






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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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

def        __lowerCamelCase			(		snake_case__		)							->  List[Any]:






						"""simple docstring"""





						if "resnet-50" in model_name:
												_SCREAMING_SNAKE_CASE	      =  ResNetConfig.from_pretrained("""microsoft/resnet-50"""		)
						elif "resnet-101" in model_name:
												_SCREAMING_SNAKE_CASE	      =  ResNetConfig.from_pretrained("""microsoft/resnet-101"""		)
						else:
												raise ValueError("""Model name should include either resnet50 or resnet101"""		)

						_SCREAMING_SNAKE_CASE	      =  DetrConfig(use_timm_backbone=__lowerCAmelCase       ,backbone_config=__lowerCAmelCase		)

						# set label attributes
						_SCREAMING_SNAKE_CASE	      =  """panoptic""" in model_name
						if is_panoptic:
												_SCREAMING_SNAKE_CASE	      =  2_50
						else:
												_SCREAMING_SNAKE_CASE	      =  91
												_SCREAMING_SNAKE_CASE	      =  """huggingface/label-files"""
												_SCREAMING_SNAKE_CASE	      =  """coco-detection-id2label.json"""
												_SCREAMING_SNAKE_CASE	      =  json.load(open(hf_hub_download(__lowerCAmelCase       ,__lowerCAmelCase       ,repo_type="""dataset"""		)       ,"""r"""		)		)
												_SCREAMING_SNAKE_CASE	      =  {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
												_SCREAMING_SNAKE_CASE	      =  idalabel
												_SCREAMING_SNAKE_CASE	      =  {v: k for k, v in idalabel.items()}

						return config, is_panoptic

def        __lowerCamelCase			(		snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  []

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")		)
						rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")		)
						rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")		)
						rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")		)
						rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")		)
						# stages
						for stage_idx in range(len(config.backbone_config.depths		)		):
												for layer_idx in range(config.backbone_config.depths[stage_idx]		):
																		# shortcut
																		if layer_idx == 0:
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
																								    )		)
																		# 3 convs
																		for i in range(3		):
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																								    )		)
    # fmt: on

						for i in range(config.encoder_layers		):
												# 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}.multihead_attn.out_proj.weight',
												        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
												    )		)
												rename_keys.append(
												    (
												        F'transformer.decoder.layers.{i}.multihead_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')		)

						# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
						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"""),
						    ]		)

						return rename_keys

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  state_dict.pop(__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  val

def        __lowerCamelCase			(		snake_case__       ,snake_case__=False		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  """"""
						if is_panoptic:
												_SCREAMING_SNAKE_CASE	      =  """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)
												_SCREAMING_SNAKE_CASE	      =  state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
												_SCREAMING_SNAKE_CASE	      =  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
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight[:2_56, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias[:2_56]
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight[2_56:5_12, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias[2_56:5_12]
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight[-2_56:, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias[-2_56:]
						# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
						for i in range(6		):
												# read in weights + bias of input projection layer of self-attention
												_SCREAMING_SNAKE_CASE	      =  state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
												_SCREAMING_SNAKE_CASE	      =  state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'		)
												# next, add query, keys and values (in that order) to the state dict
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight[:2_56, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias[:2_56]
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight[2_56:5_12, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias[2_56:5_12]
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight[-2_56:, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias[-2_56:]
												# read in weights + bias of input projection layer of cross-attention
												_SCREAMING_SNAKE_CASE	      =  state_dict.pop(
												    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
												_SCREAMING_SNAKE_CASE	      =  state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'		)
												# next, add query, keys and values (in that order) of cross-attention to the state dict
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight_cross_attn[:2_56, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias_cross_attn[:2_56]
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight_cross_attn[2_56:5_12, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias_cross_attn[2_56:5_12]
												_SCREAMING_SNAKE_CASE	      =  in_proj_weight_cross_attn[-2_56:, :]
												_SCREAMING_SNAKE_CASE	      =  in_proj_bias_cross_attn[-2_56:]

def        __lowerCamelCase			(		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						_SCREAMING_SNAKE_CASE	      =  Image.open(requests.get(__lowerCAmelCase       ,stream=__lowerCAmelCase		).raw		)

						return im

@torch.no_grad()
def        __lowerCamelCase			(		snake_case__       ,snake_case__=None       ,snake_case__=False		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE	      =  get_detr_config(__lowerCAmelCase		)

