




"""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 transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			List[Any]     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					YolosConfig:
			lowerCamelCase			      =					YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						lowerCamelCase			      =					1_92
						lowerCamelCase			      =					7_68
						lowerCamelCase			      =					12
						lowerCamelCase			      =					3
						lowerCamelCase			      =					[8_00, 13_33]
						lowerCamelCase			      =					False
			elif yolos_name == "yolos_s_dWr":
						lowerCamelCase			      =					3_30
						lowerCamelCase			      =					14
						lowerCamelCase			      =					6
						lowerCamelCase			      =					13_20
			elif "yolos_s" in yolos_name:
						lowerCamelCase			      =					3_84
						lowerCamelCase			      =					15_36
						lowerCamelCase			      =					12
						lowerCamelCase			      =					6
			elif "yolos_b" in yolos_name:
						lowerCamelCase			      =					[8_00, 13_44]

			lowerCamelCase			      =					91
			lowerCamelCase			      =					"""huggingface/label-files"""
			lowerCamelCase			      =					"""coco-detection-id2label.json"""
			lowerCamelCase			      =					json.load(open(hf_hub_download(snake_case__       , snake_case__       , repo_type="""dataset"""				)       , """r"""				)				)
			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     a__ (				snake_case__       , snake_case__       , snake_case__ = False				)						->					int:
			for i in range(config.num_hidden_layers				):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowerCamelCase			      =					state_dict.pop(F'blocks.{i}.attn.qkv.weight'				)
						lowerCamelCase			      =					state_dict.pop(F'blocks.{i}.attn.qkv.bias'				)
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase			      =					in_proj_weight[: config.hidden_size, :]
						lowerCamelCase			      =					in_proj_bias[: config.hidden_size]
						lowerCamelCase			      =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase			      =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowerCamelCase			      =					in_proj_weight[-config.hidden_size :, :]
						lowerCamelCase			      =					in_proj_bias[-config.hidden_size :]



def     a__ (				snake_case__				)						->					str:
			if "backbone" in name:
						lowerCamelCase			      =					name.replace("""backbone"""       , """vit"""				)
			if "cls_token" in name:
						lowerCamelCase			      =					name.replace("""cls_token"""       , """embeddings.cls_token"""				)
			if "det_token" in name:
						lowerCamelCase			      =					name.replace("""det_token"""       , """embeddings.detection_tokens"""				)
			if "mid_pos_embed" in name:
						lowerCamelCase			      =					name.replace("""mid_pos_embed"""       , """encoder.mid_position_embeddings"""				)
			if "pos_embed" in name:
						lowerCamelCase			      =					name.replace("""pos_embed"""       , """embeddings.position_embeddings"""				)
			if "patch_embed.proj" in name:
						lowerCamelCase			      =					name.replace("""patch_embed.proj"""       , """embeddings.patch_embeddings.projection"""				)
			if "blocks" in name:
						lowerCamelCase			      =					name.replace("""blocks"""       , """encoder.layer"""				)
			if "attn.proj" in name:
						lowerCamelCase			      =					name.replace("""attn.proj"""       , """attention.output.dense"""				)
			if "attn" in name:
						lowerCamelCase			      =					name.replace("""attn"""       , """attention.self"""				)
			if "norm1" in name:
						lowerCamelCase			      =					name.replace("""norm1"""       , """layernorm_before"""				)
			if "norm2" in name:
						lowerCamelCase			      =					name.replace("""norm2"""       , """layernorm_after"""				)
			if "mlp.fc1" in name:
						lowerCamelCase			      =					name.replace("""mlp.fc1"""       , """intermediate.dense"""				)
			if "mlp.fc2" in name:
						lowerCamelCase			      =					name.replace("""mlp.fc2"""       , """output.dense"""				)
			if "class_embed" in name:
						lowerCamelCase			      =					name.replace("""class_embed"""       , """class_labels_classifier"""				)
			if "bbox_embed" in name:
						lowerCamelCase			      =					name.replace("""bbox_embed"""       , """bbox_predictor"""				)
			if "vit.norm" in name:
						lowerCamelCase			      =					name.replace("""vit.norm"""       , """vit.layernorm"""				)

			return name



def     a__ (				snake_case__       , snake_case__				)						->					dict:
			for key in orig_state_dict.copy().keys():
						lowerCamelCase			      =					orig_state_dict.pop(snake_case__				)

						if "qkv" in key:
									lowerCamelCase			      =					key.split("""."""				)
									lowerCamelCase			      =					int(key_split[2]				)
									lowerCamelCase			      =					model.vit.encoder.layer[layer_num].attention.attention.all_head_size
									if "weight" in key:
												lowerCamelCase			      =					val[:dim, :]
												lowerCamelCase			      =					val[
												    dim : dim * 2, :
												]
												lowerCamelCase			      =					val[-dim:, :]
									else:
												lowerCamelCase			      =					val[:dim]
												lowerCamelCase			      =					val[dim : dim * 2]
												lowerCamelCase			      =					val[-dim:]
						else:
									lowerCamelCase			      =					val

			return orig_state_dict



def     a__ (				)						->					torch.Tensor:
			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     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__ = False				)						->					Optional[int]:
			lowerCamelCase			      =					get_yolos_config(snake_case__				)

