






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()
__UpperCAmelCase								=						logging.get_logger(__name__)




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						YolosConfig()

					# size of the architecture
					if "yolos_ti" in yolos_name:
										UpperCAmelCase_    :      Tuple            =						192
										UpperCAmelCase_    :      str            =						768
										UpperCAmelCase_    :      List[Any]            =						12
										UpperCAmelCase_    :      Optional[int]            =						3
										UpperCAmelCase_    :      Optional[Any]            =						[800, 1_333]
										UpperCAmelCase_    :      Dict            =						False
					elif yolos_name == "yolos_s_dWr":
										UpperCAmelCase_    :      Tuple            =						330
										UpperCAmelCase_    :      List[str]            =						14
										UpperCAmelCase_    :      Tuple            =						6
										UpperCAmelCase_    :      List[str]            =						1_320
					elif "yolos_s" in yolos_name:
										UpperCAmelCase_    :      Any            =						384
										UpperCAmelCase_    :      Union[str, Any]            =						1_536
										UpperCAmelCase_    :      List[str]            =						12
										UpperCAmelCase_    :      Any            =						6
					elif "yolos_b" in yolos_name:
										UpperCAmelCase_    :      Optional[int]            =						[800, 1_344]

					UpperCAmelCase_    :      str            =						91
					UpperCAmelCase_    :      Optional[Any]            =						'huggingface/label-files'
					UpperCAmelCase_    :      Any            =						'coco-detection-id2label.json'
					UpperCAmelCase_    :      Tuple            =						json.load(open(hf_hub_download(__snake_case     ,       __snake_case     ,       repo_type='dataset'  )     ,       'r'  )  )
					UpperCAmelCase_    :      Optional[Any]            =						{int(__snake_case  ): v for k, v in idalabel.items()}
					UpperCAmelCase_    :      Optional[Any]            =						idalabel
					UpperCAmelCase_    :      List[Any]            =						{v: k for k, v in idalabel.items()}

					return config




def      lowercase__      (     __snake_case    :							dict     ,       __snake_case    :							YolosConfig     ,       __snake_case    :							bool = False  ):


					'''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)
										UpperCAmelCase_    :      Optional[Any]            =						state_dict.pop(F"blocks.{i}.attn.qkv.weight"  )
										UpperCAmelCase_    :      Tuple            =						state_dict.pop(F"blocks.{i}.attn.qkv.bias"  )
										# next, add query, keys and values (in that order) to the state dict
										UpperCAmelCase_    :      Tuple            =						in_proj_weight[: config.hidden_size, :]
										UpperCAmelCase_    :      Optional[int]            =						in_proj_bias[: config.hidden_size]
										UpperCAmelCase_    :      Tuple            =						in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										UpperCAmelCase_    :      Optional[Any]            =						in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										UpperCAmelCase_    :      List[str]            =						in_proj_weight[-config.hidden_size :, :]
										UpperCAmelCase_    :      str            =						in_proj_bias[-config.hidden_size :]




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


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

					return name




def      lowercase__      (     __snake_case    :							dict     ,       __snake_case    :							YolosForObjectDetection  ):


					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										UpperCAmelCase_    :      List[str]            =						orig_state_dict.pop(__snake_case  )

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

					return orig_state_dict




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						'http://images.cocodataset.org/val2017/000000039769.jpg'
					UpperCAmelCase_    :      Any            =						Image.open(requests.get(__snake_case     ,       stream=__snake_case  ).raw  )
					return im




@torch.no_grad()
def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							bool = False  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						get_yolos_config(__snake_case  )

					# load original state_dict
					UpperCAmelCase_    :      Tuple            =						torch.load(__snake_case     ,       map_location='cpu'  )['model']

					# load 🤗 model
					UpperCAmelCase_    :      Optional[Any]            =						YolosForObjectDetection(__snake_case  )
					model.eval()
					UpperCAmelCase_    :      List[str]            =						convert_state_dict(__snake_case     ,       __snake_case  )
					model.load_state_dict(__snake_case  )

					# Check outputs on an image, prepared by YolosImageProcessor
					UpperCAmelCase_    :      int            =						800 if yolos_name != 'yolos_ti' else 512
					UpperCAmelCase_    :      str            =						YolosImageProcessor(format='coco_detection'     ,       size=__snake_case  )
					UpperCAmelCase_    :      Optional[Any]            =						image_processor(images=prepare_img()     ,       return_tensors='pt'  )
					UpperCAmelCase_    :      Optional[Any]            =						model(**__snake_case  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						outputs.logits, outputs.pred_boxes

