




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__A        =    logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int]      )     ->       str:





				"""simple docstring"""


				if "token" in model_name_or_path:
								return "rag_token"
				if "sequence" in model_name_or_path:
								return "rag_sequence"
				if "bart" in model_name_or_path:
								return "bart"
				return None







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Union[str, Any]      )     ->       str:





				"""simple docstring"""


				return max(metric_fn(UpperCamelCase__ ,     UpperCamelCase__      ) for gt in ground_truths      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      str      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    [line.strip() for line in open(UpperCamelCase__ ,     'r'      ).readlines()]
				__lowerCamelCase		       =    []

				if args.gold_data_mode == "qa":
								__lowerCamelCase		       =    pd.read_csv(UpperCamelCase__ ,     sep='\t' ,     header=UpperCamelCase__      )
								for answer_list in data[1]:
												__lowerCamelCase		       =    ast.literal_eval(UpperCamelCase__      )
												answers.append(UpperCamelCase__      )
				else:
								__lowerCamelCase		       =    [line.strip() for line in open(UpperCamelCase__ ,     'r'      ).readlines()]
								__lowerCamelCase		       =    [[reference] for reference in references]

				__lowerCamelCase		       =    __lowerCamelCase		       =    __lowerCamelCase		       =    0
				for prediction, ground_truths in zip(UpperCamelCase__ ,     UpperCamelCase__      ):
								total += 1
								em += metric_max_over_ground_truths(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								fa += metric_max_over_ground_truths(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

				__lowerCamelCase		       =    1_00.0 * em / total
				__lowerCamelCase		       =    1_00.0 * fa / total

				logger.info(F"""F1: {fa:.2f}"""      )
				logger.info(F"""EM: {em:.2f}"""      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      List[Any]      )     ->       List[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    args.k
				__lowerCamelCase		       =    [line.strip() for line in open(UpperCamelCase__ ,     'r'      ).readlines()]
				__lowerCamelCase		       =    [line.strip() for line in open(UpperCamelCase__ ,     'r'      ).readlines()]

				__lowerCamelCase		       =    __lowerCamelCase		       =    0
				for hypo, reference in zip(UpperCamelCase__ ,     UpperCamelCase__      ):
								__lowerCamelCase		       =    set(hypo.split('\t'      )[:k]      )
								__lowerCamelCase		       =    set(reference.split('\t'      )      )
								total += 1
								em += len(hypo_provenance & ref_provenance      ) / k

				__lowerCamelCase		       =    1_00.0 * em / total
				logger.info(F"""Precision@{k}: {em: .2f}"""      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Dict      )     ->       Dict:





				"""simple docstring"""


				def strip_title(UpperCamelCase__				:      Optional[int]      ):
								if title.startswith('"'      ):
												__lowerCamelCase		       =    title[1:]
								if title.endswith('"'      ):
												__lowerCamelCase		       =    title[:-1]
								return title

				__lowerCamelCase		       =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    UpperCamelCase__ ,     return_tensors='pt' ,     padding=UpperCamelCase__ ,     truncation=UpperCamelCase__ ,     )['input_ids'].to(args.device      )

				__lowerCamelCase		       =    rag_model.rag.question_encoder(UpperCamelCase__      )
				__lowerCamelCase		       =    question_enc_outputs[0]

				__lowerCamelCase		       =    rag_model.retriever(
				    UpperCamelCase__ ,     question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy() ,     prefix=rag_model.rag.generator.config.prefix ,     n_docs=rag_model.config.n_docs ,     return_tensors='pt' ,     )
				__lowerCamelCase		       =    rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
				__lowerCamelCase		       =    []
				for docs in all_docs:
								__lowerCamelCase		       =    [strip_title(UpperCamelCase__      ) for title in docs['title']]
								provenance_strings.append('\t'.join(UpperCamelCase__      )      )
				return provenance_strings







def 			lowerCamelCase_		(       UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      List[Any]      )     ->       List[str]:





				"""simple docstring"""


				with torch.no_grad():
								__lowerCamelCase		       =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    UpperCamelCase__ ,     return_tensors='pt' ,     padding=UpperCamelCase__ ,     truncation=UpperCamelCase__      )

