

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


a_     =			'bert-base-cased'
a_     =			'google/pegasus-xsum'
a_     =			[' Sam ate lunch today.', 'Sams lunch ingredients.']
a_     =			['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
a_     =			'patrickvonplaten/t5-tiny-random'
a_     =			'sshleifer/bart-tiny-random'
a_     =			'sshleifer/tiny-mbart'
a_     =			'sshleifer/tiny-marian-en-de'

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					str    =				"\n".join(UpperCamelCase__)
	Path(UpperCamelCase__).open("w").writelines(UpperCamelCase__)

def        lowerCamelCase__       (    _a):





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






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]					,					)
			@slow
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       Optional[int]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				AutoTokenizer.from_pretrained(__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
				SCREAMING_SNAKE_CASE      :					Any    =				max(len(tokenizer.encode(__UpperCAmelCase		)		) for a in ARTICLES		)
				SCREAMING_SNAKE_CASE      :					Dict    =				max(len(tokenizer.encode(__UpperCAmelCase		)		) for a in SUMMARIES		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				4
				SCREAMING_SNAKE_CASE      :					int    =				8
				assert max_len_target > max_src_len  # Will be truncated
				assert max_len_source > max_src_len  # Will be truncated
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					List[str]    =				"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				SeqaSeqDataset(
				    __UpperCAmelCase					,					data_dir=__UpperCAmelCase					,					type_path="train"					,					max_source_length=__UpperCAmelCase					,					max_target_length=__UpperCAmelCase					,					src_lang=__UpperCAmelCase					,					tgt_lang=__UpperCAmelCase					,					)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				DataLoader(__UpperCAmelCase					,					batch_size=2					,					collate_fn=train_dataset.collate_fn		)
				for batch in dataloader:
					assert isinstance(__UpperCAmelCase					,					__UpperCAmelCase		)
					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
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				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 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       List[str]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				AutoTokenizer.from_pretrained(__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
				SCREAMING_SNAKE_CASE      :					int    =				max(len(tokenizer.encode(__UpperCAmelCase		)		) for a in ARTICLES		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				max(len(tokenizer.encode(__UpperCAmelCase		)		) for a in SUMMARIES		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				4
				SCREAMING_SNAKE_CASE      :					List[str]    =				LegacySeqaSeqDataset(
				    __UpperCAmelCase					,					data_dir=__UpperCAmelCase					,					type_path="train"					,					max_source_length=20					,					max_target_length=__UpperCAmelCase					,					)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				DataLoader(__UpperCAmelCase					,					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 							__UpperCamelCase       (	self							:       str		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
				SCREAMING_SNAKE_CASE      :					Dict    =				tmp_dir.joinpath("train.source"		).open().readlines()
				SCREAMING_SNAKE_CASE      :					Dict    =				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
				pack_data_dir(__UpperCAmelCase					,					__UpperCAmelCase					,					128					,					__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Dict    =				{x.name for x in tmp_dir.iterdir()}
				SCREAMING_SNAKE_CASE      :					str    =				{x.name for x in save_dir.iterdir()}
				SCREAMING_SNAKE_CASE      :					str    =				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(__UpperCAmelCase		) < len(__UpperCAmelCase		)
				assert len(__UpperCAmelCase		) == 1
				assert len(packed_examples[0]		) == sum(len(__UpperCAmelCase		) for x in orig_examples		)
				assert orig_paths == new_paths
			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE					,					reason="This test requires fairseq"		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					List[Any]    =				self._get_dataset(max_len=512		)
				SCREAMING_SNAKE_CASE      :					int    =				2
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ds.make_sortish_sampler(__UpperCAmelCase					,					shuffle=__UpperCAmelCase		)