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


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

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











'''simple docstring'''




import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
						import torch

						from transformers import (
						    BioGptForCausalLM,
						    BioGptForSequenceClassification,
						    BioGptForTokenClassification,
						    BioGptModel,
						    BioGptTokenizer,
						)
						from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=True       ,      lowercase=False       ,      lowercase=True       ,      lowercase=99       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=512       ,      lowercase=16       ,      lowercase=2       ,      lowercase=0.02       ,      lowercase=3       ,      lowercase=4       ,      lowercase=None       ,      ):
				A_	:						str	=     parent
				A_	:						int	=     batch_size
				A_	:						Dict	=     seq_length
				A_	:						Any	=     is_training
				A_	:						List[str]	=     use_input_mask
				A_	:						Any	=     use_token_type_ids
				A_	:						int	=     use_labels
				A_	:						str	=     vocab_size
				A_	:						Any	=     hidden_size
				A_	:						Optional[Any]	=     num_hidden_layers
				A_	:						Dict	=     num_attention_heads
				A_	:						Dict	=     intermediate_size
				A_	:						Optional[int]	=     hidden_act
				A_	:						int	=     hidden_dropout_prob
				A_	:						Optional[int]	=     attention_probs_dropout_prob
				A_	:						Any	=     max_position_embeddings
				A_	:						List[Any]	=     type_vocab_size
				A_	:						Any	=     type_sequence_label_size
				A_	:						Tuple	=     initializer_range
				A_	:						int	=     num_labels
				A_	:						Optional[int]	=     num_choices
				A_	:						Optional[int]	=     scope


	def     _a	(self							):
				A_	:						Tuple	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						List[str]	=     None
				if self.use_input_mask:
							A_	:						Any	=     random_attention_mask([self.batch_size, self.seq_length]							)

				A_	:						int	=     None
				if self.use_token_type_ids:
							A_	:						int	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.type_vocab_size							)

				A_	:						Tuple	=     None
				A_	:						List[str]	=     None
				A_	:						List[str]	=     None
				if self.use_labels:
							A_	:						Any	=     ids_tensor([self.batch_size]       ,      self.type_sequence_label_size							)
							A_	:						Dict	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.num_labels							)
							A_	:						str	=     ids_tensor([self.batch_size]       ,      self.num_choices							)

				A_	:						Optional[Any]	=     self.get_config()

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


	def     _a	(self							):
				return BioGptConfig(
				    vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      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       ,      is_decoder=lowercase       ,      initializer_range=self.initializer_range       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     BioGptModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						List[Any]	=     model(lowercase       ,      attention_mask=lowercase							)
				A_	:						str	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      ):
				A_	:						List[Any]	=     BioGptForCausalLM(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Dict	=     model(lowercase       ,      attention_mask=lowercase       ,      token_type_ids=lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      *lowercase							):
				A_	:						Optional[Any]	=     BioGptModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()

				# create attention mask
				A_	:						str	=     torch.ones(input_ids.shape       ,      dtype=torch.long       ,      device=lowercase							)
				A_	:						Optional[Any]	=     self.seq_length // 2
				A_	:						List[Any]	=     0

				# first forward pass
				A_,    A_	:						List[str]	=     model(lowercase       ,      attention_mask=lowercase							).to_tuple()

				# create hypothetical next token and extent to next_input_ids
				A_	:						Optional[Any]	=     ids_tensor((self.batch_size, 1)       ,      config.vocab_size							)

				# change a random masked slice from input_ids
				A_	:						Union[str, Any]	=     ids_tensor((1,)       ,      lowercase							).item() + 1
				A_	:						List[str]	=     ids_tensor((self.batch_size, 1)       ,      config.vocab_size							).squeeze(-1							)
				A_	:						Optional[int]	=     random_other_next_tokens

				# append to next input_ids and attn_mask
				A_	:						Union[str, Any]	=     torch.cat([input_ids, next_tokens]       ,      dim=-1							)
				A_	:						Any	=     torch.cat(
				    [attn_mask, torch.ones((attn_mask.shape[0], 1)       ,      dtype=torch.long       ,      device=lowercase							)]       ,      dim=1       ,      )

				# get two different outputs
				A_	:						List[Any]	=     model(lowercase       ,      attention_mask=lowercase							)["""last_hidden_state"""]
				A_	:						Optional[int]	=     model(lowercase       ,      past_key_values=lowercase       ,      attention_mask=lowercase							)["""last_hidden_state"""]

