


# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=0			):
							# Format the message.
							if name is None:
														__snake_case	:    Optional[Any]											=     None
							else:
														__snake_case	:    Dict											=     "." * max(0							,			spaces - 2			) + "# {:" + str(5_0 - spaces			) + "s}"
														__snake_case	:    Dict											=     fmt.format(__lowerCamelCase			)

							# Print and recurse (if needed).
							if isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														if msg is not None:
																					print(__lowerCamelCase			)
														for k in val.keys():
																					recursive_print(__lowerCamelCase							,			val[k]							,			spaces + 2			)
							elif isinstance(__lowerCamelCase							,			torch.Tensor			):
														print(__lowerCamelCase							,			":"							,			val.size()			)
							else:
														print(__lowerCamelCase							,			":"							,			__lowerCamelCase			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
							# for compatibility with later versions of NVIDIA Megatron-LM.
							# The inverse operation is performed inside Megatron-LM to read checkpoints:
							# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
							# If param is the weight tensor of the self-attention block, the returned tensor
							# will have to be transposed one more time to be read by HuggingFace GPT2.
							__snake_case	:    Union[str, Any]											=     param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														__snake_case	:    List[str]											=     (num_heads, hidden_size, num_splits) + input_shape[1:]
														__snake_case	:    int											=     param.view(*__lowerCamelCase			)
														__snake_case	:    List[Any]											=     param.transpose(0							,			2			)
														__snake_case	:    Optional[int]											=     param.transpose(1							,			2			).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														__snake_case	:    Optional[Any]											=     (num_heads, num_splits, hidden_size) + input_shape[1:]
														__snake_case	:    List[str]											=     param.view(*__lowerCamelCase			)
														__snake_case	:    str											=     param.transpose(0							,			1			).contiguous()
							__snake_case	:    Any											=     param.view(*__lowerCamelCase			)
							return param







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							# The converted output model.
							__snake_case	:    List[Any]											=     {}

							# old versions did not store training args
							__snake_case	:    List[str]											=     input_state_dict.get("args"							,			__lowerCamelCase			)
							if ds_args is not None:
														# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
														# from pprint import pprint
														# pprint(vars(ds_args))

														__snake_case	:    Union[str, Any]											=     ds_args.padded_vocab_size
														__snake_case	:    int											=     ds_args.max_position_embeddings
														__snake_case	:    List[Any]											=     ds_args.hidden_size
														__snake_case	:    List[Any]											=     ds_args.num_layers
														__snake_case	:    Optional[Any]											=     ds_args.num_attention_heads
														__snake_case	:    Dict											=     ds_args.ffn_hidden_size
														# pprint(config)

							# The number of heads.
							__snake_case	:    str											=     config.n_head
							# The hidden_size per head.
							__snake_case	:    int											=     config.n_embd // config.n_head
							# Megatron-LM checkpoint version
							if "checkpoint_version" in input_state_dict.keys():
														__snake_case	:    Tuple											=     input_state_dict["checkpoint_version"]
							else:
														__snake_case	:    Any											=     0.0

							# The model.
							__snake_case	:    Dict											=     input_state_dict["model"]
							# The language model.
							__snake_case	:    str											=     model["language_model"]
							# The embeddings.
							__snake_case	:    List[str]											=     lm["embedding"]

							# The word embeddings.
							__snake_case	:    Any											=     embeddings["word_embeddings"]["weight"]
							# Truncate the embedding table to vocab_size rows.
							__snake_case	:    Tuple											=     word_embeddings[: config.vocab_size, :]
							__snake_case	:    Tuple											=     word_embeddings

							# The position embeddings.
							__snake_case	:    Tuple											=     embeddings["position_embeddings"]["weight"]
							# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
							__snake_case	:    Dict											=     pos_embeddings.size(0			)
							if n_positions != config.n_positions:
														raise ValueError(
														    F'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'			)
							# Store the position embeddings.
							__snake_case	:    Union[str, Any]											=     pos_embeddings

							# The transformer.
							__snake_case	:    List[str]											=     lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

							# The regex to extract layer names.
							__snake_case	:    Optional[int]											=     re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"			)

							# The simple map of names for "automated" rules.
							__snake_case	:    Optional[Any]											=     {
							    "attention.dense": ".attn.c_proj.",
							    "self_attention.dense": ".attn.c_proj.",
							    "mlp.dense_h_to_4h": ".mlp.c_fc.",
							    "mlp.dense_4h_to_h": ".mlp.c_proj.",
							}

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														__snake_case	:    List[str]											=     layer_re.match(__lowerCamelCase			)

