

import argparse
import json
from pathlib import Path

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

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					:  str             =      logging.get_logger(__name__)
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   DPTConfig(embedding_type='''hybrid'''    )

     if "large" in checkpoint_url:
          SCREAMING_SNAKE_CASE__   =   10_24
          SCREAMING_SNAKE_CASE__   =   40_96
          SCREAMING_SNAKE_CASE__   =   24
          SCREAMING_SNAKE_CASE__   =   16
          SCREAMING_SNAKE_CASE__   =   [5, 11, 17, 23]
          SCREAMING_SNAKE_CASE__   =   [2_56, 5_12, 10_24, 10_24]
          SCREAMING_SNAKE_CASE__   =   (1, 3_84, 3_84)

     if "nyu" or "midas" in checkpoint_url:
          SCREAMING_SNAKE_CASE__   =   7_68
          SCREAMING_SNAKE_CASE__   =   [1, 1, 1, 0.5]
          SCREAMING_SNAKE_CASE__   =   [2_56, 5_12, 7_68, 7_68]
          SCREAMING_SNAKE_CASE__   =   1_50
          SCREAMING_SNAKE_CASE__   =   16
          SCREAMING_SNAKE_CASE__   =   (1, 3_84, 3_84)
          SCREAMING_SNAKE_CASE__   =   False
          SCREAMING_SNAKE_CASE__   =   '''project'''

     if "ade" in checkpoint_url:
          SCREAMING_SNAKE_CASE__   =   True
          SCREAMING_SNAKE_CASE__   =   7_68
          SCREAMING_SNAKE_CASE__   =   [1, 1, 1, 0.5]
          SCREAMING_SNAKE_CASE__   =   1_50
          SCREAMING_SNAKE_CASE__   =   16
          SCREAMING_SNAKE_CASE__   =   '''huggingface/label-files'''
          SCREAMING_SNAKE_CASE__   =   '''ade20k-id2label.json'''
          SCREAMING_SNAKE_CASE__   =   json.load(open(cached_download(hf_hub_url(_A				,		_A				,		repo_type='''dataset'''    )    )				,		'''r'''    )    )
          SCREAMING_SNAKE_CASE__   =   {int(_A    ): v for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__   =   idalabel
          SCREAMING_SNAKE_CASE__   =   {v: k for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__   =   [1, 1_50, 4_80, 4_80]

     return config, expected_shape
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
     for k in ignore_keys:
          state_dict.pop(_A				,		_A    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.model'''				,		'''dpt.encoder'''    )
     if "pretrained.model" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.model'''				,		'''dpt.embeddings'''    )
     if "patch_embed" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''patch_embed'''				,		''''''    )
     if "pos_embed" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pos_embed'''				,		'''position_embeddings'''    )
     if "attn.proj" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''attn.proj'''				,		'''attention.output.dense'''    )
     if "proj" in name and "project" not in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''proj'''				,		'''projection'''    )
     if "blocks" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''blocks'''				,		'''layer'''    )
     if "mlp.fc1" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''mlp.fc1'''				,		'''intermediate.dense'''    )
     if "mlp.fc2" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''mlp.fc2'''				,		'''output.dense'''    )
     if "norm1" in name and "backbone" not in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''norm1'''				,		'''layernorm_before'''    )
     if "norm2" in name and "backbone" not in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''norm2'''				,		'''layernorm_after'''    )
     if "scratch.output_conv" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''scratch.output_conv'''				,		'''head'''    )
     if "scratch" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''scratch'''				,		'''neck'''    )
     if "layer1_rn" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''layer1_rn'''				,		'''convs.0'''    )
     if "layer2_rn" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''layer2_rn'''				,		'''convs.1'''    )
     if "layer3_rn" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''layer3_rn'''				,		'''convs.2'''    )
     if "layer4_rn" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''layer4_rn'''				,		'''convs.3'''    )
     if "refinenet" in name:
          SCREAMING_SNAKE_CASE__   =   int(name[len('''neck.refinenet'''    ) : len('''neck.refinenet'''    ) + 1]    )
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          SCREAMING_SNAKE_CASE__   =   name.replace(F'''refinenet{layer_idx}'''				,		F'''fusion_stage.layers.{abs(layer_idx-4    )}'''    )
     if "out_conv" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''out_conv'''				,		'''projection'''    )
     if "resConfUnit1" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''resConfUnit1'''				,		'''residual_layer1'''    )
     if "resConfUnit2" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''resConfUnit2'''				,		'''residual_layer2'''    )
     if "conv1" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''conv1'''				,		'''convolution1'''    )
     if "conv2" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''conv2'''				,		'''convolution2'''    )
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess1.0.project.0'''				,		'''neck.reassemble_stage.readout_projects.0.0'''    )
     if "pretrained.act_postprocess2.0.project.0" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess2.0.project.0'''				,		'''neck.reassemble_stage.readout_projects.1.0'''    )
     if "pretrained.act_postprocess3.0.project.0" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess3.0.project.0'''				,		'''neck.reassemble_stage.readout_projects.2.0'''    )
     if "pretrained.act_postprocess4.0.project.0" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess4.0.project.0'''				,		'''neck.reassemble_stage.readout_projects.3.0'''    )

