



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase	       =	[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def 		UpperCamelCase     (       snake_case__	:   int	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Dict	,					snake_case__	:   Optional[int]     )     ->					Optional[Any]:
   for attribute in key.split('.'     ):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            UpperCamelCase   :  int						     =     'lm_head'

      UpperCamelCase   :  Dict						     =     getattr(lowercase_	,					lowercase_     )

   if weight_type is not None:
      UpperCamelCase   :  Optional[Any]						     =     getattr(lowercase_	,					lowercase_     ).shape
   else:
      UpperCamelCase   :  Optional[Any]						     =     hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      UpperCamelCase   :  Optional[int]						     =     value
   elif weight_type == "weight_g":
      UpperCamelCase   :  List[str]						     =     value
   elif weight_type == "weight_v":
      UpperCamelCase   :  str						     =     value
   elif weight_type == "bias":
      UpperCamelCase   :  Dict						     =     value
   else:
      UpperCamelCase   :  List[Any]						     =     value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""     )
def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[Any]	,					snake_case__	:   Optional[int]     )     ->					List[Any]:
   UpperCamelCase   :  Union[str, Any]						     =     []
   UpperCamelCase   :  Tuple						     =     fairseq_model.state_dict()

   UpperCamelCase   :  List[str]						     =     hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCamelCase   :  List[str]						     =     False
      if "conv_layers" in name:
         load_conv_layer(
             lowercase_	,					lowercase_	,					lowercase_	,					lowercase_	,					hf_model.config.feat_extract_norm == 'group'	,					)
         UpperCamelCase   :  Any						     =     True
      else:
         for key, mapped_key in MAPPING.items():
            UpperCamelCase   :  Union[str, Any]						     =     'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
               UpperCamelCase   :  Dict						     =     True
               if "*" in mapped_key:
                  UpperCamelCase   :  Union[str, Any]						     =     name.split(lowercase_     )[0].split('.'     )[-2]
                  UpperCamelCase   :  List[str]						     =     mapped_key.replace('*'	,					lowercase_     )
               if "weight_g" in name:
                  UpperCamelCase   :  List[Any]						     =     'weight_g'
               elif "weight_v" in name:
                  UpperCamelCase   :  str						     =     'weight_v'
               elif "bias" in name:
                  UpperCamelCase   :  Optional[Any]						     =     'bias'
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  UpperCamelCase   :  Optional[int]						     =     'weight'
               else:
                  UpperCamelCase   :  Union[str, Any]						     =     None
               set_recursively(lowercase_	,					lowercase_	,					lowercase_	,					lowercase_	,					lowercase_	,					lowercase_     )
            continue
      if not is_used:
         unused_weights.append(lowercase_     )

   logger.warning(F"""Unused weights: {unused_weights}"""     )
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   str	,					snake_case__	:   List[Any]	,					snake_case__	:   Dict     )     ->					List[str]:
   UpperCamelCase   :  List[Any]						     =     full_name.split('conv_layers.'     )[-1]
   UpperCamelCase   :  Any						     =     name.split('.'     )
   UpperCamelCase   :  Union[str, Any]						     =     int(items[0]     )
   UpperCamelCase   :  List[str]						     =     int(items[1]     )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         UpperCamelCase   :  List[Any]						     =     value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         UpperCamelCase   :  List[Any]						     =     value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         UpperCamelCase   :  int						     =     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         UpperCamelCase   :  Optional[Any]						     =     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
   else:
      unused_weights.append(lowercase_     )
@torch.no_grad()
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   Any	,					snake_case__	:   int=None	,					snake_case__	:   Optional[int]=None	,					snake_case__	:   str=True     )     ->					List[Any]:
   if config_path is not None:
      UpperCamelCase   :  Union[str, Any]						     =     UniSpeechConfig.from_pretrained(lowercase_     )
   else:
      UpperCamelCase   :  Dict						     =     UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         UpperCamelCase   :  str						     =     Dictionary.load_from_json(lowercase_     )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCamelCase   :  Optional[Any]						     =     target_dict.pad_index
         UpperCamelCase   :  Optional[Any]						     =     target_dict.bos_index
         UpperCamelCase   :  List[str]						     =     target_dict.eos_index
         UpperCamelCase   :  Tuple						     =     len(target_dict.symbols     )
         UpperCamelCase   :  str						     =     os.path.join(lowercase_	,					'vocab.json'     )
         if not os.path.isdir(lowercase_     ):
            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase_     )     )
            return
         os.makedirs(lowercase_	,					exist_ok=lowercase_     )
         UpperCamelCase   :  int						     =     target_dict.indices

