

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()
lowercase_									=   logging.get_logger(__name__)
def      a      (			A__	:   Tuple     )    ->      Any:





      """simple docstring"""



      _lowercase	       =YolosConfig()

      # size of the architecture
      if "yolos_ti" in yolos_name:
            _lowercase	       =192
            _lowercase	       =768
            _lowercase	       =12
            _lowercase	       =3
            _lowercase	       =[800, 1333]
            _lowercase	       =False
      elif yolos_name == "yolos_s_dWr":
            _lowercase	       =330
            _lowercase	       =14
            _lowercase	       =6
            _lowercase	       =1320
      elif "yolos_s" in yolos_name:
            _lowercase	       =384
            _lowercase	       =1536
            _lowercase	       =12
            _lowercase	       =6
      elif "yolos_b" in yolos_name:
            _lowercase	       =[800, 1344]

      _lowercase	       =91
      _lowercase	       ='huggingface/label-files'
      _lowercase	       ='coco-detection-id2label.json'
      _lowercase	       =json.load(open(hf_hub_download(lowerCamelCase__      ,   lowerCamelCase__      ,   repo_type='dataset'     )      ,   'r'     )     )
      _lowercase	       ={int(lowerCamelCase__     ): v for k, v in idalabel.items()}
      _lowercase	       =idalabel
      _lowercase	       ={v: k for k, v in idalabel.items()}

      return config
def      a      (			A__	:   List[str]      ,   A__	:   Dict      ,   A__	:   Optional[Any] = False     )    ->      List[str]:





      """simple docstring"""



      for i in range(config.num_hidden_layers     ):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowercase	       =state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''     )
            _lowercase	       =state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''     )
            # next, add query, keys and values (in that order) to the state dict
            _lowercase	       =in_proj_weight[: config.hidden_size, :]
            _lowercase	       =in_proj_bias[: config.hidden_size]
            _lowercase	       =in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowercase	       =in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowercase	       =in_proj_weight[-config.hidden_size :, :]
            _lowercase	       =in_proj_bias[-config.hidden_size :]
def      a      (			A__	:   List[str]     )    ->      int:





      """simple docstring"""



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

      return name
def      a      (			A__	:   Tuple      ,   A__	:   Optional[Any]     )    ->      List[str]:





      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            _lowercase	       =orig_state_dict.pop(lowerCamelCase__     )

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

      return orig_state_dict
def      a      (			)    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       ='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase	       =Image.open(requests.get(lowerCamelCase__      ,   stream=lowerCamelCase__     ).raw     )
      return im
@torch.no_grad()
def      a      (			A__	:   Dict      ,   A__	:   List[str]      ,   A__	:   List[str]      ,   A__	:   Optional[int] = False     )    ->      List[str]:





      """simple docstring"""



      _lowercase	       =get_yolos_config(lowerCamelCase__     )

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

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

      # Check outputs on an image, prepared by YolosImageProcessor
      _lowercase	       =800 if yolos_name != 'yolos_ti' else 512
      _lowercase	       =YolosImageProcessor(format='coco_detection'      ,   size=lowerCamelCase__     )
      _lowercase	       =image_processor(images=prepare_img()      ,   return_tensors='pt'     )
      _lowercase	       =model(**lowerCamelCase__     )
      _lowercase       ,       _lowercase	       =outputs.logits, outputs.pred_boxes

      _lowercase       ,       _lowercase	       =None, None
      if yolos_name == "yolos_ti":
            _lowercase	       =torch.tensor(
                [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]     )
            _lowercase	       =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":
            _lowercase	       =torch.tensor(
                [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]     )
            _lowercase	       =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":
            _lowercase	       =torch.tensor(
                [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]     )
            _lowercase	       =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":
            _lowercase	       =torch.tensor(
                [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]     )
            _lowercase	       =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":
            _lowercase	       =torch.tensor(
                [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]     )
            _lowercase	       =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]      ,   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:
            _lowercase	       ={
                '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...'     )
            _lowercase	       =model_mapping[yolos_name]
            image_processor.push_to_hub(lowerCamelCase__      ,   organization='hustvl'     )
            model.push_to_hub(lowerCamelCase__      ,   organization='hustvl'     )


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

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




from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import numpy
			import tensorflow as tf

			from transformers.models.esm.modeling_tf_esm import (
			    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFEsmForMaskedLM,
			    TFEsmForSequenceClassification,
			    TFEsmForTokenClassification,
			    TFEsmModel,
			)