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess1.3'''				,		'''neck.reassemble_stage.layers.0.projection'''    )
     if "pretrained.act_postprocess1.4" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess1.4'''				,		'''neck.reassemble_stage.layers.0.resize'''    )
     if "pretrained.act_postprocess2.3" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess2.3'''				,		'''neck.reassemble_stage.layers.1.projection'''    )
     if "pretrained.act_postprocess2.4" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess2.4'''				,		'''neck.reassemble_stage.layers.1.resize'''    )
     if "pretrained.act_postprocess3.3" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess3.3'''				,		'''neck.reassemble_stage.layers.2.projection'''    )
     if "pretrained.act_postprocess4.3" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess4.3'''				,		'''neck.reassemble_stage.layers.3.projection'''    )
     if "pretrained.act_postprocess4.4" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained.act_postprocess4.4'''				,		'''neck.reassemble_stage.layers.3.resize'''    )
     if "pretrained" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''pretrained'''				,		'''dpt'''    )
     if "bn" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''bn'''				,		'''batch_norm'''    )
     if "head" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''head'''				,		'''head.head'''    )
     if "encoder.norm" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''encoder.norm'''				,		'''layernorm'''    )
     if "auxlayer" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''auxlayer'''				,		'''auxiliary_head.head'''    )
     if "backbone" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''backbone'''				,		'''backbone.bit.encoder'''    )

     if ".." in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''..'''				,		'''.'''    )

     if "stem.conv" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''stem.conv'''				,		'''bit.embedder.convolution'''    )
     if "blocks" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''blocks'''				,		'''layers'''    )
     if "convolution" in name and "backbone" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''convolution'''				,		'''conv'''    )
     if "layer" in name and "backbone" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''layer'''				,		'''layers'''    )
     if "backbone.bit.encoder.bit" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''backbone.bit.encoder.bit'''				,		'''backbone.bit'''    )
     if "embedder.conv" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''embedder.conv'''				,		'''embedder.convolution'''    )
     if "backbone.bit.encoder.stem.norm" in name:
          SCREAMING_SNAKE_CASE__   =   name.replace('''backbone.bit.encoder.stem.norm'''				,		'''backbone.bit.embedder.norm'''    )
     return name
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     for i in range(config.num_hidden_layers    ):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE__   =   state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''    )
          SCREAMING_SNAKE_CASE__   =   state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''    )
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE__   =   in_proj_weight[: config.hidden_size, :]
          SCREAMING_SNAKE_CASE__   =   in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE__   =   in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE__   =   in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE__   =   in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE__   =   in_proj_bias[-config.hidden_size :]
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__   =   Image.open(requests.get(_A				,		stream=_A    ).raw    )
     return im
@torch.no_grad()
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   get_dpt_config(_A    )
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     SCREAMING_SNAKE_CASE__   =   torch.load(_A				,		map_location='''cpu'''    )
     # remove certain keys
     remove_ignore_keys_(_A    )
     # rename keys
     for key in state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__   =   state_dict.pop(_A    )
          SCREAMING_SNAKE_CASE__   =   val
     # read in qkv matrices
     read_in_q_k_v(_A				,		_A    )

