




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     :		str								=       [
    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     :		List[str]								=       logging.getLogger()







def SCREAMING_SNAKE_CASE    (	)       ->	List[str]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_="eval"						)       ->	Any:



       """simple docstring"""

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


_lowerCamelCase     :		int								=       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class     UpperCamelCase_   (			lowerCamelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Optional[Any]:





           '''simple docstring'''

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

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


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Any:





           '''simple docstring'''

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

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


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





           '''simple docstring'''

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

           with patch.object(lowercase__ ,			'''argv''' ,			lowercase__):
                  run_summarization_flax.main()
                  A__							=   get_results(lowercase__ ,			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 	SCREAMING_SNAKE_CASE							(      self				:     str)						->str:





           '''simple docstring'''

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

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


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Tuple:





           '''simple docstring'''

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

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


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





           '''simple docstring'''

           A__							=   7 if get_gpu_count() > 1 else 2

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

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





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[Any]:





           '''simple docstring'''

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

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








import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       if "cls_token" in name:
              A__							=   name.replace('''cls_token'''			,    '''vit.embeddings.cls_token'''						)
       if "mask_token" in name:
              A__							=   name.replace('''mask_token'''			,    '''decoder.mask_token'''						)
       if "decoder_pos_embed" in name:
              A__							=   name.replace('''decoder_pos_embed'''			,    '''decoder.decoder_pos_embed'''						)
       if "pos_embed" in name and "decoder" not in name:
              A__							=   name.replace('''pos_embed'''			,    '''vit.embeddings.position_embeddings'''						)
       if "patch_embed.proj" in name:
              A__							=   name.replace('''patch_embed.proj'''			,    '''vit.embeddings.patch_embeddings.projection'''						)
       if "patch_embed.norm" in name:
              A__							=   name.replace('''patch_embed.norm'''			,    '''vit.embeddings.norm'''						)
       if "decoder_blocks" in name:
              A__							=   name.replace('''decoder_blocks'''			,    '''decoder.decoder_layers'''						)
       if "blocks" in name:
              A__							=   name.replace('''blocks'''			,    '''vit.encoder.layer'''						)
       if "attn.proj" in name:
              A__							=   name.replace('''attn.proj'''			,    '''attention.output.dense'''						)
       if "attn" in name:
              A__							=   name.replace('''attn'''			,    '''attention.self'''						)
       if "norm1" in name:
              A__							=   name.replace('''norm1'''			,    '''layernorm_before'''						)
       if "norm2" in name:
              A__							=   name.replace('''norm2'''			,    '''layernorm_after'''						)
       if "mlp.fc1" in name:
              A__							=   name.replace('''mlp.fc1'''			,    '''intermediate.dense'''						)
       if "mlp.fc2" in name:
              A__							=   name.replace('''mlp.fc2'''			,    '''output.dense'''						)
       if "decoder_embed" in name:
              A__							=   name.replace('''decoder_embed'''			,    '''decoder.decoder_embed'''						)
       if "decoder_norm" in name:
              A__							=   name.replace('''decoder_norm'''			,    '''decoder.decoder_norm'''						)
       if "decoder_pred" in name:
              A__							=   name.replace('''decoder_pred'''			,    '''decoder.decoder_pred'''						)
       if "norm.weight" in name and "decoder" not in name:
              A__							=   name.replace('''norm.weight'''			,    '''vit.layernorm.weight'''						)
       if "norm.bias" in name and "decoder" not in name:
              A__							=   name.replace('''norm.bias'''			,    '''vit.layernorm.bias'''						)

       return name







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__							=   orig_state_dict.pop(lowercase_						)

              if "qkv" in key:
                     A__							=   key.split('''.'''						)
                     A__							=   int(key_split[1]						)
                     if "decoder_blocks" in key:
                            A__							=   config.decoder_hidden_size
                            A__							=   '''decoder.decoder_layers.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]
                     else:
                            A__							=   config.hidden_size
                            A__							=   '''vit.encoder.layer.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]

              else:
                     A__							=   val

       return orig_state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   ViTMAEConfig()
       if "large" in checkpoint_url:
              A__							=   1_024
              A__							=   4_096
              A__							=   24
              A__							=   16
       elif "huge" in checkpoint_url:
              A__							=   14
              A__							=   1_280
              A__							=   5_120
              A__							=   32
              A__							=   16

       A__							=   ViTMAEForPreTraining(lowercase_						)

       A__							=   torch.hub.load_state_dict_from_url(lowercase_			,    map_location='''cpu'''						)['''model''']

       A__							=   ViTMAEImageProcessor(size=config.image_size						)

       A__							=   convert_state_dict(lowercase_			,    lowercase_						)

       model.load_state_dict(lowercase_						)
       model.eval()

       A__							=   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

       A__							=   Image.open(requests.get(lowercase_			,    stream=lowercase_						).raw						)
       A__							=   ViTMAEImageProcessor(size=config.image_size						)
       A__							=   image_processor(images=lowercase_			,    return_tensors='''pt'''						)

       # forward pass
       torch.manual_seed(2						)
       A__							=   model(**lowercase_						)
       A__							=   outputs.logits

       if "large" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]						)
       elif "huge" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]						)
       else:
              A__							=   torch.tensor(
                  [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]						)

       # verify logits
       assert torch.allclose(logits[0, :3, :3]			,    lowercase_			,    atol=1E-4						)

       print(f"""Saving model to {pytorch_dump_folder_path}"""						)
       model.save_pretrained(lowercase_						)

       print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
       image_processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _lowerCamelCase     :		Optional[int]								=       parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