				# select random slice
				A_	:						List[str]	=     ids_tensor((1,)       ,      output_from_past.shape[-1]							).item()
				A_	:						int	=     output_from_no_past[:, -1, random_slice_idx].detach()
				A_	:						List[Any]	=     output_from_past[:, 0, random_slice_idx].detach()

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(lowercase       ,      lowercase       ,      atol=1E-3							)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      *lowercase							):
				A_	:						Optional[int]	=     BioGptModel(config=lowercase							).to(lowercase							).eval()

				A_	:						Union[str, Any]	=     torch.ones(input_ids.shape       ,      dtype=torch.long       ,      device=lowercase							)

				# first forward pass
				A_	:						int	=     model(lowercase       ,      attention_mask=lowercase       ,      use_cache=lowercase							)

				A_,    A_	:						int	=     outputs.to_tuple()

				# create hypothetical multiple next token and extent to next_input_ids
				A_	:						Optional[int]	=     ids_tensor((self.batch_size, 3)       ,      config.vocab_size							)
				A_	:						str	=     ids_tensor((self.batch_size, 3)       ,      2							)

				# append to next input_ids and
				A_	:						Union[str, Any]	=     torch.cat([input_ids, next_tokens]       ,      dim=-1							)
				A_	:						int	=     torch.cat([attention_mask, next_attn_mask]       ,      dim=-1							)

				A_	:						List[str]	=     model(lowercase       ,      attention_mask=lowercase							)["""last_hidden_state"""]
				A_	:						Tuple	=     model(lowercase       ,      attention_mask=lowercase       ,      past_key_values=lowercase							)[
				    """last_hidden_state"""
				]

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


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      *lowercase       ,      lowercase=False							):
				A_	:						Union[str, Any]	=     BioGptForCausalLM(lowercase							)
				model.to(lowercase							)
				if gradient_checkpointing:
							model.gradient_checkpointing_enable()

				A_	:						Dict	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.loss.shape       ,      ()							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)							)
				result.loss.backward()


	def     _a	(self       ,      lowercase       ,      *lowercase							):
				A_	:						Union[str, Any]	=     BioGptModel(lowercase							)
				A_	:						str	=     model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers							)
				for key in model.state_dict().keys():
							if "c_proj" in key and "weight" in key:
										self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]							) - model_std							)       ,      0.0_01							)
										self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]							) - 0.0							)       ,      0.01							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      *lowercase							):
				A_	:						Union[str, Any]	=     self.num_labels
				A_	:						Optional[int]	=     BioGptForTokenClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Dict	=     model(lowercase       ,      attention_mask=lowercase       ,      token_type_ids=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.prepare_config_and_inputs()
				(
				    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    
				)	:						Dict	=     config_and_inputs
				A_	:						Tuple	=     {"""input_ids""": input_ids, """attention_mask""": input_mask}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		str					=					(
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE   :		int					=					(BioGptForCausalLM,) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    {
	        'feature-extraction': BioGptModel,
	        'text-classification': BioGptForSequenceClassification,
	        'text-generation': BioGptForCausalLM,
	        'token-classification': BioGptForTokenClassification,
	        'zero-shot': BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE   :		List[str]					=					False


	def     _a	(self							):
				A_	:						Tuple	=     BioGptModelTester(self							)
				A_	:						List[Any]	=     ConfigTester(self       ,      config_class=lowercase       ,      hidden_size=37							)


	def     _a	(self							):
				self.config_tester.run_common_tests()


	def     _a	(self							):
				A_	:						Optional[Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							A_	:						int	=     type
							self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						Union[str, Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowercase							)


	def     _a	(self							):
				A_	:						Dict	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_forward_and_backwards(*lowercase       ,      gradient_checkpointing=lowercase							)


	def     _a	(self							):
				A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowercase							)


	def     _a	(self							):
				A_	:						int	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*lowercase							)


	def     _a	(self							):
				A_	:						List[str]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*lowercase							)


	@slow
	def     _a	(self							):
				A_	:						str	=     BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""							)
				model.to(lowercase							)
				A_	:						List[str]	=     BioGptTokenizer.from_pretrained("""microsoft/biogpt"""							)

				A_	:						List[str]	=     """left"""

				# Define PAD Token = EOS Token = 50256
				A_	:						Any	=     tokenizer.eos_token
				A_	:						Dict	=     model.config.eos_token_id

				# use different length sentences to test batching
				A_	:						List[Any]	=     [
				    """Hello, my dog is a little""",
				    """Today, I""",
				]

				A_	:						List[str]	=     tokenizer(lowercase       ,      return_tensors="""pt"""       ,      padding=lowercase							)
				A_	:						List[str]	=     inputs["""input_ids"""].to(lowercase							)

