


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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowerCAmelCase   : Union[str, Any]             =				[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-classification""",
        """language-modeling""",
        """summarization""",
        """token-classification""",
        """question-answering""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
					import run_clm_flax
					import run_flax_glue
					import run_flax_ner
					import run_mlm_flax
					import run_qa
					import run_summarization_flax
					import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

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




def 	_A  (       )  ->				Any:
	lowercase						:		Union[str, Any]   					=		argparse.ArgumentParser()
	parser.add_argument("-f"							)
	lowercase						:		Any   					=		parser.parse_args()
	return args.f




def 	_A  (       A							,A="eval"							)  ->				Optional[int]:
	lowercase						:		List[Any]   					=		os.path.join(A							,F'''{split}_results.json'''							)
	if os.path.exists(A							):
		with open(A							,"r"							) as f:
			return json.load(A							)
	raise ValueError(F'''can\'t find {path}'''							)


lowerCAmelCase   : Optional[int]             =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Optional[int]   					=		self.get_auto_remove_tmp_dir()
					lowercase						:		List[str]   					=		F'''
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

					with patch.object(a_							,      "argv"							,      a_							):
						run_flax_glue.main()
						lowercase						:		int   					=		get_results(a_							)
						self.assertGreaterEqual(result["eval_accuracy"]							,      0.75							)

				@slow
				def 	a__		(     self							)    ->  str:
					lowercase						:		int   					=		self.get_auto_remove_tmp_dir()
					lowercase						:		Optional[Any]   					=		F'''
            run_clm_flax.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 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

					with patch.object(a_							,      "argv"							,      a_							):
						run_clm_flax.main()
						lowercase						:		Tuple   					=		get_results(a_							)
						self.assertLess(result["eval_perplexity"]							,      1_0_0							)

				@slow
				def 	a__		(     self							)    ->  int:
					lowercase						:		List[Any]   					=		self.get_auto_remove_tmp_dir()
					lowercase						:		Tuple   					=		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
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        '''.split()

					with patch.object(a_							,      "argv"							,      a_							):
						run_summarization_flax.main()
						lowercase						:		Union[str, Any]   					=		get_results(a_							,      split="test"							)
						self.assertGreaterEqual(result["test_rouge1"]							,      1_0							)
						self.assertGreaterEqual(result["test_rouge2"]							,      2							)
						self.assertGreaterEqual(result["test_rougeL"]							,      7							)
						self.assertGreaterEqual(result["test_rougeLsum"]							,      7							)

				@slow
				def 	a__		(     self							)    ->  str:
					lowercase						:		Optional[Any]   					=		self.get_auto_remove_tmp_dir()
					lowercase						:		Optional[int]   					=		F'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        '''.split()

					with patch.object(a_							,      "argv"							,      a_							):
						run_mlm_flax.main()
						lowercase						:		Union[str, Any]   					=		get_results(a_							)
						self.assertLess(result["eval_perplexity"]							,      4_2							)

				@slow
				def 	a__		(     self							)    ->  str:
					lowercase						:		Optional[Any]   					=		self.get_auto_remove_tmp_dir()
					lowercase						:		Tuple   					=		F'''
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

					with patch.object(a_							,      "argv"							,      a_							):
						run_ta_mlm_flax.main()
						lowercase						:		List[str]   					=		get_results(a_							)
						self.assertGreaterEqual(result["eval_accuracy"]							,      0.42							)

				@slow
				def 	a__		(     self							)    ->  Any:
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					lowercase						:		str   					=		7 if get_gpu_count() > 1 else 2

					lowercase						:		Optional[Any]   					=		self.get_auto_remove_tmp_dir()
					lowercase						:		List[Any]   					=		F'''
            run_flax_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
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

					with patch.object(a_							,      "argv"							,      a_							):
						run_flax_ner.main()
						lowercase						:		Any   					=		get_results(a_							)
						self.assertGreaterEqual(result["eval_accuracy"]							,      0.75							)
						self.assertGreaterEqual(result["eval_f1"]							,      0.3							)


				@slow
				def 	a__		(     self							)    ->  Any:
					lowercase						:		Union[str, Any]   					=		self.get_auto_remove_tmp_dir()
					lowercase						:		Tuple   					=		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
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(a_							,      "argv"							,      a_							):
						run_qa.main()
						lowercase						:		int   					=		get_results(a_							)
						self.assertGreaterEqual(result["eval_f1"]							,      3_0							)
						self.assertGreaterEqual(result["eval_exact"]							,      3_0							)






'''simple docstring'''
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image


lowerCAmelCase   : List[Any]             =				["""text""", """image""", """audio"""]




def 	_A  (       A							)  ->				Dict:
	lowercase						:		str   					=		[]

	for input_type in input_types:
		if input_type == "text":
			inputs.append("Text input"							)
		elif input_type == "image":
			inputs.append(
			    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / "000000039769.png"							).resize((5_1_2, 5_1_2)							)							)
		elif input_type == "audio":
			inputs.append(torch.ones(3_0_0_0							)							)
		elif isinstance(A							,A							):
			inputs.append(create_inputs(A							)							)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''							)

	return inputs




def 	_A  (       A							)  ->				str:
	lowercase						:		Tuple   					=		[]

	for output in outputs:
		if isinstance(A							,(str, AgentText)							):
			output_types.append("text"							)
		elif isinstance(A							,(Image.Image, AgentImage)							):
			output_types.append("image"							)
		elif isinstance(A							,(torch.Tensor, AgentAudio)							):
			output_types.append("audio"							)
		else:
			raise ValueError(F'''Invalid output: {output}'''							)

	return output_types






@is_tool_test
class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(hasattr(self.tool							,      "inputs"							)							)
					self.assertTrue(hasattr(self.tool							,      "outputs"							)							)

					lowercase						:		Optional[Any]   					=		self.tool.inputs
					for _input in inputs:
						if isinstance(_input							,      a_							):
							for __input in _input:
								self.assertTrue(__input in authorized_types							)
						else:
							self.assertTrue(_input in authorized_types							)

					lowercase						:		Any   					=		self.tool.outputs
					for _output in outputs:
						self.assertTrue(_output in authorized_types							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		create_inputs(self.tool.inputs							)
					lowercase						:		Tuple   					=		self.tool(*a_							)

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

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

				def 	a__		(     self							)    ->  List[str]:
					self.assertTrue(hasattr(self.tool							,      "description"							)							)
					self.assertTrue(hasattr(self.tool							,      "default_checkpoint"							)							)
					self.assertTrue(self.tool.description.startswith("This is a tool that"							)							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		create_inputs(self.tool.inputs							)
					lowercase						:		str   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		[outputs]

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

					for output, output_type in zip(a_							,      self.tool.outputs							):
						lowercase						:		List[str]   					=		AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(a_							,      a_							)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		int   					=		create_inputs(self.tool.inputs							)

					lowercase						:		str   					=		[]

					for _input, input_type in zip(a_							,      self.tool.inputs							):
						if isinstance(a_							,      a_							):
							_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
					lowercase						:		Optional[int]   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		str   					=		[outputs]

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