     # load HuggingFace model
     SCREAMING_SNAKE_CASE__   =   DPTForSemanticSegmentation(_A    ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_A    )
     model.load_state_dict(_A    )
     model.eval()

     # Check outputs on an image
     SCREAMING_SNAKE_CASE__   =   4_80 if '''ade''' in checkpoint_url else 3_84
     SCREAMING_SNAKE_CASE__   =   DPTImageProcessor(size=_A    )

     SCREAMING_SNAKE_CASE__   =   prepare_img()
     SCREAMING_SNAKE_CASE__   =   image_processor(_A				,		return_tensors='''pt'''    )

     # forward pass
     SCREAMING_SNAKE_CASE__   =   model(**_A    ).logits if '''ade''' in checkpoint_url else model(**_A    ).predicted_depth

     if show_prediction:
          SCREAMING_SNAKE_CASE__   =   (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1    )				,		size=(image.size[1], image.size[0])				,		mode='''bicubic'''				,		align_corners=_A				,		)
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 2_55    ).show()

     if pytorch_dump_folder_path is not None:
          Path(_A    ).mkdir(exist_ok=_A    )
          print(F'''Saving model to {pytorch_dump_folder_path}'''    )
          model.save_pretrained(_A    )
          print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
          image_processor.save_pretrained(_A    )

     if push_to_hub:
          model.push_to_hub('''ybelkada/dpt-hybrid-midas'''    )
          image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  List[str]             =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
           type=str,
           help='''URL of the original DPT checkpoint you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=False,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument(
           '''--push_to_hub''',
           action='''store_true''',
       )
       parser.add_argument(
           '''--model_name''',
           default='''dpt-large''',
           type=str,
           help='''Name of the model, in case you\'re pushing to the hub.''',
       )
       parser.add_argument(
           '''--show_prediction''',
           action='''store_true''',
       )

       _SCREAMING_SNAKE_CASE					:  int             =      parser.parse_args()
       convert_dpt_checkpoint(
           args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
       )






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  def __init__(							self						:		List[str]     ,       __lowerCamelCase						:		Tuple=0.01     ,       __lowerCamelCase						:		Optional[Any]=1000 )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   p_stop
       SCREAMING_SNAKE_CASE__   =   max_length







  def __iter__(							self						:		List[str] )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   0
       SCREAMING_SNAKE_CASE__   =   False
       while not stop and count < self.max_length:
            yield count
            count += 1
            SCREAMING_SNAKE_CASE__   =   random.random() < self.p_stop






class 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Any=False     ,       __lowerCamelCase						:		Optional[int]=True )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   [
           BatchSamplerShard(__lowerCamelCase     ,       2     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )
           for i in range(2 )
       ]
       SCREAMING_SNAKE_CASE__   =   [list(__lowerCamelCase ) for batch_sampler_shard in batch_sampler_shards]
       if not split_batches:
            self.assertListEqual([len(__lowerCamelCase ) for shard in batch_sampler_shards]     ,       [len(__lowerCamelCase ) for e in expected] )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Any )		->							Optional[Any]:
       # Check the shards when the dataset is a round multiple of total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1, 0]], [[1, 0, 1]]]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[int] )		->							int:
       # Check the shards when the dataset is a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size or num_processes.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1]], [[0, 1]]]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							Dict:
       # Check the shards when the dataset is a round multiple of total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1]], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		List[str] )		->							Tuple:
       # Check the shards when the dataset is a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size or num_processes.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1]], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[int] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
       SCREAMING_SNAKE_CASE__   =   [BatchSamplerShard(__lowerCamelCase     ,       2     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase ) for i in range(2 )]