								__lowerCamelCase		       =    inputs_dict.input_ids.to(args.device      )
								__lowerCamelCase		       =    inputs_dict.attention_mask.to(args.device      )
								__lowerCamelCase		       =    rag_model.generate(  # rag_model overwrites generate
								    UpperCamelCase__ ,     attention_mask=UpperCamelCase__ ,     num_beams=args.num_beams ,     min_length=args.min_length ,     max_length=args.max_length ,     early_stopping=UpperCamelCase__ ,     num_return_sequences=1 ,     bad_words_ids=[[0, 0]] ,     )
								__lowerCamelCase		       =    rag_model.retriever.generator_tokenizer.batch_decode(UpperCamelCase__ ,     skip_special_tokens=UpperCamelCase__      )

								if args.print_predictions:
												for q, a in zip(UpperCamelCase__ ,     UpperCamelCase__      ):
																logger.info('Q: {} - A: {}'.format(UpperCamelCase__ ,     UpperCamelCase__      )      )

								return answers







def 			lowerCamelCase_		(       )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument(
				    '--model_type' ,     choices=['rag_sequence', 'rag_token', 'bart'] ,     type=UpperCamelCase__ ,     help=(
				        'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
				        ' model_name_or_path'
				    ) ,     )
				parser.add_argument(
				    '--index_name' ,     default=UpperCamelCase__ ,     choices=['exact', 'compressed', 'legacy'] ,     type=UpperCamelCase__ ,     help='RAG model retriever type' ,     )
				parser.add_argument(
				    '--index_path' ,     default=UpperCamelCase__ ,     type=UpperCamelCase__ ,     help='Path to the retrieval index' ,     )
				parser.add_argument('--n_docs' ,     default=5 ,     type=UpperCamelCase__ ,     help='Number of retrieved docs'      )
				parser.add_argument(
				    '--model_name_or_path' ,     default=UpperCamelCase__ ,     type=UpperCamelCase__ ,     required=UpperCamelCase__ ,     help='Path to pretrained checkpoints or model identifier from huggingface.co/models' ,     )
				parser.add_argument(
				    '--eval_mode' ,     choices=['e2e', 'retrieval'] ,     default='e2e' ,     type=UpperCamelCase__ ,     help=(
				        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
				        ' precision@k.'
				    ) ,     )
				parser.add_argument('--k' ,     default=1 ,     type=UpperCamelCase__ ,     help='k for the precision@k calculation'      )
				parser.add_argument(
				    '--evaluation_set' ,     default=UpperCamelCase__ ,     type=UpperCamelCase__ ,     required=UpperCamelCase__ ,     help='Path to a file containing evaluation samples' ,     )
				parser.add_argument(
				    '--gold_data_path' ,     default=UpperCamelCase__ ,     type=UpperCamelCase__ ,     required=UpperCamelCase__ ,     help='Path to a tab-separated file with gold samples' ,     )
				parser.add_argument(
				    '--gold_data_mode' ,     default='qa' ,     type=UpperCamelCase__ ,     choices=['qa', 'ans'] ,     help=(
				        'Format of the gold data file'
				        'qa - a single line in the following format: question [tab] answer_list'
				        'ans - a single line of the gold file contains the expected answer string'
				    ) ,     )
				parser.add_argument(
				    '--predictions_path' ,     type=UpperCamelCase__ ,     default='predictions.txt' ,     help='Name of the predictions file, to be stored in the checkpoints directory' ,     )
				parser.add_argument(
				    '--eval_all_checkpoints' ,     action='store_true' ,     help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number' ,     )
				parser.add_argument(
				    '--eval_batch_size' ,     default=8 ,     type=UpperCamelCase__ ,     help='Batch size per GPU/CPU for evaluation.' ,     )
				parser.add_argument(
				    '--recalculate' ,     help='Recalculate predictions even if the prediction file exists' ,     action='store_true' ,     )
				parser.add_argument(
				    '--num_beams' ,     default=4 ,     type=UpperCamelCase__ ,     help='Number of beams to be used when generating answers' ,     )
				parser.add_argument('--min_length' ,     default=1 ,     type=UpperCamelCase__ ,     help='Min length of the generated answers'      )
				parser.add_argument('--max_length' ,     default=50 ,     type=UpperCamelCase__ ,     help='Max length of the generated answers'      )

				parser.add_argument(
				    '--print_predictions' ,     action='store_true' ,     help='If True, prints predictions while evaluating.' ,     )
				parser.add_argument(
				    '--print_docs' ,     action='store_true' ,     help='If True, prints docs retried while generating.' ,     )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
				return args







