



import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A__     				=	logging.get_logger(__name__)


def 		_UpperCAmelCase    (				snake_case     ,						snake_case=False     ,						snake_case=False      ):





     """simple docstring"""

     _lowerCAmelCase      =   """backbone.""" if is_semantic else """"""

     _lowerCAmelCase      =   []
     for i in range(config.num_hidden_layers      ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F'{prefix}blocks.{i}.norm1.weight', F'beit.encoder.layer.{i}.layernorm_before.weight')      )
          rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'beit.encoder.layer.{i}.layernorm_before.bias')      )
          rename_keys.append(
              (F'{prefix}blocks.{i}.attn.proj.weight', F'beit.encoder.layer.{i}.attention.output.dense.weight')      )
          rename_keys.append(
              (F'{prefix}blocks.{i}.attn.proj.bias', F'beit.encoder.layer.{i}.attention.output.dense.bias')      )
          rename_keys.append((F'{prefix}blocks.{i}.norm2.weight', F'beit.encoder.layer.{i}.layernorm_after.weight')      )
          rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'beit.encoder.layer.{i}.layernorm_after.bias')      )
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.weight', F'beit.encoder.layer.{i}.intermediate.dense.weight')      )
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.bias', F'beit.encoder.layer.{i}.intermediate.dense.bias')      )
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'beit.encoder.layer.{i}.output.dense.weight')      )
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'beit.encoder.layer.{i}.output.dense.bias')      )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (F'{prefix}cls_token', """beit.embeddings.cls_token"""),
             (F'{prefix}patch_embed.proj.weight', """beit.embeddings.patch_embeddings.projection.weight"""),
             (F'{prefix}patch_embed.proj.bias', """beit.embeddings.patch_embeddings.projection.bias"""),
         ]      )

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ("""mask_token""", """beit.embeddings.mask_token"""),
                  (
                      """rel_pos_bias.relative_position_bias_table""",
                      """beit.encoder.relative_position_bias.relative_position_bias_table""",
                  ),
                  (
                      """rel_pos_bias.relative_position_index""",
                      """beit.encoder.relative_position_bias.relative_position_index""",
                  ),
                  ("""norm.weight""", """layernorm.weight"""),
                  ("""norm.bias""", """layernorm.bias"""),
              ]      )
     elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                  ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                  ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                  ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
              ]      )
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
                  ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
                  ("""head.weight""", """classifier.weight"""),
                  ("""head.bias""", """classifier.bias"""),
              ]      )

     return rename_keys


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case=False     ,						snake_case=False      ):





     """simple docstring"""

     for i in range(config.num_hidden_layers      ):
          _lowerCAmelCase      =   """backbone.""" if is_semantic else """"""
          # queries, keys and values
          _lowerCAmelCase      =   state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'      )
          _lowerCAmelCase      =   state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'      )
          _lowerCAmelCase      =   state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'      )

          _lowerCAmelCase      =   in_proj_weight[
              : config.hidden_size, :
          ]
          _lowerCAmelCase      =   q_bias
          _lowerCAmelCase      =   in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _lowerCAmelCase      =   in_proj_weight[
              -config.hidden_size :, :
          ]
          _lowerCAmelCase      =   v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          _lowerCAmelCase      =   state_dict.pop(F'{prefix}blocks.{i}.gamma_1'      )
          _lowerCAmelCase      =   state_dict.pop(F'{prefix}blocks.{i}.gamma_2'      )

          _lowerCAmelCase      =   gamma_a
          _lowerCAmelCase      =   gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               _lowerCAmelCase      =   state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_bias_table'      )
               _lowerCAmelCase      =   state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_index'      )

               _lowerCAmelCase      =   table
               _lowerCAmelCase      =   index


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





     """simple docstring"""

     _lowerCAmelCase      =   dct.pop(snake_case      )
     _lowerCAmelCase      =   val


def 		_UpperCAmelCase    (				):





     """simple docstring"""

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


@torch.no_grad()
def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





     """simple docstring"""

     _lowerCAmelCase      =   BeitConfig()
     _lowerCAmelCase      =   False
     _lowerCAmelCase      =   False
     _lowerCAmelCase      =   """huggingface/label-files"""
     # set config parameters based on URL
     if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          _lowerCAmelCase      =   True
          _lowerCAmelCase      =   True
          _lowerCAmelCase      =   True
     elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          _lowerCAmelCase      =   True
          _lowerCAmelCase      =   2_18_41
          _lowerCAmelCase      =   """imagenet-22k-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()}
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[92_05]
          del idalabel[1_50_27]
          _lowerCAmelCase      =   idalabel
          _lowerCAmelCase      =   {v: k for k, v in idalabel.items()}
     elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          _lowerCAmelCase      =   True
          _lowerCAmelCase      =   10_00
          _lowerCAmelCase      =   """imagenet-1k-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()}
          if "384" in checkpoint_url:
               _lowerCAmelCase      =   3_84
          if "512" in checkpoint_url:
               _lowerCAmelCase      =   5_12
     elif "ade20k" in checkpoint_url:
          # fine-tuning
          _lowerCAmelCase      =   True
          _lowerCAmelCase      =   1_50
          _lowerCAmelCase      =   """ade20k-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()}
          _lowerCAmelCase      =   6_40
          _lowerCAmelCase      =   True
     else:
          raise ValueError("""Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\'"""      )

