
'''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__				:	Dict					=						[
    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__				:	Dict					=						logging.getLogger()







def 					A       ( )   ->    str:







					'''simple docstring'''



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







def 					A       ( UpperCamelCase_   :       Tuple )   ->    str:







					'''simple docstring'''



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







def 					A       ( )   ->    Any:







					'''simple docstring'''



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


UpperCAmelCase__				:	List[Any]					=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 				A       (		lowercase__      ):





	@skip("Skipping until shape inference for to_tf_dataset PR is merged."							)
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


						"""simple docstring"""

						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(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"							)
											lowerCAmelCase__									=							get_results(lowerCAmelCase__							)
											self.assertGreaterEqual(result["eval_accuracy"] ,							0.75							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


						"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


						"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


						"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


						"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


						"""simple docstring"""

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





	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


						"""simple docstring"""

						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(lowerCAmelCase__ ,							"argv" ,							lowerCAmelCase__							):
											run_summarization.main()
											lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


						"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


						"""simple docstring"""

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







'''simple docstring'''

import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types






class 				A       (		unittest.TestCase					, SCREAMING_SNAKE_CASE__      ):





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							load_tool("translation"							)
				self.tool.setup()
				lowerCAmelCase__									=							load_tool("translation" ,							remote=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.tool("Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.remote_tool("Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.tool(text="Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.remote_tool(text="Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							["Hey, what's up?", "English", "Spanish"]
				lowerCAmelCase__									=							self.tool(*__magic_name__							)

				# There is a single output
				if len(self.tool.outputs							) == 1:
							lowerCAmelCase__									=							[outputs]

				self.assertListEqual(output_types(__magic_name__							) ,							self.tool.outputs							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							["Hey, what's up?", "English", "Spanish"]
				lowerCAmelCase__									=							self.tool(*__magic_name__							)

				if not isinstance(__magic_name__ ,							__magic_name__							):
							lowerCAmelCase__									=							[outputs]

				self.assertEqual(len(__magic_name__							) ,							len(self.tool.outputs							)							)

				for output, output_type in zip(__magic_name__ ,							self.tool.outputs							):
							lowerCAmelCase__									=							AGENT_TYPE_MAPPING[output_type]
							self.assertTrue(isinstance(__magic_name__ ,							__magic_name__							)							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							["Hey, what's up?", "English", "Spanish"]

				lowerCAmelCase__									=							[]

				for _input, input_type in zip(__magic_name__ ,							self.tool.inputs							):
							if isinstance(__magic_name__ ,							__magic_name__							):
										_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input							) for _input_type in input_type]							)
							else:
										_inputs.append(AGENT_TYPE_MAPPING[input_type](_input							)							)

        # Should not raise an error
				lowerCAmelCase__									=							self.tool(*__magic_name__							)

				if not isinstance(__magic_name__ ,							__magic_name__							):
							lowerCAmelCase__									=							[outputs]

				self.assertEqual(len(__magic_name__							) ,							len(self.tool.outputs							)							)





