


'''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()
a_						:       Dict      			=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_						:       Optional[int]      			=				[]
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      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[str]	):
 lowerCamelCase_  				=						state_dict.pop(UpperCAmelCase_	)
 lowerCamelCase_  				=						val





def __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 lowerCamelCase_  				=						OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   lowerCamelCase_  				=						key.replace("backbone.0.body"      ,       "backbone.conv_encoder.model"	)
   lowerCamelCase_  				=						value
  else:
   lowerCamelCase_  				=						value

 return new_state_dict





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		int=False	):
 lowerCamelCase_  				=						""
 if is_panoptic:
  lowerCamelCase_  				=						"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)
  lowerCamelCase_  				=						state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
  lowerCamelCase_  				=						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
  lowerCamelCase_  				=						in_proj_weight[:256, :]
  lowerCamelCase_  				=						in_proj_bias[:256]
  lowerCamelCase_  				=						in_proj_weight[256:512, :]
  lowerCamelCase_  				=						in_proj_bias[256:512]
  lowerCamelCase_  				=						in_proj_weight[-256:, :]
  lowerCamelCase_  				=						in_proj_bias[-256:]





def __snake_case      (   ):
 lowerCamelCase_  				=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_  				=						Image.open(requests.get(UpperCAmelCase_      ,       stream=UpperCAmelCase_	).raw	)

 return im





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

 # load image processor
 lowerCamelCase_  				=						"coco_panoptic" if is_panoptic else "coco_detection"
 lowerCamelCase_  				=						ConditionalDetrImageProcessor(format=UpperCAmelCase_	)

 # prepare image
 lowerCamelCase_  				=						prepare_img()
 lowerCamelCase_  				=						image_processor(images=UpperCAmelCase_      ,       return_tensors="pt"	)
 lowerCamelCase_  				=						encoding["pixel_values"]

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

 # load original model from torch hub
 lowerCamelCase_  				=						torch.hub.load("DeppMeng/ConditionalDETR"      ,       UpperCAmelCase_      ,       pretrained=UpperCAmelCase_	).eval()
 lowerCamelCase_  				=						conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   lowerCamelCase_  				=						"conditional_detr." + src
  rename_key(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						rename_backbone_keys(UpperCAmelCase_	)
 # query, key and value matrices need special treatment
 read_in_q_k_v(UpperCAmelCase_      ,       is_panoptic=UpperCAmelCase_	)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 lowerCamelCase_  				=						"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"	)
   ):
    lowerCamelCase_  				=						state_dict.pop(UpperCAmelCase_	)
    lowerCamelCase_  				=						val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    lowerCamelCase_  				=						state_dict.pop(UpperCAmelCase_	)
    lowerCamelCase_  				=						val
   elif key.startswith("bbox_attention"	) or key.startswith("mask_head"	):
    continue
   else:
    lowerCamelCase_  				=						state_dict.pop(UpperCAmelCase_	)
    lowerCamelCase_  				=						val
  else:
   if not key.startswith("class_labels_classifier"	) and not key.startswith("bbox_predictor"	):
    lowerCamelCase_  				=						state_dict.pop(UpperCAmelCase_	)
    lowerCamelCase_  				=						val
    # finally, create HuggingFace model and load state dict
 lowerCamelCase_  				=						ConditionalDetrForSegmentation(UpperCAmelCase_	) if is_panoptic else ConditionalDetrForObjectDetection(UpperCAmelCase_	)
 model.load_state_dict(UpperCAmelCase_	)
 model.eval()
 model.push_to_hub(repo_id=UpperCAmelCase_      ,       organization="DepuMeng"      ,       commit_message="Add model"	)
 # verify our conversion
 lowerCamelCase_  				=						conditional_detr(UpperCAmelCase_	)
 lowerCamelCase_  				=						model(UpperCAmelCase_	)
 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(UpperCAmelCase_	).mkdir(exist_ok=UpperCAmelCase_	)
 model.save_pretrained(UpperCAmelCase_	)
 image_processor.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
     a_						:       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."""
     )
     a_						:       Any      			=				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_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   for model_result in results.values():
    for batch_size, sequence_length in zip(model_result["bs"]    ,						model_result["ss"]  ):
     lowerCamelCase_  				=						model_result["result"][batch_size][sequence_length]
     self.assertIsNotNone(UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sgugger/tiny-distilbert-classification"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						only_pretrain_model=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"patrickvonplaten/t5-tiny-random"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						configs=[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"  )  ) == 0    ,						"Cannot do xla on CPU."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						use_xla=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						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  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						inference=UpperCamelCase    ,						save_to_csv=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						inference_time_csv_file=os.path.join(UpperCamelCase    ,						"inf_time.csv"  )    ,						inference_memory_csv_file=os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )    ,						env_info_csv_file=os.path.join(UpperCamelCase    ,						"env.csv"  )    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    benchmark.run()
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_time.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"env.csv"  )  ).exists()  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"

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

   with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						log_filename=os.path.join(UpperCamelCase    ,						"log.txt"  )    ,						log_print=UpperCamelCase    ,						trace_memory_line_by_line=UpperCamelCase    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    lowerCamelCase_  				=						benchmark.run()
    _check_summary_is_not_empty(result.inference_summary  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"log.txt"  )  ).exists()  )



