import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase      :				Dict     			=logging.get_logger(__name__)







def     A__	(       __A						):







    '''simple docstring'''
    _lowerCamelCase  :		Union[str, Any]			    =    tf.image.resize(UpperCamelCase__ ,			(346, 346)						)
    _lowerCamelCase  :		Tuple			    =    tf.image.crop_to_bounding_box(UpperCamelCase__ ,			(346 - 289) // 2 ,			(346 - 289) // 2 ,			289 ,			289						)
    return image







def     A__	(       ):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""						)
    _lowerCamelCase  :		Tuple			    =    289
    _lowerCamelCase  :		Union[str, Any]			    =    640
    _lowerCamelCase  :		Tuple			    =    {"""0""": """LABEL_0""", """1""": """LABEL_1"""}
    _lowerCamelCase  :		Any			    =    {"""LABEL_0""": 0, """LABEL_1""": 1}
    _lowerCamelCase  :		List[Any]			    =    []

    _lowerCamelCase  :		Dict			    =    BertConfig()
    _lowerCamelCase  :		Optional[Any]			    =    AlignConfig.from_text_vision_configs(
        text_config=UpperCamelCase__ ,			vision_config=UpperCamelCase__ ,			projection_dim=640						)
    return config







def     A__	(       ):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
    _lowerCamelCase  :		Optional[Any]			    =    Image.open(requests.get(UpperCamelCase__ ,			stream=UpperCamelCase__						).raw						)
    return im







def     A__	(       ):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    EfficientNetImageProcessor(
        do_center_crop=UpperCamelCase__ ,			rescale_factor=1 / 127.5 ,			rescale_offset=UpperCamelCase__ ,			do_normalize=UpperCamelCase__ ,			include_top=UpperCamelCase__ ,			resample=Image.BILINEAR ,			)
    _lowerCamelCase  :		List[Any]			    =    BertTokenizer.from_pretrained("""bert-base-uncased"""						)
    _lowerCamelCase  :		Tuple			    =    64
    _lowerCamelCase  :		int			    =    AlignProcessor(image_processor=UpperCamelCase__ ,			tokenizer=UpperCamelCase__						)
    return processor







def     A__	(       __A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    [v.split("""_"""						)[0].split("""block"""						)[1] for v in original_param_names if v.startswith("""block"""						)]
    _lowerCamelCase  :		Dict			    =    list(set(UpperCamelCase__						)						)
    _lowerCamelCase  :		Any			    =    sorted(UpperCamelCase__						)
    _lowerCamelCase  :		Any			    =    len(UpperCamelCase__						)
    _lowerCamelCase  :		int			    =    {b: str(UpperCamelCase__						) for b, i in zip(UpperCamelCase__ ,			range(UpperCamelCase__						)						)}

    _lowerCamelCase  :		Any			    =    []
    rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")						)
    rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")						)
    rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")						)
    rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")						)
    rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")						)

    for b in block_names:
        _lowerCamelCase  :		int			    =    block_name_mapping[b]
        rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")						)
        rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")						)
        rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")						)
        rename_keys.append(
            (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")						)
        rename_keys.append(
            (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")						)
        rename_keys.append(
            (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")						)
        rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")						)
        rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")						)
        rename_keys.append(
            (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")						)
        rename_keys.append(
            (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")						)

        rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")						)
        rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")						)
        rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")						)
        rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")						)
        rename_keys.append(
            (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")						)
        rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")						)
        rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")						)
        rename_keys.append(
            (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")						)
        rename_keys.append(
            (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")						)

    _lowerCamelCase  :		Optional[int]			    =    {}
    for item in rename_keys:
        if item[0] in original_param_names:
            _lowerCamelCase  :		Optional[Any]			    =    """vision_model.""" + item[1]

    # BERT text encoder
    _lowerCamelCase  :		Any			    =    []
    _lowerCamelCase  :		Union[str, Any]			    =    """tf_bert_model/bert"""
    _lowerCamelCase  :		Any			    =    """text_model"""
    for i in range(12						):
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
                F"""{new}.encoder.layer.{i}.attention.self.query.weight""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
                F"""{new}.encoder.layer.{i}.attention.self.query.bias""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
                F"""{new}.encoder.layer.{i}.attention.self.key.weight""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
                F"""{new}.encoder.layer.{i}.attention.self.key.bias""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
                F"""{new}.encoder.layer.{i}.attention.self.value.weight""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
                F"""{new}.encoder.layer.{i}.attention.self.value.bias""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
                F"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
                F"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
                F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
                F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
                F"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
            )						)
        rename_keys.append(
            (
                F"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
                F"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
            )						)
        rename_keys.append(
            (F"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", F"""{new}.encoder.layer.{i}.output.dense.weight""")						)
        rename_keys.append(
            (F"""{old}/encoder/layer_._{i}/output/dense/bias:0""", F"""{new}.encoder.layer.{i}.output.dense.bias""")						)
        rename_keys.append(
            (F"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")						)
        rename_keys.append(
            (F"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")						)