			# load original state_dict
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# load 🤗 model
			lowerCamelCase			      =					YolosForObjectDetection(snake_case__				)
			model.eval()
			lowerCamelCase			      =					convert_state_dict(snake_case__       , snake_case__				)
			model.load_state_dict(snake_case__				)

			# Check outputs on an image, prepared by YolosImageProcessor
			lowerCamelCase			      =					8_00 if yolos_name != """yolos_ti""" else 5_12
			lowerCamelCase			      =					YolosImageProcessor(format="""coco_detection"""       , size=snake_case__				)
			lowerCamelCase			      =					image_processor(images=prepare_img()       , return_tensors="""pt"""				)
			lowerCamelCase			      =					model(**snake_case__				)
			lowerCamelCase      ,   lowerCamelCase			      =					outputs.logits, outputs.pred_boxes

			lowerCamelCase      ,   lowerCamelCase			      =					None, None
			if yolos_name == "yolos_ti":
						lowerCamelCase			      =					torch.tensor(
						    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
						lowerCamelCase			      =					torch.tensor(
						    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]				)
			elif yolos_name == "yolos_s_200_pre":
						lowerCamelCase			      =					torch.tensor(
						    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
						lowerCamelCase			      =					torch.tensor(
						    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]				)
			elif yolos_name == "yolos_s_300_pre":
						lowerCamelCase			      =					torch.tensor(
						    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
						lowerCamelCase			      =					torch.tensor(
						    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]				)
			elif yolos_name == "yolos_s_dWr":
						lowerCamelCase			      =					torch.tensor(
						    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
						lowerCamelCase			      =					torch.tensor(
						    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]				)
			elif yolos_name == "yolos_base":
						lowerCamelCase			      =					torch.tensor(
						    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
						lowerCamelCase			      =					torch.tensor(
						    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]				)
			else:
						raise ValueError(F'Unknown yolos_name: {yolos_name}'				)

			assert torch.allclose(logits[0, :3, :3]       , snake_case__       , atol=1E-4				)
			assert torch.allclose(pred_boxes[0, :3, :3]       , snake_case__       , atol=1E-4				)

			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			print(F'Saving model {yolos_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:
						lowerCamelCase			      =					{
						    """yolos_ti""": """yolos-tiny""",
						    """yolos_s_200_pre""": """yolos-small""",
						    """yolos_s_300_pre""": """yolos-small-300""",
						    """yolos_s_dWr""": """yolos-small-dwr""",
						    """yolos_base""": """yolos-base""",
						}

						print("""Pushing to the hub..."""				)
						lowerCamelCase			      =					model_mapping[yolos_name]
						image_processor.push_to_hub(snake_case__       , organization="""hustvl"""				)
						model.push_to_hub(snake_case__       , organization="""hustvl"""				)


if __name__ == "__main__":
					lowerCAmelCase							:			Optional[Any]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--yolos_name""",
					    default="""yolos_s_200_pre""",
					    type=str,
					    help=(
					        """Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
					        """ 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
					    ),
					)
					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
					)
					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 or not to push the converted model to the 🤗 hub."""
					)

					lowerCAmelCase							:			int     							=							parser.parse_args()
					convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__="attention"				)						->					List[Any]:
			lowerCamelCase			      =					lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					k_tmp.reshape(k_tmp.shape[0]       , k_tmp.shape[1] * k_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]       , o_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					q_tmp.reshape(q_tmp.shape[0]       , q_tmp.shape[1] * q_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					v_tmp.reshape(v_tmp.shape[0]       , v_tmp.shape[1] * v_tmp.shape[2]				)
			return k, o, q, v



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__=False				)						->					List[str]:
			if split_mlp_wi:
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
						lowerCamelCase			      =					(wi_a, wi_a)
			else:
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

			lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
			return wi, wo



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					Tuple:
			return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def     a__ (				snake_case__       , *, snake_case__       , snake_case__       , snake_case__ = False				)						->					Dict:
			lowerCamelCase			      =					traverse_util.flatten_dict(variables["""target"""]				)
			lowerCamelCase			      =					{"""/""".join(snake_case__				): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			lowerCamelCase			      =					"""encoder/encoder/mlp/wi_0/kernel""" in old
			print("""Split MLP:"""       , snake_case__				)

			lowerCamelCase			      =					collections.OrderedDict()

			# Shared embeddings.
			lowerCamelCase			      =					old["""token_embedder/embedding"""]

			# Encoder.
			for i in range(snake_case__				):
						# Block i, layer 0 (Self Attention).
						lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """encoder"""       , """pre_attention_layer_norm"""				)
						lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """encoder"""       , """attention"""				)
						lowerCamelCase			      =					layer_norm
						lowerCamelCase			      =					k.T
						lowerCamelCase			      =					o.T
						lowerCamelCase			      =					q.T
						lowerCamelCase			      =					v.T