						# load original model from torch hub
						_SCREAMING_SNAKE_CASE	      =  {
						    """detr-resnet-50""": """detr_resnet50""",
						    """detr-resnet-101""": """detr_resnet101""",
						}
						logger.info(F'Converting model {model_name}...'		)
						_SCREAMING_SNAKE_CASE	      =  torch.hub.load("""facebookresearch/detr"""       ,model_name_to_original_name[model_name]       ,pretrained=__lowerCAmelCase		).eval()
						_SCREAMING_SNAKE_CASE	      =  detr.state_dict()
						# rename keys
						for src, dest in create_rename_keys(__lowerCAmelCase		):
												if is_panoptic:
																		_SCREAMING_SNAKE_CASE	      =  """detr.""" + src
												rename_key(__lowerCAmelCase       ,__lowerCAmelCase       ,__lowerCAmelCase		)
						# query, key and value matrices need special treatment
						read_in_q_k_v(__lowerCAmelCase       ,is_panoptic=__lowerCAmelCase		)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_SCREAMING_SNAKE_CASE	      =  """detr.model.""" if is_panoptic else """model."""
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith("""detr"""		)
																		    and not key.startswith("""class_labels_classifier"""		)
																		    and not key.startswith("""bbox_predictor"""		)
																		):
																								_SCREAMING_SNAKE_CASE	      =  state_dict.pop(__lowerCAmelCase		)
																								_SCREAMING_SNAKE_CASE	      =  val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								_SCREAMING_SNAKE_CASE	      =  state_dict.pop(__lowerCAmelCase		)
																								_SCREAMING_SNAKE_CASE	      =  val
																		elif key.startswith("""bbox_attention"""		) or key.startswith("""mask_head"""		):
																								continue
																		else:
																								_SCREAMING_SNAKE_CASE	      =  state_dict.pop(__lowerCAmelCase		)
																								_SCREAMING_SNAKE_CASE	      =  val
												else:
																		if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
																								_SCREAMING_SNAKE_CASE	      =  state_dict.pop(__lowerCAmelCase		)
																								_SCREAMING_SNAKE_CASE	      =  val

    # finally, create HuggingFace model and load state dict
						_SCREAMING_SNAKE_CASE	      =  DetrForSegmentation(__lowerCAmelCase		) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase		)
						model.load_state_dict(__lowerCAmelCase		)
						model.eval()

						# verify our conversion on an image
						_SCREAMING_SNAKE_CASE	      =  """coco_panoptic""" if is_panoptic else """coco_detection"""
						_SCREAMING_SNAKE_CASE	      =  DetrImageProcessor(format=__lowerCAmelCase		)

						_SCREAMING_SNAKE_CASE	      =  processor(images=prepare_img()       ,return_tensors="""pt"""		)
						_SCREAMING_SNAKE_CASE	      =  encoding["""pixel_values"""]

						_SCREAMING_SNAKE_CASE	      =  detr(__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  model(__lowerCAmelCase		)

						assert torch.allclose(outputs.logits       ,original_outputs["""pred_logits"""]       ,atol=1e-3		)
						assert torch.allclose(outputs.pred_boxes       ,original_outputs["""pred_boxes"""]       ,atol=1e-3		)
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks       ,original_outputs["""pred_masks"""]       ,atol=1e-4		)
						print("""Looks ok!"""		)

						if pytorch_dump_folder_path is not None:
												# Save model and image processor
												logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'		)
												Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
												model.save_pretrained(__lowerCAmelCase		)
												processor.save_pretrained(__lowerCAmelCase		)

						if push_to_hub:
												# Upload model and image processor to the hub
												logger.info("""Uploading PyTorch model and image processor to the hub..."""		)
												model.push_to_hub(F'nielsr/{model_name}'		)
												processor.push_to_hub(F'nielsr/{model_name}'		)


if __name__ == "__main__":
		UpperCamelCase 				=	argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''detr-resnet-50''',
		    type=str,
		    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
		    help='''Name of the 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.'''
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
		UpperCamelCase 				=	parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


