


import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__a 					=   datasets.utils.logging.get_logger(__name__)

__a 					=   ['''names''', '''prefix''']
__a 					=   ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__a 					=   ['''encoding_errors''', '''on_bad_lines''']
__a 					=   ['''date_format''']







@dataclass
class 					__SCREAMING_SNAKE_CASE (      datasets.BuilderConfig ):
      A					:				int     				=  ','
      A					:				Dict     				=  None
      A					:				List[Any]     				=  'infer'
      A					:				Tuple     				=  None
      A					:				str     				=  None
      A					:				List[str]     				=  None
      A					:				List[Any]     				=  None
      A					:				Tuple     				=  None
      A					:				str     				=  True
      A					:				str     				=  None
      A					:				Tuple     				=  None
      A					:				Optional[int]     				=  None
      A					:				Optional[Any]     				=  None
      A					:				List[Any]     				=  False
      A					:				List[str]     				=  None
      A					:				List[Any]     				=  None
      A					:				List[str]     				=  None
      A					:				Optional[int]     				=  True
      A					:				List[str]     				=  True
      A					:				Dict     				=  False
      A					:				List[str]     				=  True
      A					:				Union[str, Any]     				=  None
      A					:				Optional[Any]     				=  '.'
      A					:				List[str]     				=  None
      A					:				Any     				=  '"'
      A					:				Any     				=  0
      A					:				Optional[int]     				=  None
      A					:				Optional[int]     				=  None
      A					:				Any     				=  None
      A					:				int     				=  None
      A					:				List[str]     				=  True
      A					:				List[Any]     				=  True
      A					:				int     				=  0
      A					:				Optional[Any]     				=  True
      A					:				str     				=  False
      A					:				Any     				=  None
      A					:				Optional[Any]     				=  1_0000
      A					:				str     				=  None
      A					:				int     				=  'strict'
      A					:				List[str]     				=  'error'
      A					:				Dict     				=  None


      def 							__lowerCamelCase      (				self		):




            if self.delimiter is not None:
                  lowercase      : Any        =		self.delimiter
            if self.column_names is not None:
                  lowercase      : Dict        =		self.column_names






      @property
      def 							__lowerCamelCase      (				self		):
            lowercase      : str        =		{
                '''sep''': self.sep,
                '''header''': self.header,
                '''names''': self.names,
                '''index_col''': self.index_col,
                '''usecols''': self.usecols,
                '''prefix''': self.prefix,
                '''mangle_dupe_cols''': self.mangle_dupe_cols,
                '''engine''': self.engine,
                '''converters''': self.converters,
                '''true_values''': self.true_values,
                '''false_values''': self.false_values,
                '''skipinitialspace''': self.skipinitialspace,
                '''skiprows''': self.skiprows,
                '''nrows''': self.nrows,
                '''na_values''': self.na_values,
                '''keep_default_na''': self.keep_default_na,
                '''na_filter''': self.na_filter,
                '''verbose''': self.verbose,
                '''skip_blank_lines''': self.skip_blank_lines,
                '''thousands''': self.thousands,
                '''decimal''': self.decimal,
                '''lineterminator''': self.lineterminator,
                '''quotechar''': self.quotechar,
                '''quoting''': self.quoting,
                '''escapechar''': self.escapechar,
                '''comment''': self.comment,
                '''encoding''': self.encoding,
                '''dialect''': self.dialect,
                '''error_bad_lines''': self.error_bad_lines,
                '''warn_bad_lines''': self.warn_bad_lines,
                '''skipfooter''': self.skipfooter,
                '''doublequote''': self.doublequote,
                '''memory_map''': self.memory_map,
                '''float_precision''': self.float_precision,
                '''chunksize''': self.chunksize,
                '''encoding_errors''': self.encoding_errors,
                '''on_bad_lines''': self.on_bad_lines,
                '''date_format''': self.date_format,
            }

            # some kwargs must not be passed if they don't have a default value
            # some others are deprecated and we can also not pass them if they are the default value
            for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
                  if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()				,    A_		):
                        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
            if not (datasets.config.PANDAS_VERSION.major >= 2):
                  for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
            if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
                  for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                        del pd_read_csv_kwargs[pd_read_csv_parameter]

            return pd_read_csv_kwargs







class 					__SCREAMING_SNAKE_CASE (      datasets.ArrowBasedBuilder ):
      A					:				List[str]     				=  CsvConfig


      def 							__lowerCamelCase      (				self		):
            return datasets.DatasetInfo(features=self.config.features		)