    rename_keys.append((F"""{old}/embeddings/word_embeddings/weight:0""", F"""{new}.embeddings.word_embeddings.weight""")						)
    rename_keys.append(
        (F"""{old}/embeddings/position_embeddings/embeddings:0""", F"""{new}.embeddings.position_embeddings.weight""")						)
    rename_keys.append(
        (F"""{old}/embeddings/token_type_embeddings/embeddings:0""", F"""{new}.embeddings.token_type_embeddings.weight""")						)
    rename_keys.append((F"""{old}/embeddings/LayerNorm/gamma:0""", F"""{new}.embeddings.LayerNorm.weight""")						)
    rename_keys.append((F"""{old}/embeddings/LayerNorm/beta:0""", F"""{new}.embeddings.LayerNorm.bias""")						)

    rename_keys.append((F"""{old}/pooler/dense/kernel:0""", F"""{new}.pooler.dense.weight""")						)
    rename_keys.append((F"""{old}/pooler/dense/bias:0""", F"""{new}.pooler.dense.bias""")						)
    rename_keys.append(("""dense/kernel:0""", """text_projection.weight""")						)
    rename_keys.append(("""dense/bias:0""", """text_projection.bias""")						)
    rename_keys.append(("""dense/bias:0""", """text_projection.bias""")						)
    rename_keys.append(("""temperature:0""", """temperature""")						)

    for item in rename_keys:
        if item[0] in original_param_names:
            _lowerCamelCase  :		List[Any]			    =    item[1]
    return key_mapping







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    list(hf_params.keys()						)

    for key, value in tf_params.items():
        if key not in key_mapping:
            continue

        _lowerCamelCase  :		Tuple			    =    key_mapping[key]
        if "_conv" in key and "kernel" in key:
            _lowerCamelCase  :		Any			    =    torch.from_numpy(UpperCamelCase__						).permute(3 ,			2 ,			0 ,			1						)
        elif "embeddings" in key:
            _lowerCamelCase  :		Optional[int]			    =    torch.from_numpy(UpperCamelCase__						)
        elif "depthwise_kernel" in key:
            _lowerCamelCase  :		List[str]			    =    torch.from_numpy(UpperCamelCase__						).permute(2 ,			3 ,			0 ,			1						)
        elif "kernel" in key:
            _lowerCamelCase  :		str			    =    torch.from_numpy(np.transpose(UpperCamelCase__						)						)
        elif "temperature" in key:
            _lowerCamelCase  :		Optional[int]			    =    value
        elif "bn/gamma" or "bn/beta" in key:
            _lowerCamelCase  :		Union[str, Any]			    =    torch.from_numpy(np.transpose(UpperCamelCase__						)						).squeeze()
        else:
            _lowerCamelCase  :		str			    =    torch.from_numpy(UpperCamelCase__						)

        # Replace HF parameters with original TF model parameters
        hf_params[hf_key].copy_(UpperCamelCase__						)







@torch.no_grad()
def     A__	(       __A ,			__A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    64
    _lowerCamelCase  :		Any			    =    Tokenizer(UpperCamelCase__						)
    _lowerCamelCase  :		Union[str, Any]			    =    align.Align("""efficientnet-b7""" ,			"""bert-base""" ,			640 ,			UpperCamelCase__ ,			tok.get_vocab_size()						)
    original_model.compile()
    original_model.load_weights(UpperCamelCase__						)

    _lowerCamelCase  :		List[str]			    =    original_model.trainable_variables
    _lowerCamelCase  :		Tuple			    =    original_model.non_trainable_variables
    _lowerCamelCase  :		Any			    =    {param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        _lowerCamelCase  :		str			    =    param.numpy()
    _lowerCamelCase  :		List[str]			    =    list(tf_params.keys()						)

    # Load HuggingFace model
    _lowerCamelCase  :		Optional[Any]			    =    get_align_config()
    _lowerCamelCase  :		Dict			    =    AlignModel(UpperCamelCase__						).eval()
    _lowerCamelCase  :		List[Any]			    =    hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print("""Converting parameters..."""						)
    _lowerCamelCase  :		Any			    =    rename_keys(UpperCamelCase__						)
    replace_params(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__						)