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  )				->       Optional[int]:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			13
					lowerCamelCase_						=			7
					lowerCamelCase_						=			True
					lowerCamelCase_						=			True
					lowerCamelCase_						=			True
					lowerCamelCase_						=			99
					lowerCamelCase_						=			32
					lowerCamelCase_						=			2
					lowerCamelCase_						=			4
					lowerCamelCase_						=			37
					lowerCamelCase_						=			"gelu"
					lowerCamelCase_						=			0.1
					lowerCamelCase_						=			0.1
					lowerCamelCase_						=			512
					lowerCamelCase_						=			16
					lowerCamelCase_						=			2
					lowerCamelCase_						=			0.0_2
					lowerCamelCase_						=			3
					lowerCamelCase_						=			4
					lowerCamelCase_						=			None



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )

					lowerCamelCase_						=			None
					if self.use_input_mask:
									lowerCamelCase_						=			random_attention_mask([self.batch_size, self.seq_length] )

					lowerCamelCase_						=			None
					lowerCamelCase_						=			None
					lowerCamelCase_						=			None
					if self.use_labels:
									lowerCamelCase_						=			ids_tensor([self.batch_size]						,  self.type_sequence_label_size )
									lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.num_labels )
									lowerCamelCase_						=			ids_tensor([self.batch_size]						,  self.num_choices )

					lowerCamelCase_						=			EsmConfig(
					    vocab_size=self.vocab_size						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  pad_token_id=1						,  num_attention_heads=self.num_attention_heads						,  intermediate_size=self.intermediate_size						,  hidden_act=self.hidden_act						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  max_position_embeddings=self.max_position_embeddings						,  type_vocab_size=self.type_vocab_size						,  initializer_range=self.initializer_range						,  )

					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			self.prepare_config_and_inputs()

					lowerCamelCase_						=			True
					lowerCamelCase_						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  vocab_size=2 )

					return (
					    config,
					    input_ids,
					    input_mask,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			TFEsmModel(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			[input_ids, input_mask]
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			model(lowercase )

					self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       Tuple:
					lowerCamelCase_						=			True

					lowerCamelCase_						=			TFEsmModel(config=lowercase )
					lowerCamelCase_						=			{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "encoder_hidden_states": encoder_hidden_states,
					    "encoder_attention_mask": encoder_attention_mask,
					}
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			[input_ids, input_mask]
					lowerCamelCase_						=			model(lowercase						,  encoder_hidden_states=lowercase )

					# Also check the case where encoder outputs are not passed
					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase )

					self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Optional[int]:
					lowerCamelCase_						=			TFEsmForMaskedLM(config=lowercase )
					lowerCamelCase_						=			model([input_ids, input_mask] )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					lowerCamelCase_						=			self.num_labels
					lowerCamelCase_						=			TFEsmForTokenClassification(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			self.prepare_config_and_inputs()
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			config_and_inputs
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict




@require_tf
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (
	    (
	        TFEsmModel,
	        TFEsmForMaskedLM,
	        TFEsmForSequenceClassification,
	        TFEsmForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase__         = (
	    {
	        'feature-extraction': TFEsmModel,
	        'fill-mask': TFEsmForMaskedLM,
	        'text-classification': TFEsmForSequenceClassification,
	        'token-classification': TFEsmForTokenClassification,
	        'zero-shot': TFEsmForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			TFEsmModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  hidden_size=37 )



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



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*lowercase )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			TFEsmModel.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )



	@unittest.skip("Protein models do not support embedding resizing." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					pass



	@unittest.skip("Protein models do not support embedding resizing." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					pass



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

					for model_class in self.all_model_classes:
									lowerCamelCase_						=			model_class(lowercase )
									assert isinstance(model.get_input_embeddings()						,  tf.keras.layers.Layer )
									if model_class is TFEsmForMaskedLM:
													# Output embedding test differs from the main test because they're a matrix, not a layer
													lowerCamelCase_						=			model.get_bias()
													assert isinstance(lowercase						,  lowercase )
													for k, v in name.items():
																	assert isinstance(lowercase						,  tf.Variable )
									else:
													lowerCamelCase_						=			model.get_output_embeddings()
													assert x is None
													lowerCamelCase_						=			model.get_bias()
													assert name is None




@require_tf
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )

					lowerCamelCase_						=			tf.constant([[0, 1, 2, 3, 4, 5]] )
					lowerCamelCase_						=			model(lowercase )[0]
					lowerCamelCase_						=			[1, 6, 33]
					self.assertEqual(list(output.numpy().shape )						,  lowercase )
					# compare the actual values for a slice.
					lowerCamelCase_						=			tf.constant(
					    [
					        [
					            [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
					            [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
					            [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
					        ]
					    ] )
					self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,  expected_slice.numpy()						,  atol=1e-2 ) )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )

					lowerCamelCase_						=			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
					lowerCamelCase_						=			model(lowercase )[0]
					# compare the actual values for a slice.
					lowerCamelCase_						=			tf.constant(
					    [
					        [
					            [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
					            [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
					            [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
					        ]
					    ] )
					self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,  expected_slice.numpy()						,  atol=1e-4 ) )