     # size of the architecture
     if "base" in checkpoint_url:
          pass
     elif "large" in checkpoint_url:
          _lowerCAmelCase      =   10_24
          _lowerCAmelCase      =   40_96
          _lowerCAmelCase      =   24
          _lowerCAmelCase      =   16
          if "ade20k" in checkpoint_url:
               _lowerCAmelCase      =   6_40
               _lowerCAmelCase      =   [7, 11, 15, 23]
     else:
          raise ValueError("""Should either find \'base\' or \'large\' in checkpoint URL"""      )

     # load state_dict of original model, remove and rename some keys
     _lowerCAmelCase      =   torch.hub.load_state_dict_from_url(snake_case     ,						map_location="""cpu"""     ,						check_hash=snake_case      )
     _lowerCAmelCase      =   state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

     _lowerCAmelCase      =   create_rename_keys(snake_case     ,						has_lm_head=snake_case     ,						is_semantic=snake_case      )
     for src, dest in rename_keys:
          rename_key(snake_case     ,						snake_case     ,						snake_case      )
     read_in_q_k_v(snake_case     ,						snake_case     ,						has_lm_head=snake_case     ,						is_semantic=snake_case      )
     if is_semantic:
          # add prefix to decoder keys
          for key, val in state_dict.copy().items():
               _lowerCAmelCase      =   state_dict.pop(snake_case      )
               if key.startswith("""backbone.fpn"""      ):
                    _lowerCAmelCase      =   key.replace("""backbone.fpn"""     ,						"""fpn"""      )
               _lowerCAmelCase      =   val

    # load HuggingFace model
     if checkpoint_url[-9:-4] == "pt22k":
          _lowerCAmelCase      =   BeitForMaskedImageModeling(snake_case      )
     elif "ade20k" in checkpoint_url:
          _lowerCAmelCase      =   BeitForSemanticSegmentation(snake_case      )
     else:
          _lowerCAmelCase      =   BeitForImageClassification(snake_case      )
     model.eval()
     model.load_state_dict(snake_case      )

     # Check outputs on an image
     if is_semantic:
          _lowerCAmelCase      =   BeitImageProcessor(size=config.image_size     ,						do_center_crop=snake_case      )
          _lowerCAmelCase      =   load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,						split="""test"""      )
          _lowerCAmelCase      =   Image.open(ds[0]["""file"""]      )
     else:
          _lowerCAmelCase      =   BeitImageProcessor(
              size=config.image_size     ,						resample=PILImageResampling.BILINEAR     ,						do_center_crop=snake_case      )
          _lowerCAmelCase      =   prepare_img()

     _lowerCAmelCase      =   image_processor(images=snake_case     ,						return_tensors="""pt"""      )
     _lowerCAmelCase      =   encoding["""pixel_values"""]

     _lowerCAmelCase      =   model(snake_case      )
     _lowerCAmelCase      =   outputs.logits

     # verify logits
     _lowerCAmelCase      =   torch.Size([1, 10_00]      )
     if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""      ):
          _lowerCAmelCase      =   torch.Size([1, 1_96, 81_92]      )
     elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""      ):
          _lowerCAmelCase      =   torch.Size([1, 1_96, 81_92]      )
     elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""      ):
          _lowerCAmelCase      =   torch.Size([1, 2_18_41]      )
          _lowerCAmelCase      =   torch.tensor([2.2_288, 2.4_671, 0.7_395]      )
          _lowerCAmelCase      =   23_97
     elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""      ):
          _lowerCAmelCase      =   torch.Size([1, 2_18_41]      )
          _lowerCAmelCase      =   torch.tensor([1.6_881, -0.2_787, 0.5_901]      )
          _lowerCAmelCase      =   23_96
     elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""      ):
          _lowerCAmelCase      =   torch.tensor([0.1_241, 0.0_798, -0.6_569]      )
          _lowerCAmelCase      =   2_85
     elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""      ):
          _lowerCAmelCase      =   torch.tensor([-1.2_385, -1.0_987, -1.0_108]      )
          _lowerCAmelCase      =   2_81
     elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""      ):
          _lowerCAmelCase      =   torch.tensor([-1.5_303, -0.9_484, -0.3_147]      )
          _lowerCAmelCase      =   7_61
     elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""      ):
          _lowerCAmelCase      =   torch.tensor([0.4_610, -0.0_928, 0.2_086]      )
          _lowerCAmelCase      =   7_61
     elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""      ):
          _lowerCAmelCase      =   torch.tensor([-0.4_804, 0.6_257, -0.1_837]      )
          _lowerCAmelCase      =   7_61
     elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""      ):
          _lowerCAmelCase      =   torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]      )
          _lowerCAmelCase      =   7_61
     elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""      ):
          _lowerCAmelCase      =   torch.tensor([-0.3_062, 0.7_261, 0.4_852]      )
          _lowerCAmelCase      =   7_61
     elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""      ):
          _lowerCAmelCase      =   (1, 1_50, 1_60, 1_60)
          _lowerCAmelCase      =   torch.tensor(
              [
                  [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                  [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                  [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
              ]      )
     elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""      ):
          _lowerCAmelCase      =   (1, 1_50, 1_60, 1_60)
          _lowerCAmelCase      =   torch.tensor(
              [
                  [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                  [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                  [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
              ]      )
     else:
          raise ValueError("""Can\'t verify logits as model is not supported"""      )

