
import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
			from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=None	, ):
				if attention_mask is None:
								lowerCamelCase_						=			input_ids.ne(config.pad_token_id    )
				if decoder_attention_mask is None:
								lowerCamelCase_						=			decoder_input_ids.ne(config.pad_token_id    )
				if head_mask is None:
								lowerCamelCase_						=			torch.ones(config.encoder_layers	, config.encoder_attention_heads	, device=lowerCamelCase__    )
				if decoder_head_mask is None:
								lowerCamelCase_						=			torch.ones(config.decoder_layers	, config.decoder_attention_heads	, device=lowerCamelCase__    )
				if cross_attn_head_mask is None:
								lowerCamelCase_						=			torch.ones(config.decoder_layers	, config.decoder_attention_heads	, device=lowerCamelCase__    )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase=13						,  lowercase=7						,  lowercase=True						,  lowercase=False						,  lowercase=99						,  lowercase=16						,  lowercase=2						,  lowercase=4						,  lowercase=4						,  lowercase="relu"						,  lowercase=0.1						,  lowercase=0.1						,  lowercase=0.0						,  lowercase=0.0						,  lowercase=20						,  lowercase=2						,  lowercase=1						,  lowercase=0						,  )				->       int:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			seq_length
					lowerCamelCase_						=			is_training
					lowerCamelCase_						=			use_labels
					lowerCamelCase_						=			vocab_size
					lowerCamelCase_						=			hidden_size
					lowerCamelCase_						=			num_hidden_layers
					lowerCamelCase_						=			num_attention_heads
					lowerCamelCase_						=			intermediate_size
					lowerCamelCase_						=			hidden_act
					lowerCamelCase_						=			hidden_dropout_prob
					lowerCamelCase_						=			attention_probs_dropout_prob
					lowerCamelCase_						=			encoder_layerdrop
					lowerCamelCase_						=			decoder_layerdrop
					lowerCamelCase_						=			max_position_embeddings
					lowerCamelCase_						=			eos_token_id
					lowerCamelCase_						=			pad_token_id
					lowerCamelCase_						=			bos_token_id



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )
					lowerCamelCase_						=			self.eos_token_id  # Eos Token
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )

					# we need to clamp the input ids here to avoid having pad token in between
					# this is because for M2M100 the position_ids are prepared such that
					# all pad tokens have pos id = 2 and rest are between 2..seq_length
					# and the seq_length here is seq_length - num_pad_tokens
					# but when using past, there is no way of knowing if the past input ids had
					# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
					# position_ids being off by num_pad_tokens in past input
					lowerCamelCase_						=			input_ids.clamp(self.pad_token_id + 1 )
					lowerCamelCase_						=			decoder_input_ids.clamp(self.pad_token_id + 1 )

					lowerCamelCase_						=			self.get_config()
					lowerCamelCase_						=			prepare_mam_aaa_inputs_dict(lowercase						,  lowercase						,  lowercase )
					return config, inputs_dict



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					return MaMaaaConfig(
					    vocab_size=self.vocab_size						,  d_model=self.hidden_size						,  encoder_layers=self.num_hidden_layers						,  decoder_layers=self.num_hidden_layers						,  encoder_attention_heads=self.num_attention_heads						,  decoder_attention_heads=self.num_attention_heads						,  encoder_ffn_dim=self.intermediate_size						,  decoder_ffn_dim=self.intermediate_size						,  dropout=self.hidden_dropout_prob						,  attention_dropout=self.attention_probs_dropout_prob						,  encoder_layerdrop=self.encoder_layerdrop						,  decoder_layerdrop=self.decoder_layerdrop						,  max_position_embeddings=self.max_position_embeddings						,  eos_token_id=self.eos_token_id						,  bos_token_id=self.bos_token_id						,  pad_token_id=self.pad_token_id						,  )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_	,    lowerCamelCase_						=			self.prepare_config_and_inputs()
					return config, inputs_dict



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			MaMaaaModel(config=lowercase ).get_decoder().to(lowercase ).eval()
					lowerCamelCase_						=			inputs_dict["input_ids"]
					lowerCamelCase_						=			inputs_dict["attention_mask"]
					lowerCamelCase_						=			inputs_dict["head_mask"]

					# first forward pass
					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase						,  head_mask=lowercase						,  use_cache=lowercase )

					lowerCamelCase_	,    lowerCamelCase_						=			outputs.to_tuple()

					# create hypothetical multiple next token and extent to next_input_ids
					lowerCamelCase_						=			ids_tensor((self.batch_size, 3)						,  config.vocab_size )
					lowerCamelCase_						=			ids_tensor((self.batch_size, 3)						,  2 )

					# append to next input_ids and
					lowerCamelCase_						=			torch.cat([input_ids, next_tokens]						,  dim=-1 )
					lowerCamelCase_						=			torch.cat([attention_mask, next_attn_mask]						,  dim=-1 )

