
import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__  :  List[str]     			=   logging.get_logger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Tuple )->			Optional[int]:
    A_								=					MobileNetVaConfig(layer_norm_eps=0.0_0_1 )

    if "quant" in model_name:
        raise ValueError("Quantized models are not supported." )

    A_								=					re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"				,					snake_case__ )
    if matches:
        A_								=					float(matches[1] )
        A_								=					int(matches[2] )

    if model_name.startswith("deeplabv3_" ):
        A_								=					8
        A_								=					21
        A_								=					"pascal-voc-id2label.json"
    else:
        # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
        # of the usual 1000. The first class (index 0) is "background".
        A_								=					1001
        A_								=					"imagenet-1k-id2label.json"

    A_								=					"huggingface/label-files"
    A_								=					json.load(open(hf_hub_download(snake_case__				,					snake_case__				,					repo_type="dataset" )				,					"r" ) )

    if config.num_labels == 1001:
        A_								=					{int(snake_case__ ) + 1: v for k, v in idalabel.items()}
        A_								=					"background"
    else:
        A_								=					{int(snake_case__ ): v for k, v in idalabel.items()}

    A_								=					idalabel
    A_								=					{v: k for k, v in idalabel.items()}

    return config





def        lowerCAmelCase       (				)->			str:
    A_								=					"http://images.cocodataset.org/val2017/000000039769.jpg"
    A_								=					Image.open(requests.get(snake_case__				,					stream=snake_case__ ).raw )
    return im





@torch.no_grad()
def        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      Union[str, Any]				,					snake_case__   :      Tuple				,					snake_case__   :      int=False )->			Optional[int]:
    A_								=					get_mobilenet_va_config(snake_case__ )

    # Load 🤗 model
    if model_name.startswith("deeplabv3_" ):
        A_								=					MobileNetVaForSemanticSegmentation(snake_case__ ).eval()
    else:
        A_								=					MobileNetVaForImageClassification(snake_case__ ).eval()

    # Load weights from TensorFlow checkpoint
    load_tf_weights_in_mobilenet_va(snake_case__				,					snake_case__				,					snake_case__ )

    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    A_								=					MobileNetVaImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size}				,					size={"shortest_edge": config.image_size + 32}				,					)
    A_								=					image_processor(images=prepare_img()				,					return_tensors="pt" )
    A_								=					model(**snake_case__ )
    A_								=					outputs.logits

    if model_name.startswith("deeplabv3_" ):
        assert logits.shape == (1, 21, 65, 65)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            A_								=					torch.tensor(
                [
                    [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                    [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                    [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                ] )

        else:
            raise ValueError(f'Unknown model name: {model_name}' )

        assert torch.allclose(logits[0, :3, :3, :3]				,					snake_case__				,					atol=1e-4 )
    else:
        assert logits.shape == (1, 1001)

        if model_name == "mobilenet_v2_1.4_224":
            A_								=					torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8] )
        elif model_name == "mobilenet_v2_1.0_224":
            A_								=					torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] )
        elif model_name == "mobilenet_v2_0.75_160":
            A_								=					torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9] )
        elif model_name == "mobilenet_v2_0.35_96":
            A_								=					torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2] )
        else:
            A_								=					None

        if expected_logits is not None:
            assert torch.allclose(logits[0, :3]				,					snake_case__				,					atol=1e-4 )

    Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
    print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
    model.save_pretrained(snake_case__ )
    print(f'Saving image processor to {pytorch_dump_folder_path}' )
    image_processor.save_pretrained(snake_case__ )

    if push_to_hub:
        print("Pushing to the hub..." )
        A_								=					"google/" + model_name
        image_processor.push_to_hub(snake_case__ )
        model.push_to_hub(snake_case__ )


if __name__ == "__main__":
     __magic_name__  :  Optional[int]     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='mobilenet_v2_1.0_224',
         type=str,
         help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.',
     )
     parser.add_argument(
         '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     __magic_name__  :  List[str]     			=   parser.parse_args()
     convert_movilevit_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )








import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def        lowerCAmelCase       (				snake_case__   :      List[str]				,					snake_case__   :      List[str]				,					snake_case__   :      Optional[int]=0 )->			Union[str, Any]:
    # Format the message.
    if name is None:
        A_								=					None
    else:
        A_								=					"." * max(0				,					spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
        A_								=					fmt.format(snake_case__ )