				SCREAMING_SNAKE_CASE      :					Any    =				DataLoader(__UpperCAmelCase					,					batch_size=__UpperCAmelCase					,					collate_fn=ds.collate_fn					,					num_workers=2		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				DataLoader(__UpperCAmelCase					,					batch_size=__UpperCAmelCase					,					collate_fn=ds.collate_fn					,					num_workers=2					,					sampler=__UpperCAmelCase		)

				SCREAMING_SNAKE_CASE      :					Dict    =				tokenizer.pad_token_id

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

				assert sum(count_pad_tokens(__UpperCAmelCase					,					k="labels"		)		) < sum(count_pad_tokens(__UpperCAmelCase					,					k="labels"		)		)
				assert sum(count_pad_tokens(__UpperCAmelCase		)		) < sum(count_pad_tokens(__UpperCAmelCase		)		)
				assert len(__UpperCAmelCase		) == len(__UpperCAmelCase		)
			def 							__UpperCamelCase       (	self							:       int					,					a							:       List[Any]=1000					,					a							:       Tuple=128		)							->				List[str]:

				"""simple docstring"""

				if os.getenv("USE_REAL_DATA"					,					__UpperCAmelCase		):
					SCREAMING_SNAKE_CASE      :					List[Any]    =				"examples/seq2seq/wmt_en_ro"
					SCREAMING_SNAKE_CASE      :					Tuple    =				max_len * 2 * 64
					if not Path(__UpperCAmelCase		).joinpath("train.len"		).exists():
						save_len_file(__UpperCAmelCase					,					__UpperCAmelCase		)
				else:
					SCREAMING_SNAKE_CASE      :					Any    =				"examples/seq2seq/test_data/wmt_en_ro"
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				max_len * 4
					save_len_file(__UpperCAmelCase					,					__UpperCAmelCase		)

				SCREAMING_SNAKE_CASE      :					Any    =				AutoTokenizer.from_pretrained(__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				SeqaSeqDataset(
				    __UpperCAmelCase					,					data_dir=__UpperCAmelCase					,					type_path="train"					,					max_source_length=__UpperCAmelCase					,					max_target_length=__UpperCAmelCase					,					n_obs=__UpperCAmelCase					,					)
				return ds, max_tokens, tokenizer
			def 							__UpperCamelCase       (	self							:       Any		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Optional[int]    =				self._get_dataset()
				SCREAMING_SNAKE_CASE      :					List[str]    =				set(DistributedSortishSampler(__UpperCAmelCase					,					256					,					num_replicas=2					,					rank=0					,					add_extra_examples=__UpperCAmelCase		)		)
				SCREAMING_SNAKE_CASE      :					Any    =				set(DistributedSortishSampler(__UpperCAmelCase					,					256					,					num_replicas=2					,					rank=1					,					add_extra_examples=__UpperCAmelCase		)		)
				assert idsa.intersection(__UpperCAmelCase		) == set()






			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]					,					)
			def 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       int		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				AutoTokenizer.from_pretrained(__UpperCAmelCase					,					use_fast=__UpperCAmelCase		)
				if tok_name == MBART_TINY:
					SCREAMING_SNAKE_CASE      :					Dict    =				SeqaSeqDataset(
					    __UpperCAmelCase					,					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"					,					)
					SCREAMING_SNAKE_CASE      :					int    =				train_dataset.dataset_kwargs
					assert "src_lang" in kwargs and "tgt_lang" in kwargs
				else:
					SCREAMING_SNAKE_CASE      :					Dict    =				SeqaSeqDataset(
					    __UpperCAmelCase					,					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					,					)
					SCREAMING_SNAKE_CASE      :					Any    =				train_dataset.dataset_kwargs
					assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
					assert len(__UpperCAmelCase		) == 1 if tok_name == BART_TINY else len(__UpperCAmelCase		) == 0
"""simple docstring"""




# Copyright 2023 The HuggingFace Team. 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["VivitImageProcessor"]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VivitModel",
				    "VivitPreTrainedModel",
				    "VivitForVideoClassification",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





