
'''simple docstring'''



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()
__A       =      logging.get_logger(__name__)





def 	_A						(							lowercase__		):
						lowercase__								=    tf.image.resize(lowercase__						,						(346, 346)		)
						lowercase__								=    tf.image.crop_to_bounding_box(lowercase__						,						(346 - 289) // 2						,						(346 - 289) // 2						,						289						,						289		)
						return image





def 	_A						(							):
						lowercase__								=    EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""		)
						lowercase__								=    289
						lowercase__								=    640
						lowercase__								=    {"""0""": """LABEL_0""", """1""": """LABEL_1"""}
						lowercase__								=    {"""LABEL_0""": 0, """LABEL_1""": 1}
						lowercase__								=    []

						lowercase__								=    BertConfig()
						lowercase__								=    AlignConfig.from_text_vision_configs(
						    text_config=lowercase__						,						vision_config=lowercase__						,						projection_dim=640		)
						return config





def 	_A						(							):
						lowercase__								=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowercase__								=    Image.open(requests.get(lowercase__						,						stream=lowercase__		).raw		)
						return im





def 	_A						(							):
						lowercase__								=    EfficientNetImageProcessor(
						    do_center_crop=lowercase__						,						rescale_factor=1 / 1_2_7.5						,						rescale_offset=lowercase__						,						do_normalize=lowercase__						,						include_top=lowercase__						,						resample=Image.BILINEAR						,						)
						lowercase__								=    BertTokenizer.from_pretrained("""bert-base-uncased"""		)
						lowercase__								=    64
						lowercase__								=    AlignProcessor(image_processor=lowercase__						,						tokenizer=lowercase__		)
						return processor





def 	_A						(							lowercase__		):
						# EfficientNet image encoder
						lowercase__								=    [v.split("""_"""		)[0].split("""block"""		)[1] for v in original_param_names if v.startswith("""block"""		)]
						lowercase__								=    list(set(lowercase__		)		)
						lowercase__								=    sorted(lowercase__		)
						lowercase__								=    len(lowercase__		)
						lowercase__								=    {b: str(lowercase__		) for b, i in zip(lowercase__						,						range(lowercase__		)		)}

						lowercase__								=    []
						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:
												lowercase__								=    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''')		)

						lowercase__								=    {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		lowercase__								=    """vision_model.""" + item[1]

    # BERT text encoder
						lowercase__								=    []
						lowercase__								=    """tf_bert_model/bert"""
						lowercase__								=    """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:
																		lowercase__								=    item[1]
						return key_mapping





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						list(hf_params.keys()		)

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

												lowercase__								=    key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		lowercase__								=    torch.from_numpy(lowercase__		).permute(3						,						2						,						0						,						1		)
												elif "embeddings" in key:
																		lowercase__								=    torch.from_numpy(lowercase__		)
												elif "depthwise_kernel" in key:
																		lowercase__								=    torch.from_numpy(lowercase__		).permute(2						,						3						,						0						,						1		)
												elif "kernel" in key:
																		lowercase__								=    torch.from_numpy(np.transpose(lowercase__		)		)
												elif "temperature" in key:
																		lowercase__								=    value
												elif "bn/gamma" or "bn/beta" in key:
																		lowercase__								=    torch.from_numpy(np.transpose(lowercase__		)		).squeeze()
												else:
																		lowercase__								=    torch.from_numpy(lowercase__		)

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





@torch.no_grad()
def 	_A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    64
						lowercase__								=    Tokenizer(lowercase__		)
						lowercase__								=    align.Align("""efficientnet-b7"""						,						"""bert-base"""						,						640						,						lowercase__						,						tok.get_vocab_size()		)
						original_model.compile()
						original_model.load_weights(lowercase__		)

						lowercase__								=    original_model.trainable_variables
						lowercase__								=    original_model.non_trainable_variables
						lowercase__								=    {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												lowercase__								=    param.numpy()
						lowercase__								=    list(tf_params.keys()		)

						# Load HuggingFace model
						lowercase__								=    get_align_config()
						lowercase__								=    AlignModel(lowercase__		).eval()
						lowercase__								=    hf_model.state_dict()

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

						# Initialize processor
						lowercase__								=    get_processor()
						lowercase__								=    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():
												lowercase__								=    hf_model(**lowercase__		)

						lowercase__								=    outputs.image_embeds.detach().numpy()
						lowercase__								=    outputs.text_embeds.detach().numpy()

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

						lowercase__								=    original_model.image_encoder(lowercase__						,						training=lowercase__		)
						lowercase__								=    original_model.text_encoder(lowercase__						,						training=lowercase__		)

						lowercase__								=    tf.nn.la_normalize(lowercase__						,						axis=-1		)
						lowercase__								=    tf.nn.la_normalize(lowercase__						,						axis=-1		)

						# Check whether original and HF model outputs match  -> np.allclose
						if not np.allclose(lowercase__						,						lowercase__						,						atol=1e-3		):
												raise ValueError("""The predicted image features are not the same."""		)
						if not np.allclose(lowercase__						,						lowercase__						,						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(lowercase__		):
																		os.mkdir(lowercase__		)
												# Save converted model and image processor
												hf_model.save_pretrained(lowercase__		)
												processor.save_pretrained(lowercase__		)

						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__":
				__A       =      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")

				__A       =      parser.parse_args()
				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








'''simple docstring'''



def 	_A						(							lowercase__		):
						lowercase__								=    1.3
						lowercase__								=    len(lowercase__		)
						lowercase__								=    False

						while not completed:
												# Update the gap value for a next comb
												lowercase__								=    int(gap / shrink_factor		)
												if gap <= 1:
																		lowercase__								=    True

												lowercase__								=    0
												while index + gap < len(lowercase__		):
																		if data[index] > data[index + gap]:
																								# Swap values
																								lowercase__	,  lowercase__								=    data[index + gap], data[index]
																								lowercase__								=    False
																		index += 1

						return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				print(comb_sort(unsorted))






