """embeddings.position_embedding"""			,     __SCREAMING_SNAKE_CASE )
  if "encoder.cls_token" in key:
    __A		:     Tuple												=		re.sub(R"""encoder.cls_token"""			,     """embeddings.class_embedding"""			,     __SCREAMING_SNAKE_CASE )

  if "self_attn" in key:
    __A		:     Tuple												=		re.sub(R"""self_attn.proj"""			,     """self_attn.projection"""			,     __SCREAMING_SNAKE_CASE )

  return key







@torch.no_grad()
def        A_     (						__SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  List[str]=None )				->       int:





  """simple docstring"""

  if config_path is not None:
    __A		:     Any												=		BlipConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
  else:
    __A		:     List[Any]												=		BlipConfig(projection_dim=512			,     text_config={}			,     vision_config={} )

  __A		:     List[Any]												=		BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE ).eval()

  __A		:     List[str]												=		"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

  __A		:     List[str]												=		blip_decoder(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=384			,     vit="""base""" )
  __A		:     List[str]												=		pt_model.eval()

  __A		:     int												=		pt_model.state_dict()
  for key in modified_state_dict.copy():
    __A		:     Tuple												=		modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
    __A		:     Dict												=		rename_key(__SCREAMING_SNAKE_CASE )
    __A		:     Tuple												=		value

  hf_model.load_state_dict(__SCREAMING_SNAKE_CASE )

  __A		:     List[Any]												=		384
  __A		:     Dict												=		load_demo_image(image_size=__SCREAMING_SNAKE_CASE			,     device="""cpu""" )
  __A		:     Dict												=		BertTokenizer.from_pretrained("""bert-base-uncased""" )
  __A		:     Optional[Any]												=		tokenizer(["""a picture of"""] ).input_ids

  __A		:     int												=		hf_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

  __A		:     str												=		hf_model.generate(__SCREAMING_SNAKE_CASE )

  assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

  if pytorch_dump_folder_path is not None:
    hf_model.save_pretrained(__SCREAMING_SNAKE_CASE )

  # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
  __A		:     List[Any]												=		(
      """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
  )

  __A		:     List[Any]												=		blip_vqa(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="""base""" )
  vqa_model.eval()

  __A		:     List[Any]												=		vqa_model.state_dict()
  for key in modified_state_dict.copy():
    __A		:     List[Any]												=		modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
    __A		:     int												=		rename_key(__SCREAMING_SNAKE_CASE )
    __A		:     Union[str, Any]												=		value

  __A		:     Any												=		BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE )

  hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE )

  __A		:     Tuple												=		["""How many dogs are in this image?"""]
  __A		:     Union[str, Any]												=		tokenizer(__SCREAMING_SNAKE_CASE			,     return_tensors="""pt""" ).input_ids

  __A		:     List[str]												=		hf_vqa_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  print(tokenizer.decode(answer[0] ) )

  assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
  if pytorch_dump_folder_path is not None:
    hf_vqa_model.save_pretrained(pytorch_dump_folder_path + """_vqa""" )

  __A		:     str												=		"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

  __A		:     List[str]												=		blip_itm(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="""base""" )
  itm_model.eval()

  __A		:     List[str]												=		itm_model.state_dict()
  for key in modified_state_dict.copy():
    __A		:     Optional[Any]												=		modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
    __A		:     str												=		rename_key(__SCREAMING_SNAKE_CASE )
    __A		:     Any												=		value

  __A		:     List[Any]												=		BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE )

  __A		:     Tuple												=		["""A picture of a woman with a dog sitting in a beach"""]
  __A		:     List[str]												=		tokenizer(
      __SCREAMING_SNAKE_CASE			,     return_tensors="""pt"""			,     padding="""max_length"""			,     truncation=__SCREAMING_SNAKE_CASE			,     max_length=35			,     ).input_ids

  hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE )
  hf_itm_model.eval()

  __A		:     Any												=		hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[Any]												=		hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )

  assert out[0].item() == 0.2_110_687_494_277_954
  assert torch.nn.functional.softmax(out_itm[0]			,     dim=1 )[:, 1].item() == 0.45_698_845_386_505_127

  if pytorch_dump_folder_path is not None:
    hf_itm_model.save_pretrained(pytorch_dump_folder_path + """_itm""" )


if __name__ == "__main__":
     A__   :		Tuple 		=argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     A__   :		Any 		=parser.parse_args()

     convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