														# Stop if that's not a layer
														if m is None:
																					break

														# The index of the layer.
														__snake_case	:    int											=     int(m.group(1			)			)
														# The name of the operation.
														__snake_case	:    Dict											=     m.group(2			)
														# Is it a weight or a bias?
														__snake_case	:    Any											=     m.group(3			)

														# The name of the layer.
														__snake_case	:    List[Any]											=     F'transformer.h.{layer_idx}'

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"			):
																					__snake_case	:    Optional[int]											=     "ln_1" if op_name.startswith("input"			) else "ln_2"
																					__snake_case	:    Optional[Any]											=     val

														# Transpose the QKV matrix.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "weight":
																					# Insert a tensor of 1x1xDxD bias.
																					__snake_case	:    Dict											=     torch.tril(torch.ones((n_positions, n_positions)							,			dtype=torch.floataa			)			).view(
																					    1							,			1							,			__lowerCamelCase							,			__lowerCamelCase			)
																					__snake_case	:    List[str]											=     causal_mask

																					# Insert a "dummy" tensor for masked_bias.
																					__snake_case	:    Union[str, Any]											=     torch.tensor(-1e4							,			dtype=torch.floataa			)
																					__snake_case	:    Dict											=     masked_bias

																					__snake_case	:    Tuple											=     fix_query_key_value_ordering(__lowerCamelCase							,			__lowerCamelCase							,			3							,			__lowerCamelCase							,			__lowerCamelCase			)
																					# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																					__snake_case	:    List[Any]											=     out_val.transpose(0							,			1			).contiguous()
																					# Store.
																					__snake_case	:    Optional[int]											=     out_val

														# Transpose the bias.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "bias":
																					__snake_case	:    Any											=     fix_query_key_value_ordering(__lowerCamelCase							,			__lowerCamelCase							,			3							,			__lowerCamelCase							,			__lowerCamelCase			)
																					# Store. No change of shape.
																					__snake_case	:    Any											=     out_val

														# Transpose the weights.
														elif weight_or_bias == "weight":
																					__snake_case	:    str											=     megatron_to_transformers[op_name]
																					__snake_case	:    Union[str, Any]											=     val.transpose(0							,			1			)

														# Copy the bias.
														elif weight_or_bias == "bias":
																					__snake_case	:    Any											=     megatron_to_transformers[op_name]
																					__snake_case	:    List[Any]											=     val

    # DEBUG.
							assert config.n_layer == layer_idx + 1

							# The final layernorm.
							__snake_case	:    List[Any]											=     transformer["final_layernorm.weight"]
							__snake_case	:    List[str]											=     transformer["final_layernorm.bias"]

							# For LM head, transformers' wants the matrix to weight embeddings.
							__snake_case	:    Tuple											=     word_embeddings

							# It should be done!
							return output_state_dict







def        lowerCAmelCase_  (      ):
							# Create the argument parser.
							__snake_case	:    int											=     argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"							,			action="store_true"			)
							parser.add_argument(
							    "path_to_checkpoint"							,			type=__lowerCamelCase							,			help="Path to the checkpoint file (.zip archive or direct .pt file)"							,			)
							parser.add_argument(
							    "--config_file"							,			default=""							,			type=__lowerCamelCase							,			help="An optional config json file describing the pre-trained model."							,			)
							__snake_case	:    Optional[Any]											=     parser.parse_args()

							# Extract the basename.
							__snake_case	:    int											=     os.path.dirname(args.path_to_checkpoint			)

							# Load the model.
							# the .zip is very optional, let's keep it for backward compatibility
							print(F'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'			)
							if args.path_to_checkpoint.endswith(".zip"			):
														with zipfile.ZipFile(args.path_to_checkpoint							,			"r"			) as checkpoint:
																					with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"			) as pytorch_dict:
																												__snake_case	:    List[Any]											=     torch.load(__lowerCamelCase							,			map_location="cpu"			)
							else:
														__snake_case	:    Any											=     torch.load(args.path_to_checkpoint							,			map_location="cpu"			)

							__snake_case	:    List[str]											=     input_state_dict.get("args"							,			__lowerCamelCase			)

							# Read the config, or default to the model released by NVIDIA.
							if args.config_file == "":
														if ds_args is not None:
																					if ds_args.bias_gelu_fusion:
																												__snake_case	:    List[str]											=     "gelu_fast"
																					elif ds_args.openai_gelu:
																												__snake_case	:    Optional[int]											=     "gelu_new"
																					else:
																												__snake_case	:    int											=     "gelu"
														else:
																					# in the very early days this used to be "gelu_new"
																					__snake_case	:    Tuple											=     "gelu_new"

