

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_             =     []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
  )
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
  )
  rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
  rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
  rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
  rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
  )
  rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
  rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
  rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
  )
  rename_keys.append(
      (
          f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
          f"decoder.layers.{i}.encoder_attn.out_proj.weight",
      )
  )
  rename_keys.append(
      (
          f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
          f"decoder.layers.{i}.encoder_attn.out_proj.bias",
      )
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
  rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
  rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
  rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight")
  )

  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias")
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)
def   a   (    A__			:     List[Any]   ,     A__			:     Optional[Any]   ,     A__			:     int  )					->   int:





      """simple docstring"""



      _lowercase			 =state_dict.pop(A__  )
      _lowercase			 =val
def   a   (    A__			:     Dict  )					->   int:





      """simple docstring"""



      _lowercase			 =OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  _lowercase			 =key.replace('backbone.0.body'   ,     'backbone.conv_encoder.model'  )
                  _lowercase			 =value
            else:
                  _lowercase			 =value

      return new_state_dict
def   a   (    A__			:     Union[str, Any]   ,     A__			:     Optional[Any]=False  )					->   List[str]:





      """simple docstring"""



      _lowercase			 =''
      if is_panoptic:
            _lowercase			 ='conditional_detr.'

      # first: transformer encoder
      for i in range(6  ):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            _lowercase			 =state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
            _lowercase			 =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
            _lowercase			 =in_proj_weight[:256, :]
            _lowercase			 =in_proj_bias[:256]
            _lowercase			 =in_proj_weight[256:512, :]
            _lowercase			 =in_proj_bias[256:512]
            _lowercase			 =in_proj_weight[-256:, :]
            _lowercase			 =in_proj_bias[-256:]
def   a   (    )					->   str:





      """simple docstring"""



      _lowercase			 ='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase			 =Image.open(requests.get(A__   ,     stream=A__  ).raw  )

      return im
@torch.no_grad()
def   a   (    A__			:     Optional[int]   ,     A__			:     Dict  )					->   Dict:





      """simple docstring"""



      _lowercase			 =ConditionalDetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            _lowercase			 ='resnet101'
      if "dc5" in model_name:
            _lowercase			 =True
      _lowercase			 ='panoptic' in model_name
      if is_panoptic:
            _lowercase			 =250
      else:
            _lowercase			 =91
            _lowercase			 ='huggingface/label-files'
            _lowercase			 ='coco-detection-id2label.json'
            _lowercase			 =json.load(open(hf_hub_download(A__   ,     A__   ,     repo_type='dataset'  )   ,     'r'  )  )
            _lowercase			 ={int(A__  ): v for k, v in idalabel.items()}
            _lowercase			 =idalabel
            _lowercase			 ={v: k for k, v in idalabel.items()}

      # load image processor
      _lowercase			 ='coco_panoptic' if is_panoptic else 'coco_detection'
      _lowercase			 =ConditionalDetrImageProcessor(format=A__  )

      # prepare image
      _lowercase			 =prepare_img()
      _lowercase			 =image_processor(images=A__   ,     return_tensors='pt'  )
      _lowercase			 =encoding['pixel_values']

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

      # load original model from torch hub
      _lowercase			 =torch.hub.load('DeppMeng/ConditionalDETR'   ,     A__   ,     pretrained=A__  ).eval()
      _lowercase			 =conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  _lowercase			 ='conditional_detr.' + src
            rename_key(A__   ,     A__   ,     A__  )
      _lowercase			 =rename_backbone_keys(A__  )
      # query, key and value matrices need special treatment
      read_in_q_k_v(A__   ,     is_panoptic=A__  )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      _lowercase			 ='conditional_detr.model.' if is_panoptic else 'model.'
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith('conditional_detr'  )
                      and not key.startswith('class_labels_classifier'  )
                      and not key.startswith('bbox_predictor'  )
                  ):
                        _lowercase			 =state_dict.pop(A__  )
                        _lowercase			 =val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        _lowercase			 =state_dict.pop(A__  )
                        _lowercase			 =val
                  elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
                        continue
                  else:
                        _lowercase			 =state_dict.pop(A__  )
                        _lowercase			 =val
            else:
                  if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
                        _lowercase			 =state_dict.pop(A__  )
                        _lowercase			 =val
    # finally, create HuggingFace model and load state dict
      _lowercase			 =ConditionalDetrForSegmentation(A__  ) if is_panoptic else ConditionalDetrForObjectDetection(A__  )
      model.load_state_dict(A__  )
      model.eval()
      model.push_to_hub(repo_id=A__   ,     organization='DepuMeng'   ,     commit_message='Add model'  )
      # verify our conversion
      _lowercase			 =conditional_detr(A__  )
      _lowercase			 =model(A__  )
      assert torch.allclose(outputs.logits   ,     original_outputs['pred_logits']   ,     atol=1e-4  )
      assert torch.allclose(outputs.pred_boxes   ,     original_outputs['pred_boxes']   ,     atol=1e-4  )
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks   ,     original_outputs['pred_masks']   ,     atol=1e-4  )

