
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


snake_case_      :					Dict				      =     logging.get_logger(__name__)

snake_case_      :					Dict				      =     {
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}





class 		__lowerCamelCase     (   __A		):
							lowerCamelCase__:				Any      =							'plbart'
							lowerCamelCase__:				Optional[Any]      =							['past_key_values']
							lowerCamelCase__:				int      =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



							def __init__(     self	,				__snake_case=5_0_0_0_5	,				__snake_case=1_0_2_4	,				__snake_case=6	,				__snake_case=3_0_7_2	,				__snake_case=1_2	,				__snake_case=6	,				__snake_case=3_0_7_2	,				__snake_case=1_2	,				__snake_case=0.0	,				__snake_case=0.0	,				__snake_case=True	,				__snake_case=True	,				__snake_case="gelu"	,				__snake_case=7_6_8	,				__snake_case=0.1	,				__snake_case=0.1	,				__snake_case=0.0	,				__snake_case=0.02	,				__snake_case=0.0	,				__snake_case=True	,				__snake_case=1	,				__snake_case=0	,				__snake_case=2	,				__snake_case=2	,				**__snake_case	,				)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     vocab_size
								UpperCAmelCase:					Any 						=     max_position_embeddings
								UpperCAmelCase:					Union[str, Any] 						=     d_model
								UpperCAmelCase:					List[str] 						=     encoder_ffn_dim
								UpperCAmelCase:					Optional[int] 						=     encoder_layers
								UpperCAmelCase:					int 						=     encoder_attention_heads
								UpperCAmelCase:					Tuple 						=     decoder_ffn_dim
								UpperCAmelCase:					Optional[Any] 						=     decoder_layers
								UpperCAmelCase:					List[str] 						=     decoder_attention_heads
								UpperCAmelCase:					Optional[int] 						=     dropout
								UpperCAmelCase:					Optional[Any] 						=     attention_dropout
								UpperCAmelCase:					Optional[Any] 						=     activation_dropout
								UpperCAmelCase:					Any 						=     activation_function
								UpperCAmelCase:					List[str] 						=     init_std
								UpperCAmelCase:					Optional[Any] 						=     encoder_layerdrop
								UpperCAmelCase:					Optional[int] 						=     decoder_layerdrop
								UpperCAmelCase:					int 						=     classifier_dropout
								UpperCAmelCase:					List[str] 						=     use_cache
								UpperCAmelCase:					Any 						=     encoder_layers
								UpperCAmelCase:					Tuple 						=     scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=__lowercase	,				bos_token_id=__lowercase	,				eos_token_id=__lowercase	,				is_encoder_decoder=__lowercase	,				forced_eos_token_id=__lowercase	,				**__lowercase	,				)





class 		__lowerCamelCase     (   __A		):



							@property
							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								return OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "sequence"}),
								        ("attention_mask", {0: "batch", 1: "sequence"}),
								    ]							)




							@property
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								if self.use_past:
									return OrderedDict(
									    [
									        ("last_hidden_state", {0: "batch", 1: "sequence"}),
									        ("past_keys", {0: "batch", 2: "sequence"}),
									        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
									    ]							)
								else:
									return OrderedDict(
									    [
									        ("last_hidden_state", {0: "batch", 1: "sequence"}),
									        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
									    ]							)




'''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


_UpperCAmelCase					:			str				     =					[
    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)

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

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser()
						parser.add_argument('''-f''')
						__lowerCAmelCase											=					parser.parse_args()
						return args.f

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

def 				__magic_name__(    ):
						return bool(tf.config.list_physical_devices('''GPU'''))


_UpperCAmelCase					:			str				     =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class    a__					(			__A   ):







				"""simple docstring"""



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

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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_text_classification.main()
																# Reset the mixed precision policy so we don't break other tests
																tf.keras.mixed_precision.set_global_policy('''float32'''		)
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''eval_accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.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(__lowercase							,      '''argv'''							,      __lowercase		):
																run_clm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      1_00		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_mlm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      42		)




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

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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_ner.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_squad.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''f1''']							,      30		)
																self.assertGreaterEqual(result['''exact''']							,      30		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_swag.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''val_accuracy''']							,      0.8		)




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

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




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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_translation.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''bleu''']							,      30		)




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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_image_classification.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7		)

