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


__UpperCamelCase	: Optional[Any]    		=  ['text', 'image', 'audio']
def    A			(					_lowercase		)     ->   Dict:
						SCREAMING_SNAKE_CASE							:					Dict           =							[]

						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((512, 512)		)		)
												elif input_type == "audio":
																		inputs.append(torch.ones(3_000		)		)
												elif isinstance(_lowercase	,						_lowercase		):
																		inputs.append(create_inputs(_lowercase		)		)
												else:
																		raise ValueError(f"""Invalid type requested: {input_type}"""		)

						return inputs
def    A			(					_lowercase		)     ->   List[str]:
						SCREAMING_SNAKE_CASE							:					Dict           =							[]

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

						return output_types





@is_tool_test
class       lowercase__		:


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





												'''simple docstring'''





												self.assertTrue(hasattr(self.tool					,	'''inputs'''		)		)
												self.assertTrue(hasattr(self.tool					,	'''outputs'''		)		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							self.tool.inputs
												for _input in inputs:
																		if isinstance(_input					,	UpperCamelCase__		):
																								for __input in _input:
																														self.assertTrue(__input in authorized_types		)
																		else:
																								self.assertTrue(_input in authorized_types		)

												SCREAMING_SNAKE_CASE							:					Dict           =							self.tool.outputs
												for _output in outputs:
																		self.assertTrue(_output in authorized_types		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							create_inputs(self.tool.inputs		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.tool(*UpperCamelCase__		)

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

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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												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			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							create_inputs(self.tool.inputs		)
												SCREAMING_SNAKE_CASE							:					str           =							self.tool(*UpperCamelCase__		)

												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[outputs]

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

												for output, output_type in zip(UpperCamelCase__					,	self.tool.outputs		):
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							AGENT_TYPE_MAPPING[output_type]
																		self.assertTrue(isinstance(UpperCamelCase__					,	UpperCamelCase__		)		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_inputs(self.tool.inputs		)

												SCREAMING_SNAKE_CASE							:					Dict           =							[]

												for _input, input_type in zip(UpperCamelCase__					,	self.tool.inputs		):
																		if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								_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
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tool(*UpperCamelCase__		)

												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							[outputs]

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






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
					from sagemaker import Session, TrainingJobAnalytics
					from sagemaker.huggingface import HuggingFace





@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""			,							"""False""")) is not True			,							reason="""Skipping test because should only be run when releasing minor transformers version"""			,							)
@pytest.mark.usefixtures("""sm_env""")
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ])
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Any		):





												'''simple docstring'''





												if self.framework == "pytorch":
																		subprocess.run(
																		    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()					,	encoding='''utf-8'''					,	check=UpperCamelCase__					,	)
												assert hasattr(self					,	'''env'''		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
												# distributed data settings
												SCREAMING_SNAKE_CASE							:					Any           =							{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

												# creates estimator
												return HuggingFace(
												    entry_point=self.script					,	source_dir=self.env.test_path					,	role=self.env.role					,	image_uri=self.env.image_uri					,	base_job_name=UpperCamelCase__					,	instance_count=UpperCamelCase__					,	instance_type=self.instance_type					,	debugger_hook_config=UpperCamelCase__					,	hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,	metric_definitions=self.env.metric_definitions					,	distribution=UpperCamelCase__					,	py_version='''py36'''					,	)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												TrainingJobAnalytics(UpperCamelCase__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)



						@parameterized.expand([(2,)]		)
						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.create_estimator(UpperCamelCase__		)

												# run training
												estimator.fit()

												# result dataframe
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

												# extract kpis
												SCREAMING_SNAKE_CASE							:					List[str]           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
												SCREAMING_SNAKE_CASE							:					Dict           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												SCREAMING_SNAKE_CASE							:					List[Any]           =							(
												    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''					,	99_9999		)
												)

												# assert kpis
												assert train_runtime <= self.results["train_runtime"]
												assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy		)
												assert all(t <= self.results['''eval_loss'''] for t in eval_loss		)

												# dump tests result into json file to share in PR
												with open(f"""{estimator.latest_training_job.name}.json"""					,	'''w'''		) as outfile:
																		json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}					,	UpperCamelCase__		)