					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase )["last_hidden_state"]
					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase						,  past_key_values=lowercase )[
					    "last_hidden_state"
					]

					# select random slice
					lowerCamelCase_						=			ids_tensor((1,)						,  output_from_past.shape[-1] ).item()
					lowerCamelCase_						=			output_from_no_past[:, -3:, random_slice_idx].detach()
					lowerCamelCase_						=			output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(lowercase						,  lowercase						,  atol=1e-2 ) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			MaMaaaModel(config=lowercase ).to(lowercase ).eval()
					lowerCamelCase_						=			model(**lowercase )

					lowerCamelCase_						=			outputs.encoder_last_hidden_state
					lowerCamelCase_						=			outputs.last_hidden_state

					with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_						=			model.get_encoder()
									encoder.save_pretrained(lowercase )
									lowerCamelCase_						=			MaMaaaEncoder.from_pretrained(lowercase ).to(lowercase )

					lowerCamelCase_						=			encoder(inputs_dict["input_ids"]						,  attention_mask=inputs_dict["attention_mask"] )[
					    0
					]

					self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )

					with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_						=			model.get_decoder()
									decoder.save_pretrained(lowercase )
									lowerCamelCase_						=			MaMaaaDecoder.from_pretrained(lowercase ).to(lowercase )

					lowerCamelCase_						=			decoder(
					    input_ids=inputs_dict["decoder_input_ids"]						,  attention_mask=inputs_dict["decoder_attention_mask"]						,  encoder_hidden_states=lowercase						,  encoder_attention_mask=inputs_dict["attention_mask"]						,  )[0]

					self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )




@require_torch
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__         = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	lowerCAmelCase__         = (
	    {
	        'conversational': MaMaaaForConditionalGeneration,
	        'feature-extraction': MaMaaaModel,
	        'summarization': MaMaaaForConditionalGeneration,
	        'text2text-generation': MaMaaaForConditionalGeneration,
	        'translation': MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase__         = True
	lowerCAmelCase__         = True
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					if pipeline_test_casse_name == "TranslationPipelineTests":
									# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
									# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
									return True

					return False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			MaMaaaModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					self.config_tester.run_common_tests()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
									lowerCamelCase_						=			model_class(lowercase )

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowercase )
													lowerCamelCase_	,    lowerCamelCase_						=			model_class.from_pretrained(lowercase						,  output_loading_info=lowercase )
									self.assertEqual(info["missing_keys"]						,  [] )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_encoder_decoder_model_standalone(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
									lowerCamelCase_						=			model_class(lowercase )
									model.to(lowercase )
									model.eval()

									lowerCamelCase_						=			copy.deepcopy(self._prepare_for_class(lowercase						,  lowercase ) )

									if not self.is_encoder_decoder:
													lowerCamelCase_						=			inputs["input_ids"]
													del inputs["input_ids"]
									else:
													lowerCamelCase_						=			inputs["input_ids"]
													lowerCamelCase_						=			inputs.get("decoder_input_ids"						,  lowercase )
													del inputs["input_ids"]
													inputs.pop("decoder_input_ids"						,  lowercase )

									lowerCamelCase_						=			model.get_input_embeddings()
									if not self.is_encoder_decoder:
													lowerCamelCase_						=			wte(lowercase )
									else:
													lowerCamelCase_						=			wte(lowercase )
													lowerCamelCase_						=			wte(lowercase )

									with torch.no_grad():
													model(**lowercase )[0]



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					lowerCamelCase_						=			input_dict["input_ids"]
					lowerCamelCase_						=			input_ids.ne(1 ).to(lowercase )
					lowerCamelCase_						=			MaMaaaForConditionalGeneration(lowercase ).eval().to(lowercase )
					if torch_device == "cuda":
									model.half()
					model.generate(lowercase						,  attention_mask=lowercase )
					model.generate(num_beams=4						,  do_sample=lowercase						,  early_stopping=lowercase						,  num_return_sequences=3 )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return torch.tensor(lowerCamelCase__	, dtype=torch.long	, device=lowerCamelCase__    )


__A				=1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@cached_property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(lowercase )
					lowerCamelCase_						=			_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
					lowerCamelCase_						=			_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
					lowerCamelCase_						=			prepare_mam_aaa_inputs_dict(model.config						,  lowercase						,  lowercase )
					with torch.no_grad():
									lowerCamelCase_						=			model(**lowercase )[0]
					lowerCamelCase_						=			torch.Size((1, 11, 1024) )
					self.assertEqual(output.shape						,  lowercase )
					# change to expected output here
					lowerCamelCase_						=			torch.tensor(
					    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]						,  device=lowercase )
					self.assertTrue(torch.allclose(output[:, :3, :3]						,  lowercase						,  atol=lowercase ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(lowercase )

