

'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =['image_processor', 'tokenizer']
    __UpperCamelCase         ='OwlViTImageProcessor'
    __UpperCamelCase         =('CLIPTokenizer', 'CLIPTokenizerFast')

    def __init__(			self :      Optional[int]							,    snake_case__ :      Union[str, Any]=None							,    snake_case__ :      Dict=None							,    **snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			None
     if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'							,    snake_case__							,    )
      SCREAMING_SNAKE_CASE								=			kwargs.pop('feature_extractor'    )

     SCREAMING_SNAKE_CASE								=			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'    )
     if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'    )

     super().__init__(snake_case__							,    snake_case__    )

    def __call__(			self :      Tuple							,    snake_case__ :      Tuple=None							,    snake_case__ :      Any=None							,    snake_case__ :      Tuple=None							,    snake_case__ :      Union[str, Any]="max_length"							,    snake_case__ :      int="np"							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     if text is None and query_images is None and images is None:
      raise ValueError(
          'You have to specify at least one text or query image or image. All three cannot be none.'    )

     if text is not None:
      if isinstance(snake_case__							,    snake_case__    ) or (isinstance(snake_case__							,    snake_case__    ) and not isinstance(text[0]							,    snake_case__    )):
       SCREAMING_SNAKE_CASE								=			[self.tokenizer(snake_case__							,    padding=snake_case__							,    return_tensors=snake_case__							,    **snake_case__    )]

      elif isinstance(snake_case__							,    snake_case__    ) and isinstance(text[0]							,    snake_case__    ):
       SCREAMING_SNAKE_CASE								=			[]

       # Maximum number of queries across batch
       SCREAMING_SNAKE_CASE								=			max([len(snake_case__    ) for t in text]    )

       # Pad all batch samples to max number of text queries
       for t in text:
        if len(snake_case__    ) != max_num_queries:
         SCREAMING_SNAKE_CASE								=			t + [' '] * (max_num_queries - len(snake_case__    ))

        SCREAMING_SNAKE_CASE								=			self.tokenizer(snake_case__							,    padding=snake_case__							,    return_tensors=snake_case__							,    **snake_case__    )
        encodings.append(snake_case__    )
      else:
       raise TypeError('Input text should be a string, a list of strings or a nested list of strings'    )

      if return_tensors == "np":
       SCREAMING_SNAKE_CASE								=			np.concatenate([encoding['input_ids'] for encoding in encodings]							,    axis=0    )
       SCREAMING_SNAKE_CASE								=			np.concatenate([encoding['attention_mask'] for encoding in encodings]							,    axis=0    )

      elif return_tensors == "jax" and is_flax_available():
       import jax.numpy as jnp

       SCREAMING_SNAKE_CASE								=			jnp.concatenate([encoding['input_ids'] for encoding in encodings]							,    axis=0    )
       SCREAMING_SNAKE_CASE								=			jnp.concatenate([encoding['attention_mask'] for encoding in encodings]							,    axis=0    )

      elif return_tensors == "pt" and is_torch_available():
       import torch

       SCREAMING_SNAKE_CASE								=			torch.cat([encoding['input_ids'] for encoding in encodings]							,    dim=0    )
       SCREAMING_SNAKE_CASE								=			torch.cat([encoding['attention_mask'] for encoding in encodings]							,    dim=0    )

      elif return_tensors == "tf" and is_tf_available():
       import tensorflow as tf

       SCREAMING_SNAKE_CASE								=			tf.stack([encoding['input_ids'] for encoding in encodings]							,    axis=0    )
       SCREAMING_SNAKE_CASE								=			tf.stack([encoding['attention_mask'] for encoding in encodings]							,    axis=0    )

      else:
       raise ValueError('Target return tensor type could not be returned'    )

      SCREAMING_SNAKE_CASE								=			BatchEncoding()
      SCREAMING_SNAKE_CASE								=			input_ids
      SCREAMING_SNAKE_CASE								=			attention_mask

     if query_images is not None:
      SCREAMING_SNAKE_CASE								=			BatchEncoding()
      SCREAMING_SNAKE_CASE								=			self.image_processor(
          snake_case__							,    return_tensors=snake_case__							,    **snake_case__    ).pixel_values
      SCREAMING_SNAKE_CASE								=			query_pixel_values

     if images is not None:
      SCREAMING_SNAKE_CASE								=			self.image_processor(snake_case__							,    return_tensors=snake_case__							,    **snake_case__    )

     if text is not None and images is not None:
      SCREAMING_SNAKE_CASE								=			image_features.pixel_values
      return encoding
     elif query_images is not None and images is not None:
      SCREAMING_SNAKE_CASE								=			image_features.pixel_values
      return encoding
     elif text is not None or query_images is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case__    )							,    tensor_type=snake_case__    )

    def UpperCamelCase			(			self :      Any							,    *snake_case__ :      Optional[int]							,    **snake_case__ :      List[Any]    ):

     """simple docstring"""






     return self.image_processor.post_process(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      Any							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     return self.image_processor.post_process_object_detection(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      Any							,    *snake_case__ :      List[str]							,    **snake_case__ :      Any    ):

     """simple docstring"""






     return self.image_processor.post_process_image_guided_detection(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      Dict							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     return self.tokenizer.batch_decode(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      Optional[int]							,    *snake_case__ :      Optional[int]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     return self.tokenizer.decode(*snake_case__							,    **snake_case__    )

    @property
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,    snake_case__							,    )
     return self.image_processor_class




    @property
    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,    snake_case__							,    )
     return self.image_processor




#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist


def        __lowerCAmelCase    (       *_UpperCamelCase		:  Optional[Any]	)		->							Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,							'r'	) as fh:
  fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_EX	)
  try:
   print(*_UpperCamelCase	)
  finally:
   fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_UN	)


a_    :						int    		=      int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
a_    :						str    		=      torch.device("cuda", local_rank)
a_    :						Optional[int]    		=      socket.gethostname()

a_    :						Union[str, Any]    		=      F"""[{hostname}-{local_rank}]"""

try:
      # test distributed
      dist.init_process_group("nccl")
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      a_    :						Dict    		=      dist.get_rank()
      a_    :						Any    		=      dist.get_world_size()

      printflock(F"""{gpu} is OK (global rank: {rank}/{world_size})""")

      dist.barrier()
      if rank == 0:
            printflock(F"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
      printflock(F"""{gpu} is broken""")
      raise