SCREAMING_SNAKE_CASE__   :			Optional[int]												=    os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
					from run_translation import main  # noqa


set_seed(42)
SCREAMING_SNAKE_CASE__   :			Any												=    "sshleifer/student_marian_en_ro_6_1"
SCREAMING_SNAKE_CASE__   :			Tuple												=    "sshleifer/tiny-mbart"

@require_torch
class    lowerCAmelCase__     (	__lowercase			):






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple=False							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=True							,			)     ->							Optional[int]:
						__lowerCamelCase    					=	self.run_trainer(
						    eval_steps=1							,			max_len=12							,			model_name=SCREAMING_SNAKE_CASE__							,			num_train_epochs=1							,			distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str=SCREAMING_SNAKE_CASE__							,			predict_with_generate=SCREAMING_SNAKE_CASE__							,			do_train=SCREAMING_SNAKE_CASE__							,			do_eval=SCREAMING_SNAKE_CASE__							,			do_predict=SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history

						if not do_eval:
										return

						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]

						__lowerCamelCase    					=	eval_metrics[0]
						if predict_with_generate:
										assert "eval_bleu" in first_step_stats

										__lowerCamelCase    					=	eval_metrics[-1]
										assert isinstance(last_step_stats['''eval_bleu''']							,			SCREAMING_SNAKE_CASE__  )
										assert not math.isnan(float(last_step_stats['''eval_loss''']  )  ), "eval_loss must not be `nan`"






		@require_torch_non_multi_gpu
		def  __A       (		self		:							Optional[int]  )     ->							int:
						self.run_seqaseq_quick()






		@require_torch_multi_gpu
		def  __A       (		self		:							int  )     ->							List[str]:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__  )






		@require_torch_multi_gpu
		def  __A       (		self		:							Optional[Any]  )     ->							Tuple:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Dict  )     ->							Tuple:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp simple'''  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Optional[int]  )     ->							List[str]:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp simple --fp16'''  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Tuple  )     ->							Any:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp zero_dp_2'''							,			predict_with_generate=SCREAMING_SNAKE_CASE__  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Dict  )     ->							Tuple:
						self.run_seqaseq_quick(
						    distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''							,			predict_with_generate=SCREAMING_SNAKE_CASE__  )






		@require_apex
		@require_torch_gpu
		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
						# program and it breaks other tests that run from the same pytest worker, therefore until this is
						# sorted out it must be run only in an external program, that is distributed=True in this
						# test and only under one or more gpus - if we want cpu will need to make a special test
						#
						# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
						# 2nd main() call it botches the future eval.
						#
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--fp16 --fp16_backend=apex'''  )
						# test 2nd time - was getting eval_loss': nan'
						# to reproduce the problem set distributed=False
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--fp16 --fp16_backend=apex'''  )






		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']  )
		@require_torch_multi_gpu
		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Optional[Any]:
						# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
						__lowerCamelCase    					=	{
						    # test with the default log_level - should be info and thus log info once
						    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
						    # test with low log_level and log_level_replica - should be noisy on all processes
						    # now the info string should appear twice on 2 processes
						    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
						    # test with high log_level and low log_level_replica
						    # now the info string should appear once only on the replica
						    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
						    # test with high log_level and log_level_replica - should be quiet on all processes
						    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
						}

						__lowerCamelCase    					=	experiments[experiment_id]
						__lowerCamelCase    					=	{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
						__lowerCamelCase    					=	'''Running training'''
						with CaptureStderr() as cl:
										self.run_seqaseq_quick(**SCREAMING_SNAKE_CASE__							,			extra_args_str=data['''extra_args_str''']  )
						__lowerCamelCase    					=	len(re.findall(SCREAMING_SNAKE_CASE__							,			cl.err  )  )
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			data['''n_matches''']  )






		@slow
		def  __A       (		self		:							Any  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.run_trainer(
						    eval_steps=2							,			max_len=1_28							,			model_name=SCREAMING_SNAKE_CASE__							,			learning_rate=3e-4							,			num_train_epochs=10							,			distributed=SCREAMING_SNAKE_CASE__							,			)

						# Check metrics
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]
						__lowerCamelCase    					=	eval_metrics[0]
						__lowerCamelCase    					=	eval_metrics[-1]

						assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
						assert isinstance(last_step_stats['''eval_bleu''']							,			SCREAMING_SNAKE_CASE__  )