       self.assertEqual(len(batch_sampler_shards[0] )     ,       3 )
       self.assertEqual(len(batch_sampler_shards[1] )     ,       2 )

       self.assertListEqual(list(batch_sampler_shards[0] )     ,       [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
       self.assertListEqual(list(batch_sampler_shards[1] )     ,       [[3, 4], [9, 10, 11]] )






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Tuple=False     ,       __lowerCamelCase						:		Optional[int]=2     ,       __lowerCamelCase						:		Dict=False )		->							str:
       random.seed(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   list(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   [
           IterableDatasetShard(
               __lowerCamelCase     ,       batch_size=__lowerCamelCase     ,       drop_last=__lowerCamelCase     ,       num_processes=__lowerCamelCase     ,       process_index=__lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       )
           for i in range(__lowerCamelCase )
       ]
       SCREAMING_SNAKE_CASE__   =   []
       for iterable_dataset_shard in iterable_dataset_shards:
            # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
            random.seed(__lowerCamelCase )
            iterable_dataset_lists.append(list(__lowerCamelCase ) )

       SCREAMING_SNAKE_CASE__   =   batch_size // num_processes if split_batches else batch_size
       # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
       SCREAMING_SNAKE_CASE__   =   iterable_dataset_lists[0]
       for l in iterable_dataset_lists[1:]:
            self.assertEqual(len(__lowerCamelCase )     ,       len(__lowerCamelCase ) )
            self.assertTrue(len(__lowerCamelCase ) % shard_batch_size == 0 )

       SCREAMING_SNAKE_CASE__   =   []
       for idx in range(0     ,       len(__lowerCamelCase )     ,       __lowerCamelCase ):
            for l in iterable_dataset_lists:
                 observed += l[idx : idx + shard_batch_size]

       if not drop_last:
            while len(__lowerCamelCase ) < len(__lowerCamelCase ):
                 reference += reference
       self.assertListEqual(__lowerCamelCase     ,       reference[: len(__lowerCamelCase )] )






  def 				lowercase_		(							self						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   42
       SCREAMING_SNAKE_CASE__   =   RandomIterableDataset()

       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Edge case with a very small dataset
       SCREAMING_SNAKE_CASE__   =   RandomIterableDataset(max_length=2 )

       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(16 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   SkipBatchSampler(__lowerCamelCase     ,       2 )
       self.assertListEqual(list(__lowerCamelCase )     ,       [[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 				lowercase_		(							self						:		Union[str, Any] )		->							str:
       SCREAMING_SNAKE_CASE__   =   SkipDataLoader(list(range(16 ) )     ,       batch_size=4     ,       skip_batches=2 )
       self.assertListEqual([t.tolist() for t in dataloader]     ,       [[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 				lowercase_		(							self						:		Dict )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   DataLoader(list(range(16 ) )     ,       batch_size=4 )
       SCREAMING_SNAKE_CASE__   =   skip_first_batches(__lowerCamelCase     ,       num_batches=2 )
       self.assertListEqual([t.tolist() for t in new_dataloader]     ,       [[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 				lowercase_		(							self						:		List[Any] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   DataLoaderShard(list(range(16 ) )     ,       batch_size=4 )
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )

       # Test it also works on the second iteration
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )







  def 				lowercase_		(							self						:		Union[str, Any] )		->							str:
       Accelerator()
       SCREAMING_SNAKE_CASE__   =   DataLoaderDispatcher(range(16 )     ,       batch_size=4 )
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )

       # Test it also works on the second iteration
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )


