



"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase							:   str				=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase							:   str				=				[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

 # q, k, v projections in self/cross-attention in decoder for conditional DETR
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
 )

 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
 )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[str]														=       val

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               _UpperCAmelCase     :      Dict														=       key.replace("backbone.0.body"		,    "backbone.conv_encoder.model"							)
               _UpperCAmelCase     :      Optional[int]														=       value
          else:
               _UpperCAmelCase     :      List[str]														=       value

     return new_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Dict=False							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       ""
     if is_panoptic:
          _UpperCAmelCase     :      Optional[Any]														=       "conditional_detr."

     # first: transformer encoder
     for i in range(6							):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          _UpperCAmelCase     :      Union[str, Any]														=       state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'							)
          _UpperCAmelCase     :      int														=       state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'							)
          # next, add query, keys and values (in that order) to the state dict
          _UpperCAmelCase     :      Tuple														=       in_proj_weight[:256, :]
          _UpperCAmelCase     :      Optional[Any]														=       in_proj_bias[:256]
          _UpperCAmelCase     :      List[str]														=       in_proj_weight[256:512, :]
          _UpperCAmelCase     :      Any														=       in_proj_bias[256:512]
          _UpperCAmelCase     :      Optional[int]														=       in_proj_weight[-256:, :]
          _UpperCAmelCase     :      str														=       in_proj_bias[-256:]

def 		__snake_case		( )    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       "http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase     :      Optional[int]														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							)

     return im

@torch.no_grad()
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          _UpperCAmelCase     :      Optional[int]														=       "resnet101"
     if "dc5" in model_name:
          _UpperCAmelCase     :      Dict														=       True
     _UpperCAmelCase     :      Optional[Any]														=       "panoptic" in model_name
     if is_panoptic:
          _UpperCAmelCase     :      Any														=       250
     else:
          _UpperCAmelCase     :      Any														=       91
          _UpperCAmelCase     :      Optional[int]														=       "huggingface/label-files"
          _UpperCAmelCase     :      Optional[int]														=       "coco-detection-id2label.json"
          _UpperCAmelCase     :      str														=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    repo_type="dataset"							)		,    "r"							)							)
          _UpperCAmelCase     :      Tuple														=       {int(SCREAMING_SNAKE_CASE__							): v for k, v in idalabel.items()}
          _UpperCAmelCase     :      int														=       idalabel
          _UpperCAmelCase     :      Optional[Any]														=       {v: k for k, v in idalabel.items()}

     # load image processor
     _UpperCAmelCase     :      List[str]														=       "coco_panoptic" if is_panoptic else "coco_detection"
     _UpperCAmelCase     :      Dict														=       ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE__							)

     # prepare image
     _UpperCAmelCase     :      Union[str, Any]														=       prepare_img()
     _UpperCAmelCase     :      Optional[Any]														=       image_processor(images=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							)
     _UpperCAmelCase     :      int														=       encoding["pixel_values"]

     logger.info(f'Converting model {model_name}...'							)

     # load original model from torch hub
     _UpperCAmelCase     :      Tuple														=       torch.hub.load("DeppMeng/ConditionalDETR"		,    SCREAMING_SNAKE_CASE__		,    pretrained=SCREAMING_SNAKE_CASE__							).eval()
     _UpperCAmelCase     :      Tuple														=       conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               _UpperCAmelCase     :      Optional[Any]														=       "conditional_detr." + src
          rename_key(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       rename_backbone_keys(SCREAMING_SNAKE_CASE__							)
     # query, key and value matrices need special treatment
     read_in_q_k_v(SCREAMING_SNAKE_CASE__		,    is_panoptic=SCREAMING_SNAKE_CASE__							)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _UpperCAmelCase     :      str														=       "conditional_detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("conditional_detr"							)
                   and not key.startswith("class_labels_classifier"							)
                   and not key.startswith("bbox_predictor"							)
               ):
                    _UpperCAmelCase     :      Optional[int]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
                    _UpperCAmelCase     :      Optional[Any]														=       val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    _UpperCAmelCase     :      List[str]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
                    _UpperCAmelCase     :      Dict														=       val
               elif key.startswith("bbox_attention"							) or key.startswith("mask_head"							):
                    continue
               else:
                    _UpperCAmelCase     :      Tuple														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
                    _UpperCAmelCase     :      Union[str, Any]														=       val
          else:
               if not key.startswith("class_labels_classifier"							) and not key.startswith("bbox_predictor"							):
                    _UpperCAmelCase     :      Optional[int]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
                    _UpperCAmelCase     :      int														=       val
    # finally, create HuggingFace model and load state dict
     _UpperCAmelCase     :      Tuple														=       ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE__							) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE__							)
     model.load_state_dict(SCREAMING_SNAKE_CASE__							)
     model.eval()
     model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE__		,    organization="DepuMeng"		,    commit_message="Add model"							)
     # verify our conversion
     _UpperCAmelCase     :      Optional[int]														=       conditional_detr(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       model(SCREAMING_SNAKE_CASE__							)
     assert torch.allclose(outputs.logits		,    original_outputs["pred_logits"]		,    atol=1E-4							)
     assert torch.allclose(outputs.pred_boxes		,    original_outputs["pred_boxes"]		,    atol=1E-4							)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks		,    original_outputs["pred_masks"]		,    atol=1E-4							)