      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__		):




            if not self.config.data_files:
                  raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""		)
            lowercase      : int        =		dl_manager.download_and_extract(self.config.data_files		)
            if isinstance(A_				,    (str, list, tuple)		):
                  lowercase      : Tuple        =		data_files
                  if isinstance(A_				,    A_		):
                        lowercase      : Dict        =		[files]
                  lowercase      : int        =		[dl_manager.iter_files(A_		) for file in files]
                  return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={'''files''': files}		)]
            lowercase      : Union[str, Any]        =		[]
            for split_name, files in data_files.items():
                  if isinstance(A_				,    A_		):
                        lowercase      : Optional[int]        =		[files]
                  lowercase      : Optional[Any]        =		[dl_manager.iter_files(A_		) for file in files]
                  splits.append(datasets.SplitGenerator(name=A_				,    gen_kwargs={'''files''': files}		)		)
            return splits


      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__		):




            if self.config.features is not None:
                  lowercase      : Optional[Any]        =		self.config.features.arrow_schema
                  if all(not require_storage_cast(A_		) for feature in self.config.features.values()		):
                        # cheaper cast
                        lowercase      : Optional[Any]        =		pa.Table.from_arrays([pa_table[field.name] for field in schema]				,    schema=A_		)
                  else:
                        # more expensive cast; allows str <-> int/float or str to Audio for example
                        lowercase      : str        =		table_cast(A_				,    A_		)
            return pa_table






      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__		):
            lowercase      : Any        =		self.config.features.arrow_schema if self.config.features else None
            # dtype allows reading an int column as str
            lowercase      : Optional[int]        =		(
                {
                    name: dtype.to_pandas_dtype() if not require_storage_cast(A_		) else object
                    for name, dtype, feature in zip(schema.names				,    schema.types				,    self.config.features.values()		)
                }
                if schema is not None
                else None
            )
            for file_idx, file in enumerate(itertools.chain.from_iterable(A_		)		):
                  lowercase      : List[Any]        =		pd.read_csv(A_				,    iterator=A_				,    dtype=A_				,    **self.config.pd_read_csv_kwargs		)
                  try:
                        for batch_idx, df in enumerate(A_		):
                              lowercase      : List[Any]        =		pa.Table.from_pandas(A_		)
                              # Uncomment for debugging (will print the Arrow table size and elements)
                              # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                              # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                              yield (file_idx, batch_idx), self._cast_table(A_		)
                  except ValueError as e:
                        logger.error(f"""Failed to read file \'{file}\' with error {type(A_		)}: {e}"""		)
                        raise





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
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def 		_A		(		)					-> int:


     """simple docstring"""


     __UpperCamelCase =			'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
     __UpperCamelCase =			Image.open(requests.get(_lowercase					,  stream=_lowercase ).raw ).convert('RGB' )

     return image






def 		_A		(		_lowercase )					-> int:


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

     # fmt: on
     return rename_keys






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			dct.pop(_lowercase )
     __UpperCamelCase =			val






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """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(_lowercase					,  requires_grad=_lowercase ), v_bias) )
          __UpperCamelCase =			qkv_bias






def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			3_64 if 'coco' in model_name else 2_24
     __UpperCamelCase =			BlipaVisionConfig(image_size=_lowercase ).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 "opt-2.7b" in model_name:
          __UpperCamelCase =			OPTConfig.from_pretrained('facebook/opt-2.7b'					,  eos_token_id=_lowercase ).to_dict()
     elif "opt-6.7b" in model_name:
          __UpperCamelCase =			OPTConfig.from_pretrained('facebook/opt-6.7b'					,  eos_token_id=_lowercase ).to_dict()
     elif "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()

     __UpperCamelCase =			BlipaConfig(vision_config=_lowercase					,  text_config=_lowercase )

     return config, image_size






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase=None					,  _lowercase=False )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			(
         AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
         if 'opt' in model_name
         else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
     )
     __UpperCamelCase =			tokenizer('\n'					,  add_special_tokens=_lowercase ).input_ids[0]
     __UpperCamelCase,    __UpperCamelCase =			get_blipa_config(_lowercase					,  eos_token_id=_lowercase )

     __UpperCamelCase =			BlipaForConditionalGeneration(_lowercase ).eval()

     __UpperCamelCase =			{
         'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
         'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
         'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
         'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
         'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
         'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
         'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
     }

     __UpperCamelCase,    __UpperCamelCase =			model_name_to_original[model_name]

     # load original model
     print('Loading original model...' )
     __UpperCamelCase =			'cuda' if torch.cuda.is_available() else 'cpu'
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			load_model_and_preprocess(
         name=_lowercase					,  model_type=_lowercase					,  is_eval=_lowercase					,  device=_lowercase )
     original_model.eval()
     print('Done!' )