														# Spell out all parameters in case the defaults change.
														__snake_case	:    Dict											=     GPTaConfig(
														    vocab_size=5_0_2_5_7							,			n_positions=1_0_2_4							,			n_embd=1_0_2_4							,			n_layer=2_4							,			n_head=1_6							,			n_inner=4_0_9_6							,			activation_function=__lowerCamelCase							,			resid_pdrop=0.1							,			embd_pdrop=0.1							,			attn_pdrop=0.1							,			layer_norm_epsilon=1e-5							,			initializer_range=0.0_2							,			summary_type="cls_index"							,			summary_use_proj=__lowerCamelCase							,			summary_activation=__lowerCamelCase							,			summary_proj_to_labels=__lowerCamelCase							,			summary_first_dropout=0.1							,			scale_attn_weights=__lowerCamelCase							,			use_cache=__lowerCamelCase							,			bos_token_id=5_0_2_5_6							,			eos_token_id=5_0_2_5_6							,			)
							else:
														__snake_case	:    List[str]											=     GPTaConfig.from_json_file(args.config_file			)

							__snake_case	:    Tuple											=     ["GPT2LMHeadModel"]

							# Convert.
							print("Converting"			)
							__snake_case	:    Union[str, Any]											=     convert_megatron_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)

							# Print the structure of converted state dict.
							if args.print_checkpoint_structure:
														recursive_print(__lowerCamelCase							,			__lowerCamelCase			)

							# Add tokenizer class info to config
							# see https://github.com/huggingface/transformers/issues/13906)
							if ds_args is not None:
														__snake_case	:    Union[str, Any]											=     ds_args.tokenizer_type
														if tokenizer_type == "GPT2BPETokenizer":
																					__snake_case	:    Optional[int]											=     "gpt2"
														elif tokenizer_type == "PretrainedFromHF":
																					__snake_case	:    Any											=     ds_args.tokenizer_name_or_path
														else:
																					raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}'			)
							else:
														__snake_case	:    Optional[int]											=     "gpt2"

							__snake_case	:    Optional[Any]											=     AutoTokenizer.from_pretrained(__lowerCamelCase			)
							__snake_case	:    List[str]											=     type(__lowerCamelCase			).__name__
							__snake_case	:    Optional[int]											=     tokenizer_class

							# Store the config to file.
							print("Saving config"			)
							config.save_pretrained(__lowerCamelCase			)

							# Save tokenizer based on args
							print(F'Adding {tokenizer_class} tokenizer files'			)
							tokenizer.save_pretrained(__lowerCamelCase			)

							# Store the state_dict to file.
							__snake_case	:    Optional[int]											=     os.path.join(__lowerCamelCase							,			"pytorch_model.bin"			)
							print(F'Saving checkpoint to "{output_checkpoint_file}"'			)
							torch.save(__lowerCamelCase							,			__lowerCamelCase			)


####################################################################################################

if __name__ == "__main__":
			main()

####################################################################################################





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


_snake_case  :	Any			      =       "bert-base-cased"
_snake_case  :	List[Any]			      =       "google/pegasus-xsum"
_snake_case  :	Dict			      =       [" Sam ate lunch today.", "Sams lunch ingredients."]
_snake_case  :	Dict			      =       ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_snake_case  :	List[str]			      =       "patrickvonplaten/t5-tiny-random"
_snake_case  :	Optional[Any]			      =       "sshleifer/bart-tiny-random"
_snake_case  :	Optional[Any]			      =       "sshleifer/tiny-mbart"
_snake_case  :	Tuple			      =       "sshleifer/tiny-marian-en-de"







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Union[str, Any]											=     "\n".join(__lowerCamelCase			)
							Path(__lowerCamelCase			).open("w"			).writelines(__lowerCamelCase			)







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

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]					,			)
							@slow
							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						int       )			->					Union[str, Any]:
														__snake_case	:    Optional[Any]											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    List[Any]											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
														__snake_case	:    Optional[int]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in ARTICLES       )
														__snake_case	:    Optional[int]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in SUMMARIES       )
														__snake_case	:    str											=     4
														__snake_case	:    Optional[int]											=     8
														assert max_len_target > max_src_len  # Will be truncated
														assert max_len_source > max_src_len  # Will be truncated
														__snake_case				,    __snake_case	:    Optional[int]											=     "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
														__snake_case	:    List[str]											=     SeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=lowerCamelCase					,			max_target_length=lowerCamelCase					,			src_lang=lowerCamelCase					,			tgt_lang=lowerCamelCase					,			)
														__snake_case	:    str											=     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
																					__snake_case	:    int											=     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 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						List[Any]       )			->					Optional[Any]:
														__snake_case	:    Any											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    List[str]											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
														__snake_case	:    Union[str, Any]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in ARTICLES       )
														__snake_case	:    Tuple											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in SUMMARIES       )
														__snake_case	:    List[str]											=     4
														__snake_case	:    List[str]											=     LegacySeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=20					,			max_target_length=lowerCamelCase					,			)
														__snake_case	:    int											=     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 					__snake_case      (							self :						List[str]       )			->					int:
														__snake_case	:    str											=     AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