     assert logits.shape == expected_shape, "Shape of logits not as expected"
     if not has_lm_head:
          if is_semantic:
               assert torch.allclose(
                   logits[0, :3, :3, :3]     ,						snake_case     ,						atol=1E-3      ), "First elements of logits not as expected"
          else:
               print("""Predicted class idx:"""     ,						logits.argmax(-1      ).item()      )
               assert torch.allclose(
                   logits[0, :3]     ,						snake_case     ,						atol=1E-3      ), "First elements of logits not as expected"
               assert logits.argmax(-1      ).item() == expected_class_idx, "Predicted class index not as expected"

     Path(snake_case      ).mkdir(exist_ok=snake_case      )
     print(F'Saving model 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 __name__ == "__main__":
 A__     				=	argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth""",
     type=str,
     help="""URL to the original PyTorch checkpoint (.pth file).""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 A__     				=	parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


A__     				=	torch.cuda.is_available()
A__     				=	[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A__     				=	["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A__     				=	{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-0_8,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   """\n""".join(snake_case      )
					Path(snake_case      ).open("""w"""      ).writelines(snake_case      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(snake_case     ,						F'{split}.source'      )     ,						snake_case      )
										_dump_articles(os.path.join(snake_case     ,						F'{split}.target'      )     ,						snake_case      )
					return tmp_dir





class  __lowerCAmelCase			(					lowerCamelCase__	):




				@classmethod
				def 							snake_case       (							cls		):



									"""simple docstring"""





									return cls




				@require_torch_multi_gpu
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(_snake_case						,    check_contents=_snake_case		)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case=True		):



									"""simple docstring"""





									_lowerCAmelCase      =   {
									    """label_smoothing""": 0.0,
									    """early_stopping_patience""": -1,
									    """train_batch_size""": 1,
									    """eval_batch_size""": 2,
									    """max_epochs""": 2,
									    """alpha_mlm""": 0.2,
									    """alpha_ce""": 0.8,
									    """do_predict""": True,
									    """model_name_or_path""": """sshleifer/tinier_bart""",
									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
									    """val_check_interval""": 0.5,
									}
									default_updates.update(_snake_case		)
									_lowerCAmelCase      =   CHEAP_ARGS.copy()
									_lowerCAmelCase      =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									_lowerCAmelCase      =   self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=_snake_case						,    output_dir=_snake_case						,    **_snake_case		)

									def convert(_snake_case						,    _snake_case		):
														if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																			return ""
														if v is False or v is None:
																			return ""
														if v is True:  # or len(str(v))==0:
																			return F'--{k}'
														return F'--{k}={v}'

									_lowerCAmelCase      =   [x for x in (convert(_snake_case						,    _snake_case		) for k, v in args_d.items()) if len(_snake_case		)]
									_lowerCAmelCase      =   [sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
									execute_subprocess_async(_snake_case						,    env=self.get_env()		)

									_lowerCAmelCase      =   os.listdir(_snake_case		)
									_lowerCAmelCase      =   {os.path.basename(_snake_case		) for p in contents}
									_lowerCAmelCase      =   [p for p in contents if p.endswith("""ckpt"""		)]
									assert len(_snake_case		) > 0

									self.assertIn("""test_generations.txt"""						,    _snake_case		)
									self.assertIn("""test_results.txt"""						,    _snake_case		)

									# get the following from the module, (we don't have access to `model` here)
									_lowerCAmelCase      =   os.path.join(_snake_case						,    """metrics.json"""		)
									_lowerCAmelCase      =   """rouge2"""

									_lowerCAmelCase      =   load_json(_snake_case		)
									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
									print(_snake_case		)
									_lowerCAmelCase      =   metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]						,    0.01		)
									self.assertIsInstance(last_step_stats[F'val_avg_{val_metric}']						,    _snake_case		)
									self.assertEqual(len(metrics["""test"""]		)						,    1		)
									_lowerCAmelCase      =   int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1		)
									self.assertEqual(len(metrics["""val"""]		)						,    _snake_case		)