					# change to intended input
					lowerCamelCase_						=			_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
					lowerCamelCase_						=			_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
					lowerCamelCase_						=			prepare_mam_aaa_inputs_dict(model.config						,  lowercase						,  lowercase )
					with torch.no_grad():
									lowerCamelCase_						=			model(**lowercase )[0]
					lowerCamelCase_						=			torch.Size((1, 11, model.config.vocab_size) )
					self.assertEqual(output.shape						,  lowercase )
					# change to expected output here
					lowerCamelCase_						=			torch.tensor(
					    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]						,  device=lowercase )
					self.assertTrue(torch.allclose(output[:, :3, :3]						,  lowercase						,  atol=lowercase ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(lowercase )
					lowerCamelCase_						=			MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"						,  src_lang="fr"						,  tgt_lang="en" )

					lowerCamelCase_						=			[
					    "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
					    "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
					    "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
					    " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
					    " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
					]

					# The below article tests that we don't add any hypotheses outside of the top n_beams
					lowerCamelCase_						=			tokenizer(lowercase						,  padding=lowercase						,  return_tensors="pt" )

					lowerCamelCase_						=			model.generate(
					    input_ids=dct["input_ids"].to(lowercase )						,  attention_mask=dct["attention_mask"].to(lowercase )						,  num_beams=5						,  forced_bos_token_id=tokenizer.get_lang_id("en" )						,  )

					lowerCamelCase_						=			[
					    "The NSA case highlights the total absence of intelligence debate",
					    "I think there are two levels of response from the French government.",
					    "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
					    " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
					    " communications in France.",
					]

					lowerCamelCase_						=			tokenizer.batch_decode(
					    hypotheses_batch.tolist()						,  clean_up_tokenization_spaces=lowercase						,  skip_special_tokens=lowercase )
					assert generated == expected_en




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()
__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								lowerCamelCase_						=			1_9_2
								lowerCamelCase_						=			7_6_8
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			3
								lowerCamelCase_						=			[8_0_0, 1_3_3_3]
								lowerCamelCase_						=			False
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			3_3_0
								lowerCamelCase_						=			1_4
								lowerCamelCase_						=			6
								lowerCamelCase_						=			1_3_2_0
				elif "yolos_s" in yolos_name:
								lowerCamelCase_						=			3_8_4
								lowerCamelCase_						=			1_5_3_6
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			6
				elif "yolos_b" in yolos_name:
								lowerCamelCase_						=			[8_0_0, 1_3_4_4]

				lowerCamelCase_						=			9_1
				lowerCamelCase_						=			"huggingface/label-files"
				lowerCamelCase_						=			"coco-detection-id2label.json"
				lowerCamelCase_						=			json.load(open(hf_hub_download(lowerCamelCase__	, lowerCamelCase__	, repo_type="dataset"    )	, "r"    )    )
				lowerCamelCase_						=			{int(lowerCamelCase__    ): v for k, v in idalabel.items()}
				lowerCamelCase_						=			idalabel
				lowerCamelCase_						=			{v: k for k, v in idalabel.items()}

				return config





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				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 			lowerCamelCase_ (				lowerCamelCase__    ):
				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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

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





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				lowerCamelCase_						=			get_yolos_config(lowerCamelCase__    )

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

				# load 🤗 model
				lowerCamelCase_						=			YolosForObjectDetection(lowerCamelCase__    )
				model.eval()
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				model.load_state_dict(lowerCamelCase__    )

				# Check outputs on an image, prepared by YolosImageProcessor
				lowerCamelCase_						=			8_0_0 if yolos_name != "yolos_ti" else 5_1_2
				lowerCamelCase_						=			YolosImageProcessor(format="coco_detection"	, size=lowerCamelCase__    )
				lowerCamelCase_						=			image_processor(images=prepare_img()	, return_tensors="pt"    )
				lowerCamelCase_						=			model(**lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			outputs.logits, outputs.pred_boxes

				lowerCamelCase_	,    lowerCamelCase_						=			None, None
				if yolos_name == "yolos_ti":
								lowerCamelCase_						=			torch.tensor(
								    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
				elif yolos_name == "yolos_s_200_pre":
								lowerCamelCase_						=			torch.tensor(
								    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
				elif yolos_name == "yolos_s_300_pre":
								lowerCamelCase_						=			torch.tensor(
								    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			torch.tensor(
								    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
				elif yolos_name == "yolos_base":
								lowerCamelCase_						=			torch.tensor(
								    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
				else:
								raise ValueError(F'Unknown yolos_name: {yolos_name}'    )

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

				Path(lowerCamelCase__    ).mkdir(exist_ok=lowerCamelCase__    )
				print(F'Saving model {yolos_name} to {pytorch_dump_folder_path}'    )
				model.save_pretrained(lowerCamelCase__    )
				print(F'Saving image processor to {pytorch_dump_folder_path}'    )
				image_processor.save_pretrained(lowerCamelCase__    )

				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(lowerCamelCase__	, organization="hustvl"    )
								model.push_to_hub(lowerCamelCase__	, organization="hustvl"    )


if __name__ == "__main__":
			__A				=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.'''
			)

			__A				=parser.parse_args()
			convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