      # Save model and image processor
      logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''  )
      Path(A__  ).mkdir(exist_ok=A__  )
      model.save_pretrained(A__  )
      image_processor.save_pretrained(A__  )


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

  parser.add_argument(
      '--model_name',
      default='conditional_detr_resnet50',
      type=str,
      help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  lowercase_             =     parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase_             =     'bert-base-cased'
lowercase_             =     'google/pegasus-xsum'
lowercase_             =     [' Sam ate lunch today.', 'Sams lunch ingredients.']
lowercase_             =     ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowercase_             =     'patrickvonplaten/t5-tiny-random'
lowercase_             =     'sshleifer/bart-tiny-random'
lowercase_             =     'sshleifer/tiny-mbart'
lowercase_             =     'sshleifer/tiny-marian-en-de'
def   a   (    A__			:     Path   ,     A__			:     list  )					->   Tuple:





      """simple docstring"""



      _lowercase			 ='\n'.join(A__  )
      Path(A__  ).open('w'  ).writelines(A__  )
def   a   (    A__			:     str  )					->   Union[str, Any]:





      """simple docstring"""



      for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(A__   ,     F'''{split}.source'''  )   ,     A__  )
            _dump_articles(os.path.join(A__   ,     F'''{split}.target'''  )   ,     A__  )
      return tmp_dir







class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]							,     )
  @slow
  def 						A__  (       self							,     lowerCAmelCase      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in ARTICLES      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in SUMMARIES      )
        _lowercase			 =4
        _lowercase			 =8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        _lowercase , _lowercase			 ='ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
        _lowercase			 =SeqaSeqDataset(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=lowerCAmelCase							,     max_target_length=lowerCAmelCase							,     src_lang=lowerCAmelCase							,     tgt_lang=lowerCAmelCase							,     )
        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_size=2							,     collate_fn=train_dataset.collate_fn      )
        for batch in dataloader:
              assert isinstance(lowerCAmelCase							,     lowerCAmelCase      )
              assert batch["attention_mask"].shape == batch["input_ids"].shape
              # show that articles were trimmed.
              assert batch["input_ids"].shape[1] == max_src_len
              # show that targets are the same len
              assert batch["labels"].shape[1] == max_tgt_len
              if tok_name != MBART_TINY:
                    continue
              # check language codes in correct place
              _lowercase			 =shift_tokens_right(batch['labels']							,     tokenizer.pad_token_id      )
              assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
              assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
              assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
              assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

              break  # No need to test every batch




  @parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in ARTICLES      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in SUMMARIES      )
        _lowercase			 =4
        _lowercase			 =LegacySeqaSeqDataset(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=20							,     max_target_length=lowerCAmelCase							,     )
        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_size=2							,     collate_fn=train_dataset.collate_fn      )
        for batch in dataloader:
              assert batch["attention_mask"].shape == batch["input_ids"].shape
              # show that articles were trimmed.
              assert batch["input_ids"].shape[1] == max_len_source
              assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
              # show that targets were truncated
              assert batch["labels"].shape[1] == trunc_target  # Truncated
              assert max_len_target > trunc_target  # Truncated
              break  # No need to test every batch




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'      )

        _lowercase			 =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
        _lowercase			 =tmp_dir.joinpath('train.source'      ).open().readlines()
        _lowercase			 =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
        pack_data_dir(lowerCAmelCase							,     lowerCAmelCase							,     128							,     lowerCAmelCase      )
        _lowercase			 ={x.name for x in tmp_dir.iterdir()}
        _lowercase			 ={x.name for x in save_dir.iterdir()}
        _lowercase			 =save_dir.joinpath('train.source'      ).open().readlines()
        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
        assert len(lowerCAmelCase      ) < len(lowerCAmelCase      )
        assert len(lowerCAmelCase      ) == 1
        assert len(packed_examples[0]      ) == sum(len(lowerCAmelCase      ) for x in orig_examples      )
        assert orig_paths == new_paths




