'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:     List[str]  =logging.get_logger(__name__)





def 	lowerCAmelCase_				(					snake_case_       : List[Any]			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   SwinConfig(
         embed_dim=1_92					,    depths=(2, 2, 18, 2)					,    num_heads=(6, 12, 24, 48)					,    window_size=12					,    out_features=["stage2", "stage3", "stage4"]					,    )

     UpperCAmelCase_           =   DetaConfig(
         backbone_config=snake_case_					,    num_queries=9_00					,    encoder_ffn_dim=20_48					,    decoder_ffn_dim=20_48					,    num_feature_levels=5					,    assign_first_stage=snake_case_					,    with_box_refine=snake_case_					,    two_stage=snake_case_					,    )

     # set labels
     UpperCAmelCase_           =   "huggingface/label-files"
     if "o365" in model_name:
          UpperCAmelCase_           =   3_66
          UpperCAmelCase_           =   "object365-id2label.json"
     else:
          UpperCAmelCase_           =   91
          UpperCAmelCase_           =   "coco-detection-id2label.json"

     UpperCAmelCase_           =   num_labels
     UpperCAmelCase_           =   json.load(open(cached_download(hf_hub_url(snake_case_					,    snake_case_					,    repo_type="dataset"			)			)					,    "r"			)			)
     UpperCAmelCase_           =   {int(snake_case_			): v for k, v in idalabel.items()}
     UpperCAmelCase_           =   idalabel
     UpperCAmelCase_           =   {v: k for k, v in idalabel.items()}

     return config





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   []

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")			)
     rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")			)
     rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")			)
     rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")			)
     # stages
     for i in range(len(config.backbone_config.depths			)			):
          for j in range(config.backbone_config.depths[i]			):
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")			)

          if i < 3:
               rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")			)

     rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")			)
     rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")			)
     rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")			)
     rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")			)
     rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")			)
     rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")			)

     # transformer encoder
     for i in range(config.encoder_layers			):
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")			)

     # transformer decoder
     for i in range(config.decoder_layers			):
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")			)

     # fmt: on

     return rename_keys





def 	lowerCAmelCase_				(					snake_case_       : Tuple					,    snake_case_       : int					,    snake_case_       : Optional[int]			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   dct.pop(snake_case_			)
     UpperCAmelCase_           =   val





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Optional[Any]			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
     for i in range(len(backbone_config.depths			)			):
          UpperCAmelCase_           =   num_features[i]
          for j in range(backbone_config.depths[i]			):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               UpperCAmelCase_           =   state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""			)
               UpperCAmelCase_           =   state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""			)
               # next, add query, keys and values (in that order) to the state dict
               UpperCAmelCase_           =   in_proj_weight[:dim, :]
               UpperCAmelCase_           =   in_proj_bias[: dim]
               UpperCAmelCase_           =   in_proj_weight[
                   dim : dim * 2, :
               ]
               UpperCAmelCase_           =   in_proj_bias[
                   dim : dim * 2
               ]
               UpperCAmelCase_           =   in_proj_weight[
                   -dim :, :
               ]
               UpperCAmelCase_           =   in_proj_bias[-dim :]
               # fmt: on





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Dict			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   config.d_model
     for i in range(config.decoder_layers			):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase_           =   state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
          UpperCAmelCase_           =   state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""			)
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_           =   in_proj_weight[:hidden_size, :]
          UpperCAmelCase_           =   in_proj_bias[:hidden_size]
          UpperCAmelCase_           =   in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          UpperCAmelCase_           =   in_proj_bias[hidden_size : hidden_size * 2]
          UpperCAmelCase_           =   in_proj_weight[-hidden_size:, :]
          UpperCAmelCase_           =   in_proj_bias[-hidden_size:]





def 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




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

     return im





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : str					,    snake_case_       : str			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   get_deta_config(snake_case_			)

     # load original state dict
     if model_name == "deta-swin-large":
          UpperCAmelCase_           =   hf_hub_download(repo_id="nielsr/deta-checkpoints"					,    filename="adet_swin_ft.pth"			)
     elif model_name == "deta-swin-large-o365":
          UpperCAmelCase_           =   hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"					,    filename="deta_swin_pt_o365.pth"			)
     else:
          raise ValueError(f"""Model name {model_name} not supported"""			)

     UpperCAmelCase_           =   torch.load(snake_case_					,    map_location="cpu"			)["model"]

     # original state dict
     for name, param in state_dict.items():
          print(snake_case_					,    param.shape			)

     # rename keys
     UpperCAmelCase_           =   create_rename_keys(snake_case_			)
     for src, dest in rename_keys:
          rename_key(snake_case_					,    snake_case_					,    snake_case_			)
     read_in_swin_q_k_v(snake_case_					,    config.backbone_config			)
     read_in_decoder_q_k_v(snake_case_					,    snake_case_			)

