


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__snake_case   							=						'''scheduler_config.json'''



class 	__lowerCamelCase			(_a						):
       _lowercase											=     1
       _lowercase											=     2
       _lowercase											=     3
       _lowercase											=     4
       _lowercase											=     5



@dataclass
class 	__lowerCamelCase			(_a						):
       _lowercase											=     42



class 	__lowerCamelCase			:
       _lowercase											=     SCHEDULER_CONFIG_NAME
       _lowercase											=     ["""dtype"""]
       _lowercase											=     []
       _lowercase											=     True







       @classmethod
       def 		snake_case_	(			cls: Any,A_: Dict[str, Any] = None,A_: Optional[str] = None,A_: Union[str, Any]=False,**A_: List[str],):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			cls.load_config(
                pretrained_model_name_or_path=A_,subfolder=A_,return_unused_kwargs=A_,**A_,)
            __UpperCamelCase,    __UpperCamelCase =			cls.from_config(A_,return_unused_kwargs=A_,**A_					)

            if hasattr(A_,'create_state'					) and getattr(A_,'has_state',A_					):
                 __UpperCamelCase =			scheduler.create_state()

            if return_unused_kwargs:
                 return scheduler, state, unused_kwargs

            return scheduler, state







       def 		snake_case_	(			self: str,A_: Union[str, os.PathLike],A_: bool = False,**A_: Any					):






            '''simple docstring'''




            self.save_config(save_directory=A_,push_to_hub=A_,**A_					)







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self._get_compatibles()







       @classmethod
       def 		snake_case_	(			cls: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			list(set([cls.__name__] + cls._compatibles					)					)
            __UpperCamelCase =			importlib.import_module(__name__.split('.'					)[0]					)
            __UpperCamelCase =			[
                getattr(A_,A_					) for c in compatible_classes_str if hasattr(A_,A_					)
            ]
            return compatible_classes






def 		_A		(		_lowercase					,  _lowercase )					-> jnp.ndarray:


     """simple docstring"""


     assert len(_lowercase ) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowercase ) - x.ndim) )					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase=0.9_99					,  _lowercase=jnp.floataa )					-> jnp.ndarray:


     """simple docstring"""


     def alpha_bar(_lowercase ):
          return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

     __UpperCamelCase =			[]
     for i in range(_lowercase ):
          __UpperCamelCase =			i / num_diffusion_timesteps
          __UpperCamelCase =			(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(_lowercase ) / alpha_bar(_lowercase )					,  _lowercase ) )
     return jnp.array(_lowercase					,  dtype=_lowercase )



@flax.struct.dataclass
class 	__lowerCamelCase			:
       _lowercase											=     42
       _lowercase											=     42
       _lowercase											=     42







       @classmethod
       def 		snake_case_	(			cls: Any,A_: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			scheduler.config

            if config.trained_betas is not None:
                 __UpperCamelCase =			jnp.asarray(config.trained_betas,dtype=scheduler.dtype					)
            elif config.beta_schedule == "linear":
                 __UpperCamelCase =			jnp.linspace(config.beta_start,config.beta_end,config.num_train_timesteps,dtype=scheduler.dtype					)
            elif config.beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 __UpperCamelCase =			(
                     jnp.linspace(
                         config.beta_start**0.5,config.beta_end**0.5,config.num_train_timesteps,dtype=scheduler.dtype					)
                     ** 2
                 )
            elif config.beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 __UpperCamelCase =			betas_for_alpha_bar(config.num_train_timesteps,dtype=scheduler.dtype					)
            else:
                 raise NotImplementedError(
                     F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''					)

            __UpperCamelCase =			1.0 - betas

            __UpperCamelCase =			jnp.cumprod(A_,axis=0					)

            return cls(
                alphas=A_,betas=A_,alphas_cumprod=A_,)






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			state.alphas_cumprod

     __UpperCamelCase =			alphas_cumprod[timesteps] ** 0.5
     __UpperCamelCase =			sqrt_alpha_prod.flatten()
     __UpperCamelCase =			broadcast_to_shape_from_left(_lowercase					,  original_samples.shape )

     __UpperCamelCase =			(1 - alphas_cumprod[timesteps]) ** 0.5
     __UpperCamelCase =			sqrt_one_minus_alpha_prod.flatten()
     __UpperCamelCase =			broadcast_to_shape_from_left(_lowercase					,  original_samples.shape )

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase,    __UpperCamelCase =			get_sqrt_alpha_prod(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
     __UpperCamelCase =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase,    __UpperCamelCase =			get_sqrt_alpha_prod(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
     __UpperCamelCase =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity






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)