         # fairseq has the <pad> and <s> switched
         UpperCamelCase   :  Optional[int]						     =     42
         UpperCamelCase   :  Optional[Any]						     =     43
         with open(lowercase_	,					'w'	,					encoding='utf-8'     ) as vocab_handle:
            json.dump(lowercase_	,					lowercase_     )
         UpperCamelCase   :  List[str]						     =     WavaVecaPhonemeCTCTokenizer(
             lowercase_	,					unk_token=target_dict.unk_word	,					pad_token=target_dict.pad_word	,					bos_token=target_dict.bos_word	,					eos_token=target_dict.eos_word	,					word_delimiter_token='|'	,					do_lower_case=lowercase_	,					)
         UpperCamelCase   :  Union[str, Any]						     =     True if config.feat_extract_norm == 'layer' else False
         UpperCamelCase   :  Optional[Any]						     =     WavaVecaFeatureExtractor(
             feature_size=1	,					sampling_rate=16000	,					padding_value=0	,					do_normalize=lowercase_	,					return_attention_mask=lowercase_	,					)
         UpperCamelCase   :  Dict						     =     WavaVecaProcessor(feature_extractor=lowercase_	,					tokenizer=lowercase_     )
         processor.save_pretrained(lowercase_     )

      UpperCamelCase   :  Any						     =     UniSpeechForCTC(lowercase_     )
   else:
      UpperCamelCase   :  Optional[int]						     =     UniSpeechForPreTraining(lowercase_     )

   if is_finetuned:
      UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  List[Any]						     =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,					arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     ), 'w2v_path': checkpoint_path}     )
   else:
      UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  str						     =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

   UpperCamelCase   :  Optional[int]						     =     model[0].eval()

   recursively_load_weights(lowercase_	,					lowercase_	,					lowercase_     )

   hf_unispeech.save_pretrained(lowercase_     )


if __name__ == "__main__":
    __UpperCAmelCase	       =	argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    __UpperCAmelCase	       =	parser.parse_args()
    convert_unispeech_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )







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 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  Any						     =     'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
   UpperCamelCase   :  List[str]						     =     Image.open(requests.get(snake_case__	,					stream=snake_case__     ).raw     ).convert('RGB'     )

   return image
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					List[Any]:
   UpperCamelCase   :  Optional[int]						     =     []
   # 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 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   Dict	,					snake_case__	:   Optional[int]     )     ->					Optional[int]:
   UpperCamelCase   :  Dict						     =     dct.pop(snake_case__     )
   UpperCamelCase   :  str						     =     val
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   Union[str, Any]     )     ->					Optional[int]:
   for i in range(config.vision_config.num_hidden_layers     ):
      # read in original q and v biases
      UpperCamelCase   :  Optional[Any]						     =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""     )
      UpperCamelCase   :  int						     =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""     )

      # next, set bias in the state dict
      UpperCamelCase   :  int						     =     torch.cat((q_bias, torch.zeros_like(snake_case__	,					requires_grad=snake_case__     ), v_bias)     )
      UpperCamelCase   :  Tuple						     =     qkv_bias
def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[Any]     )     ->					Dict:
   UpperCamelCase   :  str						     =     364 if 'coco' in model_name else 224
   UpperCamelCase   :  Union[str, Any]						     =     BlipaVisionConfig(image_size=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 "opt-2.7b" in model_name:
      UpperCamelCase   :  List[Any]						     =     OPTConfig.from_pretrained('facebook/opt-2.7b'	,					eos_token_id=snake_case__     ).to_dict()
   elif "opt-6.7b" in model_name:
      UpperCamelCase   :  int						     =     OPTConfig.from_pretrained('facebook/opt-6.7b'	,					eos_token_id=snake_case__     ).to_dict()
   elif "t5-xl" in model_name:
      UpperCamelCase   :  List[str]						     =     TaConfig.from_pretrained('google/flan-t5-xl'	,					dense_act_fn='gelu'	,					bos_token_id=1     ).to_dict()
   elif "t5-xxl" in model_name:
      UpperCamelCase   :  int						     =     TaConfig.from_pretrained('google/flan-t5-xxl'	,					dense_act_fn='gelu'	,					bos_token_id=1     ).to_dict()

   UpperCamelCase   :  Any						     =     BlipaConfig(vision_config=snake_case__	,					text_config=snake_case__     )

   return config, image_size
@torch.no_grad()
def 		UpperCamelCase     (       snake_case__	:   int	,					snake_case__	:   Dict=None	,					snake_case__	:   int=False     )     ->					List[Any]:
   UpperCamelCase   :  str						     =     (
       AutoTokenizer.from_pretrained('facebook/opt-2.7b'     )
       if 'opt' in model_name
       else AutoTokenizer.from_pretrained('google/flan-t5-xl'     )
   )
   UpperCamelCase   :  int						     =     tokenizer('\n'	,					add_special_tokens=snake_case__     ).input_ids[0]
   UpperCamelCase     ,					UpperCamelCase   :  Union[str, Any]						     =     get_blipa_config(snake_case__	,					eos_token_id=snake_case__     )

   UpperCamelCase   :  Dict						     =     BlipaForConditionalGeneration(snake_case__     ).eval()

   UpperCamelCase   :  Optional[Any]						     =     {
       '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   :  Optional[Any]						     =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'     )
   UpperCamelCase   :  List[str]						     =     'cuda' if torch.cuda.is_available() else 'cpu'
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     load_model_and_preprocess(
       name=snake_case__	,					model_type=snake_case__	,					is_eval=snake_case__	,					device=snake_case__     )
   original_model.eval()
   print('Done!'     )

