"""simple docstring"""





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


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



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE	)->     Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:    List[str]      	=     """\n""".join(UpperCamelCase__	)
							Path(UpperCamelCase__	).open("""w"""	).writelines(UpperCamelCase__	)



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE	)->     List[str]:
							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 			_snake_case		(     UpperCamelCase__			):







				"""simple docstring"""






				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]   ,     )
				@slow
				def 		_lowerCAmelCase		(   self				:				Union[str, Any]   ,     _A				:				Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Tuple      	=     AutoTokenizer.from_pretrained(__lowerCamelCase)
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     max(len(tokenizer.encode(__lowerCamelCase)) for a in ARTICLES)
											_SCREAMING_SNAKE_CASE		:    int      	=     max(len(tokenizer.encode(__lowerCamelCase)) for a in SUMMARIES)
											_SCREAMING_SNAKE_CASE		:    str      	=     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		:    int      	=     """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     SeqaSeqDataset(
											    __lowerCamelCase   ,     data_dir=__lowerCamelCase   ,     type_path="""train"""   ,     max_source_length=__lowerCamelCase   ,     max_target_length=__lowerCamelCase   ,     src_lang=__lowerCamelCase   ,     tgt_lang=__lowerCamelCase   ,     )
											_SCREAMING_SNAKE_CASE		:    Any      	=     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
																		_SCREAMING_SNAKE_CASE		:    Tuple      	=     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 		_lowerCAmelCase		(   self				:				List[str]   ,     _A				:				Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     AutoTokenizer.from_pretrained(__lowerCamelCase)
											_SCREAMING_SNAKE_CASE		:    Dict      	=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
											_SCREAMING_SNAKE_CASE		:    str      	=     max(len(tokenizer.encode(__lowerCamelCase)) for a in ARTICLES)
											_SCREAMING_SNAKE_CASE		:    Any      	=     max(len(tokenizer.encode(__lowerCamelCase)) for a in SUMMARIES)
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     4
											_SCREAMING_SNAKE_CASE		:    int      	=     LegacySeqaSeqDataset(
											    __lowerCamelCase   ,     data_dir=__lowerCamelCase   ,     type_path="""train"""   ,     max_source_length=2_0   ,     max_target_length=__lowerCamelCase   ,     )
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     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 2_0 >= 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 		_lowerCAmelCase		(   self				:				str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Dict      	=     AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25""")

											_SCREAMING_SNAKE_CASE		:    Dict      	=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
											_SCREAMING_SNAKE_CASE		:    int      	=     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(__lowerCamelCase   ,     __lowerCamelCase   ,     1_2_8   ,     __lowerCamelCase)
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     {x.name for x in tmp_dir.iterdir()}
											_SCREAMING_SNAKE_CASE		:    Dict      	=     {x.name for x in save_dir.iterdir()}
											_SCREAMING_SNAKE_CASE		:    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 		_lowerCAmelCase		(   self				:				Union[str, Any]):



											"""simple docstring"""







											if not FAIRSEQ_AVAILABLE:
																		return
											_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     self._get_dataset(max_len=6_4)
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     6_4
											_SCREAMING_SNAKE_CASE		:    str      	=     ds.make_dynamic_sampler(__lowerCamelCase   ,     required_batch_size_multiple=__lowerCamelCase)
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     [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
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     DataLoader(__lowerCamelCase   ,     batch_sampler=__lowerCamelCase   ,     collate_fn=ds.collate_fn   ,     num_workers=2)
											_SCREAMING_SNAKE_CASE		:    Any      	=     []
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     []
											for batch in data_loader:
																		_SCREAMING_SNAKE_CASE		:    Dict      	=     batch["""input_ids"""].shape
																		_SCREAMING_SNAKE_CASE		:    str      	=     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(__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 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		:    int      	=     self._get_dataset(max_len=5_1_2)
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     2
											_SCREAMING_SNAKE_CASE		:    str      	=     ds.make_sortish_sampler(__lowerCamelCase   ,     shuffle=__lowerCamelCase)

											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     DataLoader(__lowerCamelCase   ,     batch_size=__lowerCamelCase   ,     collate_fn=ds.collate_fn   ,     num_workers=2)
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     DataLoader(__lowerCamelCase   ,     batch_size=__lowerCamelCase   ,     collate_fn=ds.collate_fn   ,     num_workers=2   ,     sampler=__lowerCamelCase)

											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     tokenizer.pad_token_id

											def count_pad_tokens(_A				:				List[Any]   ,     _A				:				List[str]="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 		_lowerCAmelCase		(   self				:				List[Any]   ,     _A				:				Dict=1_0_0_0   ,     _A				:				int=1_2_8):



											"""simple docstring"""







											if os.getenv("""USE_REAL_DATA"""   ,     __lowerCamelCase):
																		_SCREAMING_SNAKE_CASE		:    Tuple      	=     """examples/seq2seq/wmt_en_ro"""
																		_SCREAMING_SNAKE_CASE		:    int      	=     max_len * 2 * 6_4
																		if not Path(__lowerCamelCase).joinpath("""train.len""").exists():
																									save_len_file(__lowerCamelCase   ,     __lowerCamelCase)
											else:
																		_SCREAMING_SNAKE_CASE		:    Dict      	=     """examples/seq2seq/test_data/wmt_en_ro"""
																		_SCREAMING_SNAKE_CASE		:    Any      	=     max_len * 4
																		save_len_file(__lowerCamelCase   ,     __lowerCamelCase)

											_SCREAMING_SNAKE_CASE		:    Dict      	=     AutoTokenizer.from_pretrained(__lowerCamelCase)
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     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 		_lowerCAmelCase		(   self				:				List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		:    Tuple      	=     self._get_dataset()
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     set(DistributedSortishSampler(__lowerCamelCase   ,     2_5_6   ,     num_replicas=2   ,     rank=0   ,     add_extra_examples=__lowerCamelCase))
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     set(DistributedSortishSampler(__lowerCamelCase   ,     2_5_6   ,     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 		_lowerCAmelCase		(   self				:				List[str]   ,     _A				:				Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Dict      	=     AutoTokenizer.from_pretrained(__lowerCamelCase   ,     use_fast=__lowerCamelCase)
											if tok_name == MBART_TINY:
																		_SCREAMING_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"""   ,     )
																		_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     train_dataset.dataset_kwargs
																		assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
																		_SCREAMING_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   ,     )
																		_SCREAMING_SNAKE_CASE		:    List[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(__lowerCamelCase) == 1 if tok_name == BART_TINY else len(__lowerCamelCase) == 0









import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2					)
        A__		   =		[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        for example in examples:
               A__		   =		video_classifier(__lowerCamelCase					)

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                   ],)



 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__		   =		VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10}					)
        A__		   =		pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4					)

        A__		   =		hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		video_classifier(__lowerCamelCase,top_k=2					)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__		   =		video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 							UpperCamelCase     (				self					):
        pass





