





"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_snake_case					:		Union[str, Any]							=			[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm
		import run_image_classification
		import run_mlm
		import run_ner
		import run_qa as run_squad
		import run_summarization
		import run_swag
		import run_text_classification
		import run_translation


logging.basicConfig(level=logging.DEBUG)

_snake_case					:		Optional[Any]							=			logging.getLogger()






def  A__    (      ):
							A		=  argparse.ArgumentParser()
							parser.add_argument("-f"	)
							A		=  parser.parse_args()
							return args.f






def  A__    (      UpperCamelCase	):
							A		=  {}
							A		=  os.path.join(UpperCamelCase ,  "all_results.json"	)
							if os.path.exists(UpperCamelCase	):
														with open(UpperCamelCase ,  "r"	) as f:
																					A		=  json.load(UpperCamelCase	)
							else:
														raise ValueError(F"can't find {path}"	)
							return results






def  A__    (      ):
							return bool(tf.config.list_physical_devices("GPU"	)	)


_snake_case					:		Optional[Any]							=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class  _UpperCAmelCase       (     lowercase_			):


					@skip("Skipping until shape inference for to_tf_dataset PR is merged."		)
					def      lowerCamelCase	( self      :int		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

												if is_cuda_available():
																			testargs.append("--fp16"		)

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_text_classification.main()
																			# Reset the mixed precision policy so we don't break other tests
																			tf.keras.mixed_precision.set_global_policy("float32"		)
																			A		=  get_results(__UpperCamelCase		)
																			self.assertGreaterEqual(result["eval_accuracy"]					,	0.75		)


					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

												if len(tf.config.list_physical_devices("GPU"		)		) > 1:
																			# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																			return

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_clm.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertLess(result["eval_perplexity"]					,	1_00		)


					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        ".split()

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_mlm.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertLess(result["eval_perplexity"]					,	42		)


					def      lowerCamelCase	( self      :Dict		):
												# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
												A		=  7 if get_gpu_count() > 1 else 2

												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_ner.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertGreaterEqual(result["accuracy"]					,	0.75		)


					def      lowerCamelCase	( self      :List[str]		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_squad.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertGreaterEqual(result["f1"]					,	30		)
																			self.assertGreaterEqual(result["exact"]					,	30		)


					def      lowerCamelCase	( self      :List[str]		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_swag.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertGreaterEqual(result["val_accuracy"]					,	0.8		)


					@slow
					def      lowerCamelCase	( self      :Tuple		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_summarization.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertGreaterEqual(result["rouge1"]					,	10		)
																			self.assertGreaterEqual(result["rouge2"]					,	2		)
																			self.assertGreaterEqual(result["rougeL"]					,	7		)
																			self.assertGreaterEqual(result["rougeLsum"]					,	7		)


					@slow
					def      lowerCamelCase	( self      :Any		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        ".split()

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_translation.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertGreaterEqual(result["bleu"]					,	30		)


					def      lowerCamelCase	( self      :Tuple		):
												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            ".split()

												with patch.object(__UpperCamelCase					,	"argv"					,	__UpperCamelCase		):
																			run_image_classification.main()
																			A		=  get_results(__UpperCamelCase		)
																			self.assertGreaterEqual(result["accuracy"]					,	0.7		)









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Optional[int]							=			logging.get_logger(__name__)

_snake_case					:		List[Any]							=			{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tvlt'''


					def __init__( self      :Dict					,	__UpperCamelCase      :Tuple=2_24					,	__UpperCamelCase      :Dict=20_48					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Optional[int]=[16, 16]					,	__UpperCamelCase      :Any=[16, 16]					,	__UpperCamelCase      :List[str]=3					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=8					,	__UpperCamelCase      :str=7_68					,	__UpperCamelCase      :List[str]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :Union[str, Any]=30_72					,	__UpperCamelCase      :List[Any]="gelu"					,	__UpperCamelCase      :List[str]=0.0					,	__UpperCamelCase      :Union[str, Any]=0.0					,	__UpperCamelCase      :List[str]=0.02					,	__UpperCamelCase      :Dict=1e-6					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :str=16					,	__UpperCamelCase      :List[str]=5_12					,	__UpperCamelCase      :int=8					,	__UpperCamelCase      :int=20_48					,	__UpperCamelCase      :int=0.75					,	__UpperCamelCase      :Tuple=0.15					,	__UpperCamelCase      :Optional[Any]="frame-level"					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :str=True					,	__UpperCamelCase      :Tuple="classification"					,	**__UpperCamelCase      :Any					,	):
												super().__init__(**__UpperCamelCase		)

												if audio_mask_type not in ("frame-level", "patch_level"):
																			raise ValueError(
																			    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
																			    f"got {audio_mask_type}"		)

												A		=  image_size
												A		=  spectrogram_length
												A		=  frequency_length
												A		=  image_patch_size
												A		=  audio_patch_size
												A		=  num_image_channels
												A		=  num_audio_channels
												A		=  num_frames

												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps
												A		=  qkv_bias
												A		=  use_mean_pooling

												A		=  decoder_num_attention_heads
												A		=  decoder_hidden_size
												A		=  decoder_num_hidden_layers
												A		=  decoder_intermediate_size
												A		=  pixel_mask_ratio
												A		=  audio_mask_ratio
												A		=  audio_mask_type

												A		=  task_matching
												A		=  task_mae
												A		=  loss_type