     # Save model and image processor
     logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'							)
     Path(SCREAMING_SNAKE_CASE__							).mkdir(exist_ok=SCREAMING_SNAKE_CASE__							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Any				=				argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     default="conditional_detr_resnet50",
     type=str,
     help="Name of the CONDITIONAL_DETR model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 _lowerCAmelCase							:   Optional[int]				=				parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments






@require_torch
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Optional[Any]		):
           for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["bs"]	,      model_result["ss"]		):
                     _UpperCAmelCase     :      List[str]														=       model_result["result"][batch_size][sequence_length]
                     self.assertIsNotNone(A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[str]														=       "sgugger/tiny-distilbert-classification"
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      only_pretrain_model=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      torchscript=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      @unittest.skipIf(torch_device == "cpu"	,      "Cant do half precision"		)
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      fpaa=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[str]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           # set architectures equal to `None`
           _UpperCAmelCase     :      Optional[Any]														=       None
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Dict														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      @unittest.skipIf(torch_device == "cpu"	,      "Can't do half precision"		)
      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      str														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      fpaa=A	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      List[str]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[str]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      Any														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Union[str, Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Tuple														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Tuple														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Optional[int]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      str														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      int														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      int														=       "sshleifer/tiny-gpt2"
           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase     :      Union[str, Any]														=       PyTorchBenchmarkArguments(
                    models=[MODEL_ID]	,      training=A	,      inference=A	,      save_to_csv=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      inference_time_csv_file=os.path.join(A	,      "inf_time.csv"		)	,      train_memory_csv_file=os.path.join(A	,      "train_mem.csv"		)	,      inference_memory_csv_file=os.path.join(A	,      "inf_mem.csv"		)	,      train_time_csv_file=os.path.join(A	,      "train_time.csv"		)	,      env_info_csv_file=os.path.join(A	,      "env.csv"		)	,      multi_process=A	,      )
                _UpperCAmelCase     :      int														=       PyTorchBenchmark(A		)
                benchmark.run()
                self.assertTrue(Path(os.path.join(A	,      "inf_time.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "train_time.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "inf_mem.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "train_mem.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "env.csv"		)		).exists()		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[Any]														=       "sshleifer/tiny-gpt2"

           def _check_summary_is_not_empty(A	:      int		):
                self.assertTrue(hasattr(A	,      "sequential"		)		)
                self.assertTrue(hasattr(A	,      "cumulative"		)		)
                self.assertTrue(hasattr(A	,      "current"		)		)
                self.assertTrue(hasattr(A	,      "total"		)		)

           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase     :      int														=       PyTorchBenchmarkArguments(
                    models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      log_filename=os.path.join(A	,      "log.txt"		)	,      log_print=A	,      trace_memory_line_by_line=A	,      multi_process=A	,      )
                _UpperCAmelCase     :      int														=       PyTorchBenchmark(A		)
                _UpperCAmelCase     :      Any														=       benchmark.run()
                _check_summary_is_not_empty(result.inference_summary		)
                _check_summary_is_not_empty(result.train_summary		)
                self.assertTrue(Path(os.path.join(A	,      "log.txt"		)		).exists()		)
