



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


_lowercase:					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)

_lowercase:					int      =						logging.getLogger()






def        a( )						->			int:
					"""simple docstring"""





					a   	=  argparse.ArgumentParser()
					parser.add_argument("-f"			)
					a   	=  parser.parse_args()
					return args.f






def        a( A			:       List[Any]			)						->			Optional[int]:
					"""simple docstring"""





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






def        a( )						->			Tuple:
					"""simple docstring"""





					return bool(tf.config.list_physical_devices("GPU"			)			)


_lowercase:					Union[str, Any]      =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@skip("Skipping until shape inference for to_tf_dataset PR is merged."						)
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											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(lowerCamelCase_						)
											self.assertGreaterEqual(result["eval_accuracy"]						,			0.75						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_clm.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertLess(result["eval_perplexity"]						,			100						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_mlm.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertLess(result["eval_perplexity"]						,			42						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  7 if get_gpu_count() > 1 else 2

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_ner.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertGreaterEqual(result["accuracy"]						,			0.75						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_squad.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertGreaterEqual(result["f1"]						,			30						)
											self.assertGreaterEqual(result["exact"]						,			30						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_swag.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertGreaterEqual(result["val_accuracy"]						,			0.8						)




	@slow
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_summarization.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertGreaterEqual(result["rouge1"]						,			10						)
											self.assertGreaterEqual(result["rouge2"]						,			2						)
											self.assertGreaterEqual(result["rougeL"]						,			7						)
											self.assertGreaterEqual(result["rougeLsum"]						,			7						)




	@slow
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_translation.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertGreaterEqual(result["bleu"]						,			30						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_auto_remove_tmp_dir()
						a   	=  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(lowerCamelCase_						,			"argv"						,			lowerCamelCase_						):
											run_image_classification.main()
											a   	=  get_results(lowerCamelCase_						)
											self.assertGreaterEqual(result["accuracy"]						,			0.7						)





import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
				import sqlitea

				import sqlalchemy


_lowercase:					Optional[Any]      =						datasets.utils.logging.get_logger(__name__)



@dataclass
class       _lowercase				(  datasets.BuilderConfig      ):





	"""simple docstring"""
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				True
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				10000
	__A		      =				None




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if self.sql is None:
											raise ValueError("sql must be specified"						)
						if self.con is None:
											raise ValueError("con must be specified"						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  config_kwargs.copy()
						# We need to stringify the Selectable object to make its hash deterministic

						# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
						a   	=  config_kwargs["sql"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																import sqlalchemy

																if isinstance(lowerCamelCase_						,			sqlalchemy.sql.Selectable						):
																					a   	=  sqlalchemy.create_engine(config_kwargs["con"].split("://"						)[0] + "://"						)
																					a   	=  str(sql.compile(dialect=engine.dialect						)						)
																					a   	=  sql_str
																else:
																					raise TypeError(
																					    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
											else:
																raise TypeError(
																    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
						a   	=  config_kwargs["con"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											a   	=  id(lowerCamelCase_						)
											logger.info(
											    F'''SQL connection \'con\' of type {type(lowerCamelCase_						)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''						)

						return super().create_config_id(lowerCamelCase_						,			custom_features=lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "index_col": self.index_col,
						    "columns": self.columns,
						    "params": self.params,
						    "coerce_float": self.coerce_float,
						    "parse_dates": self.parse_dates,
						}
						return pd_read_sql_kwargs




class       _lowercase				(  datasets.ArrowBasedBuilder      ):





	"""simple docstring"""
	__A		      =				SqlConfig




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return datasets.DatasetInfo(features=self.config.features						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,			gen_kwargs={}						)]




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if self.config.features is not None:
											a   	=  self.config.features.arrow_schema
											if all(not require_storage_cast(lowerCamelCase_						) for feature in self.config.features.values()						):
																# cheaper cast
																a   	=  pa.Table.from_arrays([pa_table[field.name] for field in schema]						,			schema=lowerCamelCase_						)
											else:
																# more expensive cast; allows str <-> int/float or str to Audio for example
																a   	=  table_cast(lowerCamelCase_						,			lowerCamelCase_						)
						return pa_table




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.config.chunksize
						a   	=  pd.read_sql(
						    self.config.sql						,			self.config.con						,			chunksize=lowerCamelCase_						,			**self.config.pd_read_sql_kwargs						)
						a   	=  [sql_reader] if chunksize is None else sql_reader
						for chunk_idx, df in enumerate(lowerCamelCase_						):
											a   	=  pa.Table.from_pandas(lowerCamelCase_						)
											yield chunk_idx, self._cast_table(lowerCamelCase_						)