				A_	:						List[Any]	=     model.generate(
				    input_ids=lowercase       ,      attention_mask=inputs["""attention_mask"""].to(lowercase							)       ,      )

				A_	:						List[str]	=     tokenizer(sentences[0]       ,      return_tensors="""pt"""							).input_ids.to(lowercase							)
				A_	:						List[Any]	=     model.generate(input_ids=lowercase							)

				A_	:						List[str]	=     inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
				A_	:						Union[str, Any]	=     tokenizer(sentences[1]       ,      return_tensors="""pt"""							).input_ids.to(lowercase							)
				A_	:						Any	=     model.generate(input_ids=lowercase       ,      max_length=model.config.max_length - num_paddings							)

				A_	:						List[str]	=     tokenizer.batch_decode(lowercase       ,      skip_special_tokens=lowercase							)
				A_	:						str	=     tokenizer.decode(output_non_padded[0]       ,      skip_special_tokens=lowercase							)
				A_	:						List[str]	=     tokenizer.decode(output_padded[0]       ,      skip_special_tokens=lowercase							)

				A_	:						Union[str, Any]	=     [
				    """Hello, my dog is a little bit bigger than a little bit.""",
				    """Today, I have a good idea of how to use the information""",
				]
				self.assertListEqual(lowercase       ,      lowercase							)
				self.assertListEqual(lowercase       ,      [non_padded_sentence, padded_sentence]							)


	@slow
	def     _a	(self							):
				for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						int	=     BioGptModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()
				A_	:						List[Any]	=     3
				A_	:						Union[str, Any]	=     input_dict["""input_ids"""]
				A_	:						List[Any]	=     input_ids.ne(1							).to(lowercase							)
				A_	:						str	=     ids_tensor([self.model_tester.batch_size]       ,      self.model_tester.type_sequence_label_size							)
				A_	:						Union[str, Any]	=     BioGptForSequenceClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Tuple	=     model(lowercase       ,      attention_mask=lowercase       ,      labels=lowercase							)
				self.assertEqual(result.logits.shape       ,      (self.model_tester.batch_size, self.model_tester.num_labels)							)


	def     _a	(self							):
				A_,    A_	:						Dict	=     self.model_tester.prepare_config_and_inputs_for_common()
				A_	:						Tuple	=     3
				A_	:						Dict	=     """multi_label_classification"""
				A_	:						List[Any]	=     input_dict["""input_ids"""]
				A_	:						Tuple	=     input_ids.ne(1							).to(lowercase							)
				A_	:						List[str]	=     ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]       ,      self.model_tester.type_sequence_label_size							).to(torch.float							)
				A_	:						Dict	=     BioGptForSequenceClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						int	=     model(lowercase       ,      attention_mask=lowercase       ,      labels=lowercase							)
				self.assertEqual(result.logits.shape       ,      (self.model_tester.batch_size, self.model_tester.num_labels)							)







@require_torch
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						Dict	=     BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""							)
				A_	:						Optional[Any]	=     torch.tensor([[2, 4805, 9, 656, 21]]							)
				A_	:						Dict	=     model(lowercase							)[0]

				A_	:						Any	=     42384

				A_	:						Union[str, Any]	=     torch.Size((1, 5, vocab_size)							)
				self.assertEqual(output.shape       ,      lowercase							)

				A_	:						Union[str, Any]	=     torch.tensor(
				    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]							)

				self.assertTrue(torch.allclose(output[:, :3, :3]       ,      lowercase       ,      atol=1E-4							)							)


	@slow
	def     _a	(self							):
				A_	:						Union[str, Any]	=     BioGptTokenizer.from_pretrained("""microsoft/biogpt"""							)
				A_	:						Dict	=     BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""							)
				model.to(lowercase							)

				torch.manual_seed(0							)
				A_	:						Dict	=     tokenizer("""COVID-19 is"""       ,      return_tensors="""pt"""							).to(lowercase							)
				A_	:						Optional[int]	=     model.generate(
				    **lowercase       ,      min_length=100       ,      max_length=1024       ,      num_beams=5       ,      early_stopping=lowercase       ,      )
				A_	:						Union[str, Any]	=     tokenizer.decode(output_ids[0]       ,      skip_special_tokens=lowercase							)

				A_	:						Any	=     (
				    """COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
				    """ causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
				    """ territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
				    """ and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
				    """ more than 800,000 deaths."""
				)
				self.assertEqual(lowercase       ,      lowercase							)