




'''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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
      lowerCamelCase_	  =					Image.open(requests.get(lowercase			,							stream=lowercase  ).raw  ).convert('RGB'  )

      lowerCamelCase_	  =					transforms.Compose(
          [
              transforms.Resize((image_size, image_size)			,							interpolation=InterpolationMode.BICUBIC  ),
              transforms.ToTensor(),
              transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)			,							(0.2686_2954, 0.2613_0258, 0.2757_7711)  ),
          ]  )
      lowerCamelCase_	  =					transform(lowercase  ).unsqueeze(0  ).to(lowercase  )
      return image

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      if "visual_encoder" in key:
            lowerCamelCase_	  =					re.sub('visual_encoder*'			,							'vision_model.encoder'			,							lowercase  )
      if "blocks" in key:
            lowerCamelCase_	  =					re.sub(r'blocks'			,							'layers'			,							lowercase  )
      if "attn" in key:
            lowerCamelCase_	  =					re.sub(r'attn'			,							'self_attn'			,							lowercase  )
      if "norm1" in key:
            lowerCamelCase_	  =					re.sub(r'norm1'			,							'layer_norm1'			,							lowercase  )
      if "norm2" in key:
            lowerCamelCase_	  =					re.sub(r'norm2'			,							'layer_norm2'			,							lowercase  )
      if "encoder.norm" in key:
            lowerCamelCase_	  =					re.sub(r'encoder.norm'			,							'post_layernorm'			,							lowercase  )
      if "encoder.patch_embed.proj" in key:
            lowerCamelCase_	  =					re.sub(r'encoder.patch_embed.proj'			,							'embeddings.patch_embedding'			,							lowercase  )

      if "encoder.pos_embed" in key:
            lowerCamelCase_	  =					re.sub(r'encoder.pos_embed'			,							'embeddings.position_embedding'			,							lowercase  )
      if "encoder.cls_token" in key:
            lowerCamelCase_	  =					re.sub(r'encoder.cls_token'			,							'embeddings.class_embedding'			,							lowercase  )

      if "self_attn" in key:
            lowerCamelCase_	  =					re.sub(r'self_attn.proj'			,							'self_attn.projection'			,							lowercase  )

      return key

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Optional[Any]=None  ):


      '''simple docstring'''



      if config_path is not None:
            lowerCamelCase_	  =					BlipConfig.from_pretrained(lowercase  )
      else:
            lowerCamelCase_	  =					BlipConfig(projection_dim=5_12			,							text_config={}			,							vision_config={}  )

      lowerCamelCase_	  =					BlipForConditionalGeneration(lowercase  ).eval()

      lowerCamelCase_	  =					'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

      lowerCamelCase_	  =					blip_decoder(pretrained=lowercase			,							image_size=3_84			,							vit='base'  )
      lowerCamelCase_	  =					pt_model.eval()

      lowerCamelCase_	  =					pt_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase_	  =					modified_state_dict.pop(lowercase  )
            lowerCamelCase_	  =					rename_key(lowercase  )
            lowerCamelCase_	  =					value

      hf_model.load_state_dict(lowercase  )

      lowerCamelCase_	  =					3_84
      lowerCamelCase_	  =					load_demo_image(image_size=lowercase			,							device='cpu'  )
      lowerCamelCase_	  =					BertTokenizer.from_pretrained('bert-base-uncased'  )
      lowerCamelCase_	  =					tokenizer(['a picture of']  ).input_ids

      lowerCamelCase_	  =					hf_model.generate(lowercase			,							lowercase  )

      assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

      lowerCamelCase_	  =					hf_model.generate(lowercase  )

      assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

      # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
      lowerCamelCase_	  =					(
          'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
      )

      lowerCamelCase_	  =					blip_vqa(pretrained=lowercase			,							image_size=lowercase			,							vit='base'  )
      vqa_model.eval()

      lowerCamelCase_	  =					vqa_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase_	  =					modified_state_dict.pop(lowercase  )
            lowerCamelCase_	  =					rename_key(lowercase  )
            lowerCamelCase_	  =					value

      lowerCamelCase_	  =					BlipForQuestionAnswering(lowercase  )

      hf_vqa_model.load_state_dict(lowercase  )

      lowerCamelCase_	  =					['How many dogs are in this image?']
      lowerCamelCase_	  =					tokenizer(lowercase			,							return_tensors='pt'  ).input_ids

      lowerCamelCase_	  =					hf_vqa_model.generate(lowercase			,							lowercase  )
      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'  )

      lowerCamelCase_	  =					'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

      lowerCamelCase_	  =					blip_itm(pretrained=lowercase			,							image_size=lowercase			,							vit='base'  )
      itm_model.eval()

      lowerCamelCase_	  =					itm_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase_	  =					modified_state_dict.pop(lowercase  )
            lowerCamelCase_	  =					rename_key(lowercase  )
            lowerCamelCase_	  =					value

      lowerCamelCase_	  =					BlipForImageTextRetrieval(lowercase  )

      lowerCamelCase_	  =					['A picture of a woman with a dog sitting in a beach']
      lowerCamelCase_	  =					tokenizer(
          lowercase			,							return_tensors='pt'			,							padding='max_length'			,							truncation=lowercase			,							max_length=35			,							).input_ids

      hf_itm_model.load_state_dict(lowercase  )
      hf_itm_model.eval()

      lowerCamelCase_	  =					hf_itm_model(lowercase			,							lowercase			,							use_itm_head=lowercase  )
      lowerCamelCase_	  =					hf_itm_model(lowercase			,							lowercase			,							use_itm_head=lowercase  )

      assert out[0].item() == 0.2110_6874_9427_7954
      assert torch.nn.functional.softmax(out_itm[0]			,							dim=1  )[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
 lowerCamelCase       :	str									=     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")
 lowerCamelCase       :	int									=     parser.parse_args()

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









import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowerCamelCase       :	List[Any]									=     [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
 import run_clm_flax
 import run_flax_glue
 import run_flax_ner
 import run_mlm_flax
 import run_qa
 import run_summarization_flax
 import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase       :	Tuple									=     logging.getLogger()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					argparse.ArgumentParser()
      parser.add_argument('-f'  )
      lowerCamelCase_	  =					parser.parse_args()
      return args.f

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Dict="eval"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					os.path.join(lowercase			,							f"""{split}_results.json"""  )
      if os.path.exists(lowercase  ):
            with open(lowercase			,							'r'  ) as f:
                  return json.load(lowercase  )
      raise ValueError(f"""can't find {path}"""  )


lowerCamelCase       :	str									=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_flax_glue.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)

 @slow
 def        a__				(       self      :						List[str]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_clm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							100						)

 @slow
 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_summarization_flax.main()
             lowerCamelCase_	  =					get_results(A_		,							split='test'						)
             self.assertGreaterEqual(result['test_rouge1']		,							10						)
             self.assertGreaterEqual(result['test_rouge2']		,							2						)
             self.assertGreaterEqual(result['test_rougeL']		,							7						)
             self.assertGreaterEqual(result['test_rougeLsum']		,							7						)

 @slow
 def        a__				(       self      :						Optional[int]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							42						)

 @slow
 def        a__				(       self      :						Optional[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_ta_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.42						)

 @slow
 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					7 if get_gpu_count() > 1 else 2

       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_flax_ner.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)
             self.assertGreaterEqual(result['eval_f1']		,							0.3						)





 @slow
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_qa.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_f1']		,							30						)
             self.assertGreaterEqual(result['eval_exact']		,							30						)