														__snake_case	:    int											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
														__snake_case	:    str											=     tmp_dir.joinpath("train.source"       ).open().readlines()
														__snake_case	:    Any											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
														pack_data_dir(lowerCamelCase					,			lowerCamelCase					,			128					,			lowerCamelCase       )
														__snake_case	:    str											=     {x.name for x in tmp_dir.iterdir()}
														__snake_case	:    Optional[int]											=     {x.name for x in save_dir.iterdir()}
														__snake_case	:    Optional[int]											=     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 					__snake_case      (							self :						Tuple       )			->					str:
														if not FAIRSEQ_AVAILABLE:
																					return
														__snake_case				,    __snake_case				,    __snake_case	:    Optional[Any]											=     self._get_dataset(max_len=64       )
														__snake_case	:    Optional[int]											=     64
														__snake_case	:    List[Any]											=     ds.make_dynamic_sampler(lowerCamelCase					,			required_batch_size_multiple=lowerCamelCase       )
														__snake_case	:    str											=     [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
														__snake_case	:    Optional[Any]											=     DataLoader(lowerCamelCase					,			batch_sampler=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2       )
														__snake_case	:    Union[str, Any]											=     []
														__snake_case	:    str											=     []
														for batch in data_loader:
																					__snake_case	:    str											=     batch["input_ids"].shape
																					__snake_case	:    Dict											=     src_shape[0]
																					assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																					__snake_case	:    str											=     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 					__snake_case      (							self :						int       )			->					Any:
														__snake_case				,    __snake_case				,    __snake_case	:    Union[str, Any]											=     self._get_dataset(max_len=512       )
														__snake_case	:    Union[str, Any]											=     2
														__snake_case	:    List[str]											=     ds.make_sortish_sampler(lowerCamelCase					,			shuffle=lowerCamelCase       )

														__snake_case	:    Dict											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2       )
														__snake_case	:    List[Any]											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2					,			sampler=lowerCamelCase       )

														__snake_case	:    List[Any]											=     tokenizer.pad_token_id

														def count_pad_tokens(lowerCamelCase :						List[Any]					,			lowerCamelCase :						Optional[int]="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 					__snake_case      (							self :						Any					,			lowerCamelCase :						List[Any]=1000					,			lowerCamelCase :						Union[str, Any]=128       )			->					Any:
														if os.getenv("USE_REAL_DATA"					,			lowerCamelCase       ):
																					__snake_case	:    int											=     "examples/seq2seq/wmt_en_ro"
																					__snake_case	:    Union[str, Any]											=     max_len * 2 * 64
																					if not Path(lowerCamelCase       ).joinpath("train.len"       ).exists():
																												save_len_file(lowerCamelCase					,			lowerCamelCase       )
														else:
																					__snake_case	:    List[str]											=     "examples/seq2seq/test_data/wmt_en_ro"
																					__snake_case	:    List[Any]											=     max_len * 4
																					save_len_file(lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    Dict											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    int											=     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 					__snake_case      (							self :						Tuple       )			->					Dict:
														__snake_case				,    __snake_case				,    __snake_case	:    Any											=     self._get_dataset()
														__snake_case	:    Optional[Any]											=     set(DistributedSortishSampler(lowerCamelCase					,			256					,			num_replicas=2					,			rank=0					,			add_extra_examples=lowerCamelCase       )       )
														__snake_case	:    int											=     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 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Union[str, Any]       )			->					str:
														__snake_case	:    Tuple											=     AutoTokenizer.from_pretrained(lowerCamelCase					,			use_fast=lowerCamelCase       )
														if tok_name == MBART_TINY:
																					__snake_case	:    Dict											=     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"					,			)
																					__snake_case	:    Any											=     train_dataset.dataset_kwargs
																					assert "src_lang" in kwargs and "tgt_lang" in kwargs
														else:
																					__snake_case	:    Union[str, Any]											=     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					,			)
																					__snake_case	:    Optional[int]											=     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