     # fix some prefixes
     for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
               UpperCAmelCase_           =   state_dict.pop(snake_case_			)
               UpperCAmelCase_           =   val
          if "input_proj" in key:
               UpperCAmelCase_           =   state_dict.pop(snake_case_			)
               UpperCAmelCase_           =   val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               UpperCAmelCase_           =   state_dict.pop(snake_case_			)
               UpperCAmelCase_           =   val

    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_           =   DetaForObjectDetection(snake_case_			)
     model.load_state_dict(snake_case_			)
     model.eval()

     UpperCAmelCase_           =   "cuda" if torch.cuda.is_available() else "cpu"
     model.to(snake_case_			)

     # load image processor
     UpperCAmelCase_           =   DetaImageProcessor(format="coco_detection"			)

     # verify our conversion on image
     UpperCAmelCase_           =   prepare_img()
     UpperCAmelCase_           =   processor(images=snake_case_					,    return_tensors="pt"			)
     UpperCAmelCase_           =   encoding["pixel_values"]
     UpperCAmelCase_           =   model(pixel_values.to(snake_case_			)			)

     # verify logits
     print("Logits:"					,    outputs.logits[0, :3, :3]			)
     print("Boxes:"					,    outputs.pred_boxes[0, :3, :3]			)
     if model_name == "deta-swin-large":
          UpperCAmelCase_           =   torch.tensor(
              [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
          UpperCAmelCase_           =   torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
     elif model_name == "deta-swin-large-o365":
          UpperCAmelCase_           =   torch.tensor(
              [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
          UpperCAmelCase_           =   torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
     assert torch.allclose(outputs.logits[0, :3, :3]					,    expected_logits.to(snake_case_			)					,    atol=1E-4			)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,    expected_boxes.to(snake_case_			)					,    atol=1E-4			)
     print("Everything ok!"			)

     if pytorch_dump_folder_path:
          # Save model and processor
          logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""			)
          Path(snake_case_			).mkdir(exist_ok=snake_case_			)
          model.save_pretrained(snake_case_			)
          processor.save_pretrained(snake_case_			)

     # Push to hub
     if push_to_hub:
          print("Pushing model and processor to hub..."			)
          model.push_to_hub(f"""jozhang97/{model_name}"""			)
          processor.push_to_hub(f"""jozhang97/{model_name}"""			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Dict  =argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        type=str,
        default='deta-swin-large',
        choices=['deta-swin-large', 'deta-swin-large-o365'],
        help='Name of the 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 or not to push the converted model to the 🤗 hub.'
    )
    SCREAMING_SNAKE_CASE_:     Optional[int]  =parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)




def      lowercase__      (     __snake_case    :							List[Any]     ,       __snake_case    :							List[str]=False  ):


					'''simple docstring'''


					try:
										UpperCAmelCase_    :      int            =						os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										UpperCAmelCase_    :      Optional[int]            =						default
					else:
										# KEY is set, convert it to True or False.
										try:
															UpperCAmelCase_    :      List[Any]            =						strtobool(__snake_case  )
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(F"If set, {key} must be yes or no."  )
					return _value


__UpperCAmelCase								=						parse_flag_from_env('RUN_SLOW', default=False)




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					return unittest.skip('Test was skipped'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(_run_slow_tests     ,       'test is slow'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					return unittest.skipUnless(not torch.cuda.is_available()     ,       'test requires only a CPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.cuda.is_available()     ,       'test requires a GPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					return unittest.skipUnless(is_xpu_available()     ,       'test requires a XPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_mps_available()     ,       'test requires a `mps` backend support in `torch`'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available()     ,       'test requires the Hugging Face suite'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_bnb_available()     ,       'test requires the bitsandbytes library'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					return unittest.skipUnless(is_tpu_available()     ,       'test requires TPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.cuda.device_count() == 1     ,       'test requires a GPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.xpu.device_count() == 1     ,       'test requires a XPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.cuda.device_count() > 1     ,       'test requires multiple GPUs'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.xpu.device_count() > 1     ,       'test requires multiple XPUs'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					return unittest.skipUnless(is_safetensors_available()     ,       'test requires safetensors'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(is_deepspeed_available()     ,       'test requires DeepSpeed'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[Any]  ):


					'''simple docstring'''


					return unittest.skipUnless(is_torch_version('>='     ,       '1.12.0'  )     ,       'test requires torch version >= 1.12.0'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict=None     ,       __snake_case    :							Dict=None  ):


					'''simple docstring'''


					if test_case is None:
										return partial(__snake_case     ,       version=__snake_case  )
					return unittest.skipUnless(is_torch_version('>='     ,       __snake_case  )     ,       F"test requires torch version >= {version}"  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_tensorboard_available()     ,       'test requires Tensorboard'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					return unittest.skipUnless(is_wandb_available()     ,       'test requires wandb'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_comet_ml_available()     ,       'test requires comet_ml'  )(__snake_case  )


__UpperCAmelCase								=						(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def      lowercase__      (     __snake_case    :							List[Any]  ):