						# test if do_predict saves generations and metrics
						__lowerCamelCase    					=	os.listdir(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	{os.path.basename(SCREAMING_SNAKE_CASE__  ) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents






		@slow
		@require_bitsandbytes
		def  __A       (		self		:							Optional[int]  )     ->							str:
						from transformers.training_args import OptimizerNames

						def train_and_return_metrics(SCREAMING_SNAKE_CASE__		:							str  ) -> Tuple[int, float]:
										__lowerCamelCase    					=	'''--skip_memory_metrics 0'''

										__lowerCamelCase    					=	self.run_trainer(
										    max_len=1_28							,			model_name=SCREAMING_SNAKE_CASE__							,			learning_rate=3e-4							,			num_train_epochs=1							,			optim=SCREAMING_SNAKE_CASE__							,			distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str=SCREAMING_SNAKE_CASE__							,			do_eval=SCREAMING_SNAKE_CASE__							,			do_predict=SCREAMING_SNAKE_CASE__							,			n_gpus_to_use=1							,			)

										# Check metrics
										__lowerCamelCase    					=	TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20  )
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20  )

										__lowerCamelCase    					=	logs[0]['''train_loss''']
										return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

						__lowerCamelCase    					=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						__lowerCamelCase    					=	gpu_peak_mem_orig + gpu_alloc_mem_orig
						__lowerCamelCase    					=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						__lowerCamelCase    					=	gpu_total_mem_orig - gpu_total_mem_bnb

						# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
						# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
						# in 2 bytes and the diff in optim memory usage is derived as so:
						#
						# - normal 25*8=~200MB (8 bytes per param)
						# - bnb    25*2= ~50MB (2 bytes per param)
						#
						# Thus we should expect ~150MB total memory saved.
						#
						# Peak memory should be the same - the total should be different by about that same margin
						#
						# After leaving a small margin to accommodate for differences between gpus let's check
						# that we have at least 120MB in savings
						__lowerCamelCase    					=	1_20

						# uncomment the following if this test starts failing - requires py38 for a new print feature
						# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
						# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
						# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
						# print(f"{gpu_alloc_mem_diff=}MB")
						# print(f"{gpu_peak_mem_diff=}MB")
						# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
						# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

						self.assertGreater(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
						    f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
						    f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''							,			)

						self.assertGreater(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
						    f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
						    f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''							,			)

						self.assertEqual(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''  )







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							float = 3e-3							,			SCREAMING_SNAKE_CASE__		:							str = "adafactor"							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							int = 0							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							int = None							,			)     ->							List[Any]:
						__lowerCamelCase    					=	self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
						__lowerCamelCase    					=	self.get_auto_remove_tmp_dir()
						__lowerCamelCase    					=	f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(SCREAMING_SNAKE_CASE__  )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(SCREAMING_SNAKE_CASE__  )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

						__lowerCamelCase    					=	f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(SCREAMING_SNAKE_CASE__  )}
        '''.split()

						__lowerCamelCase    					=	'''
            --do_predict
        '''.split()

						__lowerCamelCase    					=	[]
						if do_train:
										args += args_train

						if do_eval:
										args += args_eval

						if do_predict:
										args += args_predict

						if predict_with_generate:
										args += "--predict_with_generate".split()

						if do_train:
										if optim == "adafactor":
														args += "--adafactor".split()
										else:
														args += f'''--optim {optim}'''.split()

						if extra_args_str is not None:
										args += extra_args_str.split()

						if distributed:
										if n_gpus_to_use is None:
														__lowerCamelCase    					=	get_gpu_count()
										__lowerCamelCase    					=	get_torch_dist_unique_port()
										__lowerCamelCase    					=	f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
										__lowerCamelCase    					=	[sys.executable] + distributed_args + args
										# keep for quick debug
										# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
										execute_subprocess_async(SCREAMING_SNAKE_CASE__							,			env=self.get_env()  )
						else:
										__lowerCamelCase    					=	['''run_translation.py'''] + args
										with patch.object(SCREAMING_SNAKE_CASE__							,			'''argv'''							,			SCREAMING_SNAKE_CASE__  ):
														main()

						return output_dir