  @pytest.mark.skipif(not FAIRSEQ_AVAILABLE							,     reason='This test requires fairseq'      )
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        if not FAIRSEQ_AVAILABLE:
              return
        _lowercase , _lowercase , _lowercase			 =self._get_dataset(max_len=64      )
        _lowercase			 =64
        _lowercase			 =ds.make_dynamic_sampler(lowerCAmelCase							,     required_batch_size_multiple=lowerCAmelCase      )
        _lowercase			 =[len(lowerCAmelCase      ) for x in batch_sampler]
        assert len(set(lowerCAmelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(lowerCAmelCase      ) == len(lowerCAmelCase      )  # no dropped or added examples
        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_sampler=lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2      )
        _lowercase			 =[]
        _lowercase			 =[]
        for batch in data_loader:
              _lowercase			 =batch['input_ids'].shape
              _lowercase			 =src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              _lowercase			 =np.product(batch['input_ids'].shape      )
              num_src_per_batch.append(lowerCAmelCase      )
              if num_src_tokens > (max_tokens * 1.1):
                    failures.append(lowerCAmelCase      )
        assert num_src_per_batch[0] == max(lowerCAmelCase      )
        if failures:
              raise AssertionError(F'''too many tokens in {len(lowerCAmelCase      )} batches'''      )




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase , _lowercase , _lowercase			 =self._get_dataset(max_len=512      )
        _lowercase			 =2
        _lowercase			 =ds.make_sortish_sampler(lowerCAmelCase							,     shuffle=lowerCAmelCase      )

        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_size=lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2      )
        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_size=lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2							,     sampler=lowerCAmelCase      )

        _lowercase			 =tokenizer.pad_token_id

        def count_pad_tokens(lowerCAmelCase							,     lowerCAmelCase="input_ids"      ):
              return [batch[k].eq(lowerCAmelCase      ).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(lowerCAmelCase							,     k='labels'      )      ) < sum(count_pad_tokens(lowerCAmelCase							,     k='labels'      )      )
        assert sum(count_pad_tokens(lowerCAmelCase      )      ) < sum(count_pad_tokens(lowerCAmelCase      )      )
        assert len(lowerCAmelCase      ) == len(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase=1_000							,     lowerCAmelCase=128      )						->				Union[str, Any]:
        '''simple docstring'''






        if os.getenv('USE_REAL_DATA'							,     lowerCAmelCase      ):
              _lowercase			 ='examples/seq2seq/wmt_en_ro'
              _lowercase			 =max_len * 2 * 64
              if not Path(lowerCAmelCase      ).joinpath('train.len'      ).exists():
                    save_len_file(lowerCAmelCase							,     lowerCAmelCase      )
        else:
              _lowercase			 ='examples/seq2seq/test_data/wmt_en_ro'
              _lowercase			 =max_len * 4
              save_len_file(lowerCAmelCase							,     lowerCAmelCase      )

        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =SeqaSeqDataset(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=lowerCAmelCase							,     max_target_length=lowerCAmelCase							,     n_obs=lowerCAmelCase							,     )
        return ds, max_tokens, tokenizer




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase , _lowercase , _lowercase			 =self._get_dataset()
        _lowercase			 =set(DistributedSortishSampler(lowerCAmelCase							,     256							,     num_replicas=2							,     rank=0							,     add_extra_examples=lowerCAmelCase      )      )
        _lowercase			 =set(DistributedSortishSampler(lowerCAmelCase							,     256							,     num_replicas=2							,     rank=1							,     add_extra_examples=lowerCAmelCase      )      )
        assert idsa.intersection(lowerCAmelCase      ) == set()




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]							,     )
  def 						A__  (       self							,     lowerCAmelCase      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase							,     use_fast=lowerCAmelCase      )
        if tok_name == MBART_TINY:
              _lowercase			 =SeqaSeqDataset(
                  lowerCAmelCase							,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )							,     type_path='train'							,     max_source_length=4							,     max_target_length=8							,     src_lang='EN'							,     tgt_lang='FR'							,     )
              _lowercase			 =train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
              _lowercase			 =SeqaSeqDataset(
                  lowerCAmelCase							,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )							,     type_path='train'							,     max_source_length=4							,     max_target_length=8							,     )
              _lowercase			 =train_dataset.dataset_kwargs
              assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
              assert len(lowerCAmelCase      ) == 1 if tok_name == BART_TINY else len(lowerCAmelCase      ) == 0