					'''simple docstring'''


					return unittest.skipUnless(
					    _atleast_one_tracker_available     ,       'test requires at least one tracker to be available and for `comet_ml` to not be installed'     ,       )(__snake_case  )



class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  True



		@classmethod
		def 					__UpperCAmelCase			(							cls   )	->							Union[str, Any]:
							UpperCAmelCase_    :      List[Any]            =						tempfile.mkdtemp()



		@classmethod
		def 					__UpperCAmelCase			(							cls   )	->							List[str]:
							if os.path.exists(cls.tmpdir   ):
												shutil.rmtree(cls.tmpdir   )



		def 					__UpperCAmelCase			(							self   )	->							str:
							if self.clear_on_setup:
												for path in Path(self.tmpdir   ).glob('**/*'   ):
																	if path.is_file():
																						path.unlink()
																	elif path.is_dir():
																						shutil.rmtree(_UpperCamelCase   )





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Any:
							UpperCAmelCase_    :      List[Any]            =						mocks if isinstance(_UpperCamelCase				,		(tuple, list)   ) else [mocks]
							for m in self.mocks:
												m.start()
												self.addCleanup(m.stop   )





def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						AcceleratorState()
					UpperCAmelCase_    :      str            =						tensor[None].clone().to(state.device  )
					UpperCAmelCase_    :      List[str]            =						gather(__snake_case  ).cpu()
					UpperCAmelCase_    :      List[Any]            =						tensor[0].cpu()
					for i in range(tensors.shape[0]  ):
										if not torch.equal(tensors[i]     ,       __snake_case  ):
															return False
					return True



class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Any:
							UpperCAmelCase_    :      str            =						returncode
							UpperCAmelCase_    :      Optional[Any]            =						stdout
							UpperCAmelCase_    :      Optional[Any]            =						stderr





async def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					while True:
										UpperCAmelCase_    :      Dict            =						await stream.readline()
										if line:
															callback(__snake_case  )
										else:
															break




async def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Dict=None     ,       __snake_case    :							str=None     ,       __snake_case    :							Dict=None     ,       __snake_case    :							List[str]=False     ,       __snake_case    :							Optional[int]=False  ):


					'''simple docstring'''


					if echo:
										print('\nRunning: '     ,       ' '.join(__snake_case  )  )

					UpperCAmelCase_    :      Optional[Any]            =						await asyncio.create_subprocess_exec(
					    cmd[0]     ,       *cmd[1:]     ,       stdin=__snake_case     ,       stdout=asyncio.subprocess.PIPE     ,       stderr=asyncio.subprocess.PIPE     ,       env=__snake_case     ,       )

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					UpperCAmelCase_    :      Any            =						[]
					UpperCAmelCase_    :      str            =						[]

					def tee(__snake_case    :							Dict     ,       __snake_case    :							Union[str, Any]     ,       __snake_case    :							Tuple     ,       __snake_case    :							Optional[int]=""  ):
										UpperCAmelCase_    :      List[str]            =						line.decode('utf-8'  ).rstrip()
										sink.append(__snake_case  )
										if not quiet:
															print(__snake_case     ,       __snake_case     ,       file=__snake_case  )

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout     ,       lambda __snake_case  : tee(__snake_case     ,       __snake_case     ,       sys.stdout     ,       label='stdout:'  )  )  ),
					        asyncio.create_task(_read_stream(p.stderr     ,       lambda __snake_case  : tee(__snake_case     ,       __snake_case     ,       sys.stderr     ,       label='stderr:'  )  )  ),
					    ]     ,       timeout=__snake_case     ,       )
					return _RunOutput(await p.wait()     ,       __snake_case     ,       __snake_case  )




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							List[Any]=None     ,       __snake_case    :							str=None     ,       __snake_case    :							Tuple=180     ,       __snake_case    :							Dict=False     ,       __snake_case    :							Optional[Any]=True  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						asyncio.get_event_loop()
					UpperCAmelCase_    :      int            =						loop.run_until_complete(
					    _stream_subprocess(__snake_case     ,       env=__snake_case     ,       stdin=__snake_case     ,       timeout=__snake_case     ,       quiet=__snake_case     ,       echo=__snake_case  )  )

					UpperCAmelCase_    :      int            =						' '.join(__snake_case  )
					if result.returncode > 0:
										UpperCAmelCase_    :      int            =						'\n'.join(result.stderr  )
										raise RuntimeError(
										    F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
										    F"The combined stderr from workers follows:\n{stderr}"  )

					return result



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		pass





def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							List[Any]=False  ):


					'''simple docstring'''


					try:
										UpperCAmelCase_    :      List[Any]            =						subprocess.check_output(__snake_case     ,       stderr=subprocess.STDOUT  )
										if return_stdout:
															if hasattr(__snake_case     ,       'decode'  ):
																				UpperCAmelCase_    :      str            =						output.decode('utf-8'  )
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    F"Command `{' '.join(__snake_case  )}` failed with the following error:\n\n{e.output.decode()}"  ) from e