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any]      )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase		       =    {}
				if args.model_type is None:
								__lowerCamelCase		       =    infer_model_type(args.model_name_or_path      )
								assert args.model_type is not None
				if args.model_type.startswith('rag'      ):
								__lowerCamelCase		       =    RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
								__lowerCamelCase		       =    args.n_docs
								if args.index_name is not None:
												__lowerCamelCase		       =    args.index_name
								if args.index_path is not None:
												__lowerCamelCase		       =    args.index_path
				else:
								__lowerCamelCase		       =    BartForConditionalGeneration

				__lowerCamelCase		       =    (
				    [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
				    if args.eval_all_checkpoints
				    else [args.model_name_or_path]
				)

				logger.info('Evaluate the following checkpoints: %s' ,     UpperCamelCase__      )

				__lowerCamelCase		       =    get_scores if args.eval_mode == 'e2e' else get_precision_at_k
				__lowerCamelCase		       =    evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

				for checkpoint in checkpoints:
								if os.path.exists(args.predictions_path      ) and (not args.recalculate):
												logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path      )      )
												score_fn(UpperCamelCase__ ,     args.predictions_path ,     args.gold_data_path      )
												continue

								logger.info('***** Running evaluation for {} *****'.format(UpperCamelCase__      )      )
								logger.info('  Batch size = %d' ,     args.eval_batch_size      )
								logger.info('  Predictions will be stored under {}'.format(args.predictions_path      )      )

								if args.model_type.startswith('rag'      ):
												__lowerCamelCase		       =    RagRetriever.from_pretrained(UpperCamelCase__ ,     **UpperCamelCase__      )
												__lowerCamelCase		       =    model_class.from_pretrained(UpperCamelCase__ ,     retriever=UpperCamelCase__ ,     **UpperCamelCase__      )
												model.retriever.init_retrieval()
								else:
												__lowerCamelCase		       =    model_class.from_pretrained(UpperCamelCase__ ,     **UpperCamelCase__      )
								model.to(args.device      )

								with open(args.evaluation_set ,     'r'      ) as eval_file, open(args.predictions_path ,     'w'      ) as preds_file:
												__lowerCamelCase		       =    []
												for line in tqdm(UpperCamelCase__      ):
																questions.append(line.strip()      )
																if len(UpperCamelCase__      ) == args.eval_batch_size:
																				__lowerCamelCase		       =    evaluate_batch_fn(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
																				preds_file.write('\n'.join(UpperCamelCase__      ) + '\n'      )
																				preds_file.flush()
																				__lowerCamelCase		       =    []
												if len(UpperCamelCase__      ) > 0:
																__lowerCamelCase		       =    evaluate_batch_fn(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
																preds_file.write('\n'.join(UpperCamelCase__      )      )
																preds_file.flush()

												score_fn(UpperCamelCase__ ,     args.predictions_path ,     args.gold_data_path      )


if __name__ == "__main__":
					__A        =    get_args()
					main(args)








import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
					    TimesformerForVideoClassification,
					    TimesformerModel,
					)
					from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from transformers import VideoMAEImageProcessor







class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=10			,						lowerCamelCase__=3			,						lowerCamelCase__=2			,						lowerCamelCase__=2			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=32			,						lowerCamelCase__=5			,						lowerCamelCase__=4			,						lowerCamelCase__=37			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=10			,						lowerCamelCase__=0.02			,						lowerCamelCase__="divided_space_time"			,						lowerCamelCase__=None			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    image_size
							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    patch_size
							__lowerCamelCase		       =    num_frames
							__lowerCamelCase		       =    is_training
							__lowerCamelCase		       =    use_labels
							__lowerCamelCase		       =    hidden_size
							__lowerCamelCase		       =    num_hidden_layers
							__lowerCamelCase		       =    num_attention_heads
							__lowerCamelCase		       =    intermediate_size
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    attention_type
							__lowerCamelCase		       =    initializer_range
							__lowerCamelCase		       =    scope
							__lowerCamelCase		       =    num_labels

							# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
							__lowerCamelCase		       =    (image_size // patch_size) ** 2
							__lowerCamelCase		       =    (num_frames) * self.num_patches_per_frame + 1


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    floats_tensor(
							    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

							__lowerCamelCase		       =    None
							if self.use_labels:
											__lowerCamelCase		       =    ids_tensor([self.batch_size]			,						self.num_labels							)

							__lowerCamelCase		       =    self.get_config()

							return config, pixel_values, labels


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerConfig(
							    image_size=self.image_size			,						patch_size=self.patch_size			,						num_channels=self.num_channels			,						num_frames=self.num_frames			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						initializer_range=self.initializer_range			,						attention_type=self.attention_type			,						)
							__lowerCamelCase		       =    self.num_labels
							return config