    # Print and recurse (if needed).
    if isinstance(snake_case__				,					snake_case__ ):
        if msg is not None:
            print(snake_case__ )
        for k in val.keys():
            recursive_print(snake_case__				,					val[k]				,					spaces + 2 )
    elif isinstance(snake_case__				,					torch.Tensor ):
        print(snake_case__				,					":"				,					val.size() )
    else:
        print(snake_case__				,					":"				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      List[Any]				,					snake_case__   :      str				,					snake_case__   :      str )->			Union[str, Any]:
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    A_								=					param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A_								=					(num_heads, num_splits, hidden_size) + input_shape[1:]
    A_								=					param.view(*snake_case__ )
    A_								=					param.transpose(0				,					1 ).contiguous()
    A_								=					param.view(*snake_case__ )
    return param





def        lowerCAmelCase       (				snake_case__   :      Dict				,					snake_case__   :      str )->			Optional[int]:
    A_								=					config.n_positions
    A_								=					config.n_layer
    A_								=					config.vocab_size
    A_								=					config.n_head
    A_								=					config.n_embd // config.n_head

    A_								=					sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
    A_								=					{
        "transformer.wte.weight": word_embeddings,
        "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
        "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
        "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
    }

    A_								=					"model.language_model.encoder.layers."
    for i in range(snake_case__ ):
        A_								=					torch.tril(torch.ones((n_positions, n_positions)				,					dtype=torch.bool ) )
        A_								=					causal_mask.view(1				,					1				,					snake_case__				,					snake_case__ )
        A_								=					causal_mask
        A_								=					torch.tensor(-1e4				,					dtype=torch.bfloataa )

        A_								=					sd_megatron[f'{pf}{i}.input_layernorm.weight']
        A_								=					sd_megatron[f'{pf}{i}.input_layernorm.bias']

        A_								=					sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
        A_								=					fix_query_key_value_ordering(snake_case__				,					3				,					snake_case__				,					snake_case__ )
        A_								=					vala.transpose(0				,					1 ).contiguous()

        A_								=					sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
        A_								=					fix_query_key_value_ordering(snake_case__				,					3				,					snake_case__				,					snake_case__ )
        A_								=					vala

        A_								=					sd_megatron[f'{pf}{i}.self_attention.dense.weight'].transpose(
            0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.self_attention.dense.bias']
        A_								=					sd_megatron[f'{pf}{i}.post_attention_layernorm.weight']
        A_								=					sd_megatron[f'{pf}{i}.post_attention_layernorm.bias']
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.bias']
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    A_								=					word_embeddings

    return sd_hf





def        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      List[Any] )->			Dict:
    A_								=					64000
    A_								=					config_megatron["encoder_seq_length"]
    A_								=					config_megatron["hidden_size"]
    A_								=					config_megatron["num_layers"]
    A_								=					config_megatron["num_attention_heads"]
    A_								=					config_megatron["ffn_hidden_size"]
    A_								=					"gelu"
    A_								=					0.1
    A_								=					0.1
    A_								=					0.1
    A_								=					config_megatron["layernorm_epsilon"]  # 1e-5
    A_								=					config_megatron["init_method_std"]  # 0.02
    A_								=					config_megatron["apply_query_key_layer_scaling"]  # True
    A_								=					True
    A_								=					True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4096:
        A_								=					1  # <|endoftext|>
        A_								=					1  # <|endoftext|>
        A_								=					0  # <unk>
    else:
        A_								=					2  # <s>
        A_								=					3  # <|endoftext|>
        A_								=					0  # <pad>

    return config_hf





def        lowerCAmelCase       (				snake_case__   :      Tuple )->			Dict:
    print(snake_case__ )

    A_								=					args.checkpoint_path
    A_								=					args.save_path
    if isfile(snake_case__ ):
        raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}' )

    # Load the model.
    A_								=					torch.load(snake_case__				,					map_location="cpu" )

    # Load the config.
    A_								=					checkpoint["hyper_parameters"]["cfg"]
    A_								=					GPTaConfig()
    A_								=					copy_config(config_hf=snake_case__				,					config_megatron=snake_case__ )
    A_								=					["GPT2LMHeadModel"]

    A_								=					checkpoint["state_dict"]

    # Convert.
    print("Converting" )
    A_								=					convert_megatron_checkpoint(snake_case__				,					snake_case__ )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(snake_case__				,					snake_case__ )

    A_								=					"GPTSw3Tokenizer"

    # Store the config to file.
    print("Saving config" )
    config_hf.save_pretrained(snake_case__ )

    # Store the state_dict to file.
    A_								=					os.path.join(snake_case__				,					"pytorch_model.bin" )
    print(f'Saving checkpoint to "{output_checkpoint_file}"' )
    torch.save(snake_case__				,					snake_case__ )


if __name__ == "__main__":
     __magic_name__  :  Union[str, Any]     			=   argparse.ArgumentParser()
     parser.add_argument(
         '--checkpoint_path',
         type=str,
         required=True,
         help='e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000',
     )
     parser.add_argument('--save_path', type=str, required=True, help='e.g. /home/user/gpt-sw3/hf')
     parser.add_argument('--print-checkpoint-structure', action='store_true')
     __magic_name__  :  str     			=   parser.parse_args()
     main(_args)