   # update state dict keys
   UpperCamelCase   :  List[Any]						     =     original_model.state_dict()
   UpperCamelCase   :  Tuple						     =     create_rename_keys(snake_case__     )
   for src, dest in rename_keys:
      rename_key(snake_case__	,					snake_case__	,					snake_case__     )

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      UpperCamelCase   :  Optional[Any]						     =     state_dict.pop(snake_case__     )
      if key.startswith('Qformer.bert'     ):
         UpperCamelCase   :  List[str]						     =     key.replace('Qformer.bert'	,					'qformer'     )
      if "attention.self" in key:
         UpperCamelCase   :  Tuple						     =     key.replace('self'	,					'attention'     )
      if "opt_proj" in key:
         UpperCamelCase   :  Union[str, Any]						     =     key.replace('opt_proj'	,					'language_projection'     )
      if "t5_proj" in key:
         UpperCamelCase   :  Optional[Any]						     =     key.replace('t5_proj'	,					'language_projection'     )
      if key.startswith('opt'     ):
         UpperCamelCase   :  Dict						     =     key.replace('opt'	,					'language'     )
      if key.startswith('t5'     ):
         UpperCamelCase   :  Dict						     =     key.replace('t5'	,					'language'     )
      UpperCamelCase   :  Optional[int]						     =     val

   # read in qv biases
   read_in_q_v_bias(snake_case__	,					snake_case__     )

   UpperCamelCase     ,					UpperCamelCase   :  Any						     =     hf_model.load_state_dict(snake_case__	,					strict=snake_case__     )
   assert len(snake_case__     ) == 0
   assert unexpected_keys == ["qformer.embeddings.position_ids"]

   UpperCamelCase   :  List[str]						     =     load_demo_image()
   UpperCamelCase   :  str						     =     vis_processors['eval'](snake_case__     ).unsqueeze(0     ).to(snake_case__     )
   UpperCamelCase   :  Any						     =     tokenizer(['\n']	,					return_tensors='pt'     ).input_ids.to(snake_case__     )

   # create processor
   UpperCamelCase   :  Optional[Any]						     =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}	,					image_mean=snake_case__	,					image_std=snake_case__     )
   UpperCamelCase   :  Any						     =     BlipaProcessor(image_processor=snake_case__	,					tokenizer=snake_case__     )
   UpperCamelCase   :  Optional[int]						     =     processor(images=snake_case__	,					return_tensors='pt'     ).pixel_values.to(snake_case__     )

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

   original_model.to(snake_case__     )
   hf_model.to(snake_case__     )
   with torch.no_grad():
      if "opt" in model_name:
         UpperCamelCase   :  Tuple						     =     original_model({'image': original_pixel_values, 'text_input': ['']}     ).logits
         UpperCamelCase   :  str						     =     hf_model(snake_case__	,					snake_case__     ).logits
      else:
         UpperCamelCase   :  Tuple						     =     original_model(
             {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}     ).logits
         UpperCamelCase   :  List[Any]						     =     input_ids.masked_fill(input_ids == tokenizer.pad_token_id	,					-100     )
         UpperCamelCase   :  Optional[int]						     =     hf_model(snake_case__	,					snake_case__	,					labels=snake_case__     ).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   :  List[str]						     =     torch.tensor(
          [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]	,					device=snake_case__     )
      assert torch.allclose(logits[0, :3, :3]	,					snake_case__	,					atol=1E-4     )
   elif model_name == "blip2-flan-t5-xl-coco":
      UpperCamelCase   :  Union[str, Any]						     =     torch.tensor(
          [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]	,					device=snake_case__     )
   else:
      # cast to same type
      UpperCamelCase   :  Optional[int]						     =     logits.dtype
      assert torch.allclose(original_logits.to(snake_case__     )	,					snake_case__	,					atol=1E-2     )
   print('Looks ok!'     )

   print('Generating a caption...'     )
   UpperCamelCase   :  Optional[int]						     =     ''
   UpperCamelCase   :  Union[str, Any]						     =     tokenizer(snake_case__	,					return_tensors='pt'     ).input_ids.to(snake_case__     )

   UpperCamelCase   :  str						     =     original_model.generate({'image': original_pixel_values}     )
   UpperCamelCase   :  str						     =     hf_model.generate(
       snake_case__	,					snake_case__	,					do_sample=snake_case__	,					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:'	,					snake_case__     )
   UpperCamelCase   :  Optional[int]						     =     input_ids.shape[1]
   UpperCamelCase   :  Union[str, Any]						     =     processor.batch_decode(outputs[:, prompt_length:]	,					skip_special_tokens=snake_case__     )
   UpperCamelCase   :  Dict						     =     [text.strip() for text in output_text]
   print('HF generation:'	,					snake_case__     )

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

   if push_to_hub:
      processor.push_to_hub(F"""nielsr/{model_name}"""     )
      hf_model.push_to_hub(F"""nielsr/{model_name}"""     )


if __name__ == "__main__":
    __UpperCAmelCase	       =	argparse.ArgumentParser()
    __UpperCAmelCase	       =	[
        '''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''',
    )

    __UpperCAmelCase	       =	parser.parse_args()

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