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

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          __UpperCamelCase =			state_dict.pop(_lowercase )
          if key.startswith('Qformer.bert' ):
               __UpperCamelCase =			key.replace('Qformer.bert'					,  'qformer' )
          if "attention.self" in key:
               __UpperCamelCase =			key.replace('self'					,  'attention' )
          if "opt_proj" in key:
               __UpperCamelCase =			key.replace('opt_proj'					,  'language_projection' )
          if "t5_proj" in key:
               __UpperCamelCase =			key.replace('t5_proj'					,  'language_projection' )
          if key.startswith('opt' ):
               __UpperCamelCase =			key.replace('opt'					,  'language' )
          if key.startswith('t5' ):
               __UpperCamelCase =			key.replace('t5'					,  'language' )
          __UpperCamelCase =			val

     # read in qv biases
     read_in_q_v_bias(_lowercase					,  _lowercase )

     __UpperCamelCase,    __UpperCamelCase =			hf_model.load_state_dict(_lowercase					,  strict=_lowercase )
     assert len(_lowercase ) == 0
     assert unexpected_keys == ["qformer.embeddings.position_ids"]

     __UpperCamelCase =			load_demo_image()
     __UpperCamelCase =			vis_processors['eval'](_lowercase ).unsqueeze(0 ).to(_lowercase )
     __UpperCamelCase =			tokenizer(['\n']					,  return_tensors='pt' ).input_ids.to(_lowercase )

     # create processor
     __UpperCamelCase =			BlipImageProcessor(
         size={'height': image_size, 'width': image_size}					,  image_mean=_lowercase					,  image_std=_lowercase )
     __UpperCamelCase =			BlipaProcessor(image_processor=_lowercase					,  tokenizer=_lowercase )
     __UpperCamelCase =			processor(images=_lowercase					,  return_tensors='pt' ).pixel_values.to(_lowercase )

     # make sure processor creates exact same pixel values
     assert torch.allclose(_lowercase					,  _lowercase )

     original_model.to(_lowercase )
     hf_model.to(_lowercase )
     with torch.no_grad():
          if "opt" in model_name:
               __UpperCamelCase =			original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
               __UpperCamelCase =			hf_model(_lowercase					,  _lowercase ).logits
          else:
               __UpperCamelCase =			original_model(
                   {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
               __UpperCamelCase =			input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,  -1_00 )
               __UpperCamelCase =			hf_model(_lowercase					,  _lowercase					,  labels=_lowercase ).logits

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

     # assert values
     if model_name == "blip2-flan-t5-xl":
          __UpperCamelCase =			torch.tensor(
              [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]					,  device=_lowercase )
          assert torch.allclose(logits[0, :3, :3]					,  _lowercase					,  atol=1e-4 )
     elif model_name == "blip2-flan-t5-xl-coco":
          __UpperCamelCase =			torch.tensor(
              [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]					,  device=_lowercase )
     else:
          # cast to same type
          __UpperCamelCase =			logits.dtype
          assert torch.allclose(original_logits.to(_lowercase )					,  _lowercase					,  atol=1e-2 )
     print('Looks ok!' )

     print('Generating a caption...' )
     __UpperCamelCase =			''
     __UpperCamelCase =			tokenizer(_lowercase					,  return_tensors='pt' ).input_ids.to(_lowercase )

     __UpperCamelCase =			original_model.generate({'image': original_pixel_values} )
     __UpperCamelCase =			hf_model.generate(
         _lowercase					,  _lowercase					,  do_sample=_lowercase					,  num_beams=5					,  max_length=30					,  min_length=1					,  top_p=0.9					,  repetition_penalty=1.0					,  length_penalty=1.0					,  temperature=1					,  )
     print('Original generation:'					,  _lowercase )
     __UpperCamelCase =			input_ids.shape[1]
     __UpperCamelCase =			processor.batch_decode(outputs[:, prompt_length:]					,  skip_special_tokens=_lowercase )
     __UpperCamelCase =			[text.strip() for text in output_text]
     print('HF generation:'					,  _lowercase )

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

     if push_to_hub:
          processor.push_to_hub(f'''nielsr/{model_name}''' )
          hf_model.push_to_hub(f'''nielsr/{model_name}''' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    __snake_case   							=						[
        '''blip2-opt-2.7b''',
        '''blip2-opt-6.7b''',
        '''blip2-opt-2.7b-coco''',
        '''blip2-opt-6.7b-coco''',
        '''blip2-flan-t5-xl''',
        '''blip2-flan-t5-xl-coco''',
        '''blip2-flan-t5-xxl''',
    ]
    parser.add_argument(
        '''--model_name''',
        default='''blip2-opt-2.7b''',
        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''',
    )

    __snake_case   							=						parser.parse_args()

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


