





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()
snake_case__									=				logging.get_logger(__name__)





def lowerCAmelCase__(__snake_case							)    ->				Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__        =		MobileNetVaConfig(layer_norm_eps=0.0_0_1							)

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

	lowerCamelCase__        =		re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''	,__snake_case							)
	if matches:
		lowerCamelCase__        =		float(matches[1]							)
		lowerCamelCase__        =		int(matches[2]							)

	if model_name.startswith('''deeplabv3_'''							):
		lowerCamelCase__        =		8
		lowerCamelCase__        =		21
		lowerCamelCase__        =		"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".
		lowerCamelCase__        =		1001
		lowerCamelCase__        =		"imagenet-1k-id2label.json"

	lowerCamelCase__        =		"huggingface/label-files"
	lowerCamelCase__        =		json.load(open(hf_hub_download(__snake_case	,__snake_case	,repo_type='''dataset'''							)	,'''r'''							)							)

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

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

	return config





def lowerCAmelCase__()    ->				List[Any]:







	'''simple docstring'''







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





@torch.no_grad()
def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case	,__snake_case=False							)    ->				Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__        =		get_mobilenet_va_config(__snake_case							)

	# Load 🤗 model
	if model_name.startswith('''deeplabv3_'''							):
		lowerCamelCase__        =		MobileNetVaForSemanticSegmentation(__snake_case							).eval()
	else:
		lowerCamelCase__        =		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
	lowerCamelCase__        =		MobileNetVaImageProcessor(
	    crop_size={'''width''': config.image_size, '''height''': config.image_size}	,size={'''shortest_edge''': config.image_size + 32}	,)
	lowerCamelCase__        =		image_processor(images=prepare_img()	,return_tensors='''pt'''							)
	lowerCamelCase__        =		model(**__snake_case							)
	lowerCamelCase__        =		outputs.logits

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

		if model_name == "deeplabv3_mobilenet_v2_1.0_513":
			lowerCamelCase__        =		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":
			lowerCamelCase__        =		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":
			lowerCamelCase__        =		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":
			lowerCamelCase__        =		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":
			lowerCamelCase__        =		torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]							)
		else:
			lowerCamelCase__        =		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...'''							)
		lowerCamelCase__        =		"google/" + model_name
		image_processor.push_to_hub(__snake_case							)
		model.push_to_hub(__snake_case							)


if __name__ == "__main__":
			snake_case__									=				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."
			)

			snake_case__									=				parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a									=				logging.get_logger(__name__)

_a									=				{
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""pegasus_x"""
			lowerCAmelCase_												=						["""past_key_values"""]
			lowerCAmelCase_												=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





			def __init__(   self	,  __lowerCAmelCase=9_6_1_0_3	,  __lowerCAmelCase=1_6_3_8_4	,  __lowerCAmelCase=1_6	,  __lowerCAmelCase=4_0_9_6	,  __lowerCAmelCase=1_6	,  __lowerCAmelCase=1_6	,  __lowerCAmelCase=4_0_9_6	,  __lowerCAmelCase=1_6	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=True	,  __lowerCAmelCase=True	,  __lowerCAmelCase="gelu"	,  __lowerCAmelCase=1_0_2_4	,  __lowerCAmelCase=0.1	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.02	,  __lowerCAmelCase=0	,  __lowerCAmelCase=True	,  __lowerCAmelCase=0	,  __lowerCAmelCase=1	,  __lowerCAmelCase=1	,  __lowerCAmelCase=3_2	,  __lowerCAmelCase=5_1_2	,  __lowerCAmelCase=True	,  **__lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		vocab_size
				lowerCamelCase__        =		max_position_embeddings
				lowerCamelCase__        =		d_model
				lowerCamelCase__        =		encoder_ffn_dim
				lowerCamelCase__        =		encoder_layers
				lowerCamelCase__        =		encoder_attention_heads
				lowerCamelCase__        =		decoder_ffn_dim
				lowerCamelCase__        =		decoder_layers
				lowerCamelCase__        =		decoder_attention_heads
				lowerCamelCase__        =		dropout
				lowerCamelCase__        =		attention_dropout
				lowerCamelCase__        =		activation_dropout
				lowerCamelCase__        =		activation_function
				lowerCamelCase__        =		init_std
				lowerCamelCase__        =		encoder_layerdrop
				lowerCamelCase__        =		decoder_layerdrop
				lowerCamelCase__        =		use_cache
				lowerCamelCase__        =		encoder_layers
				lowerCamelCase__        =		scale_embedding  # scale factor will be sqrt(d_model) if True

				lowerCamelCase__        =		num_global_tokens
				lowerCamelCase__        =		block_size
				lowerCamelCase__        =		stagger_local_blocks

				super().__init__(
				    pad_token_id=__lowerCAmelCase	,  eos_token_id=__lowerCAmelCase	,  is_encoder_decoder=__lowerCAmelCase	,  decoder_start_token_id=__lowerCAmelCase	,  forced_eos_token_id=__lowerCAmelCase	,  **__lowerCAmelCase	,  )





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return self.encoder_attention_heads





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return self.d_model