    # Initialize processor
    _lowerCamelCase  :		Union[str, Any]			    =    get_processor()
    _lowerCamelCase  :		int			    =    processor(
        images=prepare_img() ,			text="""A picture of a cat""" ,			padding="""max_length""" ,			max_length=64 ,			return_tensors="""pt"""						)

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        _lowerCamelCase  :		Dict			    =    hf_model(**UpperCamelCase__						)

    _lowerCamelCase  :		int			    =    outputs.image_embeds.detach().numpy()
    _lowerCamelCase  :		str			    =    outputs.text_embeds.detach().numpy()

    # Original model inference
    _lowerCamelCase  :		List[str]			    =    False
    _lowerCamelCase  :		List[str]			    =    EfficientNetImageProcessor(
        do_center_crop=UpperCamelCase__ ,			do_rescale=UpperCamelCase__ ,			do_normalize=UpperCamelCase__ ,			include_top=UpperCamelCase__ ,			resample=Image.BILINEAR ,			)
    _lowerCamelCase  :		str			    =    tf_image_processor(images=prepare_img() ,			return_tensors="""tf""" ,			data_format="""channels_last"""						)["""pixel_values"""]
    _lowerCamelCase  :		Dict			    =    tok(tf.constant(["""A picture of a cat"""]						)						)

    _lowerCamelCase  :		List[str]			    =    original_model.image_encoder(UpperCamelCase__ ,			training=UpperCamelCase__						)
    _lowerCamelCase  :		Dict			    =    original_model.text_encoder(UpperCamelCase__ ,			training=UpperCamelCase__						)

    _lowerCamelCase  :		Tuple			    =    tf.nn.la_normalize(UpperCamelCase__ ,			axis=-1						)
    _lowerCamelCase  :		List[str]			    =    tf.nn.la_normalize(UpperCamelCase__ ,			axis=-1						)

    # Check whether original and HF model outputs match  -> np.allclose
    if not np.allclose(UpperCamelCase__ ,			UpperCamelCase__ ,			atol=1E-3						):
        raise ValueError("""The predicted image features are not the same."""						)
    if not np.allclose(UpperCamelCase__ ,			UpperCamelCase__ ,			atol=1E-3						):
        raise ValueError("""The predicted text features are not the same."""						)
    print("""Model outputs match!"""						)

    if save_model:
        # Create folder to save model
        if not os.path.isdir(UpperCamelCase__						):
            os.mkdir(UpperCamelCase__						)
        # Save converted model and image processor
        hf_model.save_pretrained(UpperCamelCase__						)
        processor.save_pretrained(UpperCamelCase__						)

    if push_to_hub:
        # Push model and image processor to hub
        print("""Pushing converted ALIGN to the hub..."""						)
        processor.push_to_hub("""align-base"""						)
        hf_model.push_to_hub("""align-base"""						)


if __name__ == "__main__":
    lowerCAmelCase      :				str     			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--checkpoint_path",
        default="./weights/model-weights",
        type=str,
        help="Path to the pretrained TF ALIGN checkpoint.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default="hf_model",
        type=str,
        help="Path to the output PyTorch model directory.",
    )
    parser.add_argument("--save_model", action="store_true", help="Save model to local")
    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

    lowerCAmelCase      :				Union[str, Any]     			=parser.parse_args()
    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				__lowerCAmelCase					=  min(re.search("""^ +"""						,			UpperCamelCase__   ).end() if t.startswith(""" """   ) else 0 for t in string.splitlines()   )
				return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCamelCase__   )]   )


__A						:		str          =						"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__A						:		int          =						"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__A						:		Any          =						"\\n# Begin of markdown\n\nSome cool dataset card\n"

class 		lowercase_       (	unittest.TestCase				):




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase					=  Path(_lowercase) / """README.md"""
													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertDictEqual(_lowercase,     {"""language""": ["""zh""", """en"""], """task_ids""": ["""sentiment-classification"""]})

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertDictEqual(_lowercase,     {})

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertEqual(_lowercase,     {})







					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            """)
									assert DatasetMetadata.from_yaml_string(_lowercase)

									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            """)
									with self.assertRaises(_lowercase):
													DatasetMetadata.from_yaml_string(_lowercase)

									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            """)
									assert DatasetMetadata.from_yaml_string(_lowercase)