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__							)
							self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerForVideoClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()

							__lowerCamelCase		       =    model(lowerCamelCase__							)

							# verify the logits shape
							__lowerCamelCase		       =    torch.Size((self.batch_size, self.num_labels)							)
							self.parent.assertEqual(result.logits.shape			,						lowerCamelCase__							)






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    config_and_inputs
							__lowerCamelCase		       =    {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class     __lowerCAmelCase (       __magic_name__			,				__magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
			snake_case_   		=       (
			    {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(
							    self			,						config_class=lowerCamelCase__			,						has_text_modality=lowerCamelCase__			,						hidden_size=37							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=False							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    copy.deepcopy(lowerCamelCase__							)

							if return_labels:
											if model_class in get_values(lowerCamelCase__							):
															__lowerCamelCase		       =    torch.zeros(
															    self.model_tester.batch_size			,						dtype=torch.long			,						device=lowerCamelCase__							)

							return inputs_dict


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			@unittest.skip(reason='TimeSformer does not use inputs_embeds'							)
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											self.assertIsInstance(model.get_input_embeddings()			,						(nn.Module)							)
											__lowerCamelCase		       =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCamelCase__			,						nn.Linear							)							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											__lowerCamelCase		       =    inspect.signature(model.forward							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowerCamelCase		       =    [*signature.parameters.keys()]

											__lowerCamelCase		       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_video_classification(*lowerCamelCase__							)


			@slow
			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    TimesformerModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							if not self.has_attentions:
											pass

							else:
											__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase		       =    True

											for model_class in self.all_model_classes:
															__lowerCamelCase		       =    self.model_tester.seq_length
															__lowerCamelCase		       =    self.model_tester.num_frames

															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    False
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    outputs.attentions
															self.assertEqual(len(lowerCamelCase__							)			,						self.model_tester.num_hidden_layers							)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    outputs.attentions
															self.assertEqual(len(lowerCamelCase__							)			,						self.model_tester.num_hidden_layers							)

															# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
															self.assertListEqual(
															    list(attentions[0].shape[-3:]							)			,						[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,						)
															__lowerCamelCase		       =    len(lowerCamelCase__							)

															# Check attention is always last and order is fine
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)

															self.assertEqual(out_len + 1			,						len(lowerCamelCase__							)							)

															__lowerCamelCase		       =    outputs.attentions

															self.assertEqual(len(lowerCamelCase__							)			,						self.model_tester.num_hidden_layers							)

															# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]							)			,						[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,						)






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							def check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											model.to(lowerCamelCase__							)
											model.eval()

											with torch.no_grad():
															__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)

											__lowerCamelCase		       =    outputs.hidden_states
											__lowerCamelCase		       =    self.model_tester.num_hidden_layers + 1
											self.assertEqual(len(lowerCamelCase__							)			,						lowerCamelCase__							)

											__lowerCamelCase		       =    self.model_tester.seq_length

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]							)			,						[seq_length, self.model_tester.hidden_size]			,						)

							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    True
											check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__lowerCamelCase		       =    True

											check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)







def 			lowerCamelCase_		(       )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video' ,     filename='eating_spaghetti.npy' ,     repo_type='dataset'      )
				__lowerCamelCase		       =    np.load(UpperCamelCase__      )
				return list(UpperCamelCase__      )







@require_torch
@require_vision
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			@cached_property
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							# logits were tested with a different mean and std, so we use the same here
							return (
							    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,						image_std=[0.5, 0.5, 0.5]							)
							    if is_vision_available()
							    else None
							)






			@slow
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'							).to(
							    lowerCamelCase__							)

							__lowerCamelCase		       =    self.default_image_processor
							__lowerCamelCase		       =    prepare_video()
							__lowerCamelCase		       =    image_processor(video[:8]			,						return_tensors='pt'							).to(lowerCamelCase__							)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase		       =    model(**lowerCamelCase__							)

							# verify the logits
							__lowerCamelCase		       =    torch.Size((1, 400)							)
							self.assertEqual(outputs.logits.shape			,						lowerCamelCase__							)

							__lowerCamelCase		       =    torch.tensor([-0.30_16, -0.77_13, -0.42_05]							).to(lowerCamelCase__							)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						lowerCamelCase__			,						atol=1e-4							)							)


