
'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						"huggingface/label-files"
    snake_case_ :    Optional[Any]     			=						"imagenet-1k-id2label.json"
    snake_case_ :    List[Any]     			=						json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		repo_type="dataset"       ),		"r"       )       )
    snake_case_ :    List[Any]     			=						{int(__SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    snake_case_ :    List[str]     			=						{v: k for k, v in idalabel.items()}

    snake_case_ :    Optional[Any]     			=						"std_conv" if "bit" in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    snake_case_ :    Optional[int]     			=						BitConfig(
        conv_layer=__SCREAMING_SNAKE_CASE,		num_labels=1_0_0_0,		idalabel=__SCREAMING_SNAKE_CASE,		labelaid=__SCREAMING_SNAKE_CASE,		)

    return config

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if "stem.conv" in name:
        snake_case_ :    List[str]     			=						name.replace("stem.conv",		"bit.embedder.convolution"       )
    if "blocks" in name:
        snake_case_ :    Dict     			=						name.replace("blocks",		"layers"       )
    if "head.fc" in name:
        snake_case_ :    str     			=						name.replace("head.fc",		"classifier.1"       )
    if name.startswith("norm"       ):
        snake_case_ :    Union[str, Any]     			=						"bit." + name
    if "bit" not in name and "classifier" not in name:
        snake_case_ :    Optional[Any]     			=						"bit.encoder." + name

    return name

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case_ :    str     			=						Image.open(requests.get(__SCREAMING_SNAKE_CASE,		stream=__SCREAMING_SNAKE_CASE       ).raw       )
    return im

@torch.no_grad()
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    snake_case_ :    Any     			=						get_config(__SCREAMING_SNAKE_CASE       )

    # load original model from timm
    snake_case_ :    str     			=						create_model(__SCREAMING_SNAKE_CASE,		pretrained=__SCREAMING_SNAKE_CASE       )
    timm_model.eval()

    # load state_dict of original model
    snake_case_ :    List[Any]     			=						timm_model.state_dict()
    for key in state_dict.copy().keys():
        snake_case_ :    Union[str, Any]     			=						state_dict.pop(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						val.squeeze() if "head" in key else val

    # load HuggingFace model
    snake_case_ :    Union[str, Any]     			=						BitForImageClassification(__SCREAMING_SNAKE_CASE       )
    model.eval()
    model.load_state_dict(__SCREAMING_SNAKE_CASE       )

    # create image processor
    snake_case_ :    Optional[int]     			=						create_transform(**resolve_data_config({},		model=__SCREAMING_SNAKE_CASE       )       )
    snake_case_ :    str     			=						transform.transforms

    snake_case_ :    str     			=						{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    snake_case_ :    str     			=						BitImageProcessor(
        do_resize=__SCREAMING_SNAKE_CASE,		size={"shortest_edge": timm_transforms[0].size},		resample=pillow_resamplings[timm_transforms[0].interpolation.value],		do_center_crop=__SCREAMING_SNAKE_CASE,		crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]},		do_normalize=__SCREAMING_SNAKE_CASE,		image_mean=timm_transforms[-1].mean.tolist(),		image_std=timm_transforms[-1].std.tolist(),		)

    snake_case_ :    str     			=						prepare_img()
    snake_case_ :    Any     			=						transform(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       )
    snake_case_ :    List[Any]     			=						processor(__SCREAMING_SNAKE_CASE,		return_tensors="pt"       ).pixel_values

    # verify pixel values
    assert torch.allclose(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    # verify logits
    with torch.no_grad():
        snake_case_ :    List[Any]     			=						model(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    List[str]     			=						outputs.logits

    print("Logits:",		logits[0, :3]       )
    print("Predicted class:",		model.config.idalabel[logits.argmax(-1       ).item()]       )
    snake_case_ :    Tuple     			=						timm_model(__SCREAMING_SNAKE_CASE       )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(__SCREAMING_SNAKE_CASE,		outputs.logits,		atol=1E-3       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        Path(__SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE       )
        print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}'       )
        model.save_pretrained(__SCREAMING_SNAKE_CASE       )
        processor.save_pretrained(__SCREAMING_SNAKE_CASE       )

    if push_to_hub:
        print(f'Pushing model {model_name} and processor to the hub'       )
        model.push_to_hub(f'ybelkada/{model_name}'       )
        processor.push_to_hub(f'ybelkada/{model_name}'       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="resnetv2_50x1_bitm",
      type=str,
      help="Name of the BiT timm model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
      help="Whether to push the model to the hub.",
  )

  a_											=						parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


a_											=						["bert-base-uncased", "bert-base-cased"]
a_											=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






  class    UpperCAmelCase_				(	tf.keras.Model					):





   def __init__(    self ,       lowercase_):
       super().__init__()
       snake_case_ :    Union[str, Any]     			=						tokenizer
       snake_case_ :    List[str]     			=						AutoConfig.from_pretrained(lowercase_)
       snake_case_ :    List[str]     			=						TFAutoModel.from_config(lowercase_)





   def        snake_case__		(    self ,       lowercase_):
       snake_case_ :    List[str]     			=						self.tokenizer(lowercase_)
       snake_case_ :    str     			=						self.bert(**lowercase_)
       return out["pooler_output"]






@require_tf
@require_tensorflow_text
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     super().setUp()

     snake_case_ :    Dict     			=						[
         BertTokenizer.from_pretrained(lowercase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     snake_case_ :    Tuple     			=						[TFBertTokenizer.from_pretrained(lowercase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(lowercase_ ,       use_fast_bert_tokenizer=lowercase_)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers) == len(self.tf_tokenizers)

     snake_case_ :    Dict     			=						[
         "This is a straightforward English test sentence.",
         "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
         "Now we're going to add some Chinese: 一 二 三 一二三",
         "And some much more rare Chinese: 齉 堃 齉堃",
         "Je vais aussi écrire en français pour tester les accents",
         "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
     ]
     snake_case_ :    str     			=						list(zip(self.test_sentences ,       self.test_sentences[::-1]))





 def        snake_case__		(    self):
     for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Any     			=						tokenizer(lowercase_ ,       return_tensors="tf" ,       padding="longest")
             snake_case_ :    Union[str, Any]     			=						tf_tokenizer(lowercase_)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,       tf.intaa) == tf_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    Union[str, Any]     			=						tf_tokenizer(self.paired_sentences)
         snake_case_ :    str     			=						tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences] ,       text_pair=[sentence[1] for sentence in self.paired_sentences] ,       )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,       tf.intaa) == separated_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    int     			=						tf.function(lowercase_)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Optional[int]     			=						tf.constant(lowercase_)
             snake_case_ :    Tuple     			=						compiled_tokenizer(lowercase_)
             snake_case_ :    Any     			=						tf_tokenizer(lowercase_)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    List[Any]     			=						ModelToSave(tokenizer=lowercase_)
         snake_case_ :    Any     			=						tf.convert_to_tensor(self.test_sentences)
         snake_case_ :    List[Any]     			=						model(lowercase_)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             snake_case_ :    Optional[Any]     			=						Path(lowercase_) / "saved.model"
             model.save(lowercase_)
             snake_case_ :    Tuple     			=						tf.keras.models.load_model(lowercase_)
         snake_case_ :    str     			=						loaded_model(lowercase_)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) ,       1E-5)