						# Block i, layer 1 (MLP).
						lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """encoder"""       , """pre_mlp_layer_norm"""				)
						lowerCamelCase      ,   lowerCamelCase			      =					tax_mlp_lookup(snake_case__       , snake_case__       , """encoder"""       , snake_case__				)
						lowerCamelCase			      =					layer_norm
						if split_mlp_wi:
									lowerCamelCase			      =					wi[0].T
									lowerCamelCase			      =					wi[1].T
						else:
									lowerCamelCase			      =					wi.T
						lowerCamelCase			      =					wo.T
						if scalable_attention:
									# convert the rel_embedding of each layer
									lowerCamelCase			      =					tax_relpos_bias_lookup(
									    snake_case__       , snake_case__       , """encoder"""				).T

			lowerCamelCase			      =					old["""encoder/encoder_norm/scale"""]

			if not scalable_attention:
						lowerCamelCase			      =					tax_relpos_bias_lookup(
						    snake_case__       , 0       , """encoder"""				).T
						lowerCamelCase			      =					tax_relpos_bias_lookup(
						    snake_case__       , 0       , """decoder"""				).T

			if not is_encoder_only:
						# Decoder.
						for i in range(snake_case__				):
									# Block i, layer 0 (Self Attention).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_self_attention_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """decoder"""       , """self_attention"""				)
									lowerCamelCase			      =					layer_norm
									lowerCamelCase			      =					k.T
									lowerCamelCase			      =					o.T
									lowerCamelCase			      =					q.T
									lowerCamelCase			      =					v.T

									# Block i, layer 1 (Cross Attention).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_cross_attention_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """decoder"""       , """encoder_decoder_attention"""				)
									lowerCamelCase			      =					layer_norm
									lowerCamelCase			      =					k.T
									lowerCamelCase			      =					o.T
									lowerCamelCase			      =					q.T
									lowerCamelCase			      =					v.T

									# Block i, layer 2 (MLP).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_mlp_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase			      =					tax_mlp_lookup(snake_case__       , snake_case__       , """decoder"""       , snake_case__				)
									lowerCamelCase			      =					layer_norm
									if split_mlp_wi:
												lowerCamelCase			      =					wi[0].T
												lowerCamelCase			      =					wi[1].T
									else:
												lowerCamelCase			      =					wi.T
									lowerCamelCase			      =					wo.T

									if scalable_attention:
												# convert the rel_embedding of each layer
												lowerCamelCase			      =					tax_relpos_bias_lookup(snake_case__       , snake_case__       , """decoder"""				).T

						lowerCamelCase			      =					old["""decoder/decoder_norm/scale"""]

						# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
						if "decoder/logits_dense/kernel" in old:
									lowerCamelCase			      =					old["""decoder/logits_dense/kernel"""].T

			return new



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

			# Add what is missing.
			if "encoder.embed_tokens.weight" not in state_dict:
						lowerCamelCase			      =					state_dict["""shared.weight"""]

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCamelCase			      =					state_dict["""shared.weight"""]

						if "lm_head.weight" not in state_dict:  # For old 1.0 models.
									print("""Using shared word embeddings as lm_head."""				)
									lowerCamelCase			      =					state_dict["""shared.weight"""]

			return state_dict



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					checkpoints.load_tax_checkpoint(snake_case__				)
			lowerCamelCase			      =					convert_tax_to_pytorch(
			    snake_case__       , num_layers=config.num_layers       , is_encoder_only=snake_case__       , scalable_attention=snake_case__				)
			lowerCamelCase			      =					make_state_dict(snake_case__       , snake_case__				)
			model.load_state_dict(snake_case__       , strict=snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__ = False       , snake_case__ = False       , )						->					str:
			lowerCamelCase			      =					MTaConfig.from_json_file(snake_case__				)
			print(F'Building PyTorch model from configuration: {config}'				)
			# Non-v1.1 checkpoints could also use T5Model, but this works for all.
			# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
			if is_encoder_only:
						lowerCamelCase			      =					UMTaEncoderModel(snake_case__				)
			else:
						lowerCamelCase			      =					UMTaForConditionalGeneration(snake_case__				)

			# Load weights from tf checkpoint
			load_tax_weights_in_ta(snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'				)
			model.save_pretrained(snake_case__				)

			# Verify that we can load the checkpoint.
			model.from_pretrained(snake_case__				)
			print("""Done"""				)


if __name__ == "__main__":
					lowerCAmelCase							:			Optional[int]     							=							argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
					# Required parameters
					parser.add_argument(
					    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
					)
					parser.add_argument(
					    """--scalable_attention""",
					    action="""store_true""",
					    help="""Whether the model uses scaled attention (umt5 model)""",
					    default=False,
					)
					lowerCAmelCase							:			int     							=							parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path,
					    args.config_file,
					    args.pytorch_dump_path,
					    args.is_encoder_only,
					    args.scalable_attention,
					)



