"""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


_UpperCamelCase     :		int											=					['text', 'image', 'audio']





def        a_  (   _lowerCAmelCase						:				List[str]  ):





							'''simple docstring'''

							lowercase__    :	Union[str, Any]     					=			[]

							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(3000  )  )
														elif isinstance(__UpperCamelCase     ,				__UpperCamelCase  ):
																					inputs.append(create_inputs(__UpperCamelCase  )  )
														else:
																					raise ValueError(f"""Invalid type requested: {input_type}"""  )

							return inputs





def        a_  (   _lowerCAmelCase						:				List  ):





							'''simple docstring'''

							lowercase__    :	Any     					=			[]

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

							return output_types






@is_tool_test
class   UpperCAmelCase_			:


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

													lowercase__    :	Dict     					=			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							)

													lowercase__    :	int     					=			self.tool.outputs
													for _output in outputs:
																				self.assertTrue(_output in authorized_types							)


						def 	_UpperCAmelCase (     self							) ->      int:
													lowercase__    :	Optional[Any]     					=			create_inputs(self.tool.inputs							)
													lowercase__    :	Tuple     					=			self.tool(*UpperCamelCase__							)

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

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


						def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
													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 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
													lowercase__    :	Any     					=			create_inputs(self.tool.inputs							)
													lowercase__    :	Optional[Any]     					=			self.tool(*UpperCamelCase__							)

													if not isinstance(UpperCamelCase__	,    UpperCamelCase__							):
																				lowercase__    :	List[Any]     					=			[outputs]

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

													for output, output_type in zip(UpperCamelCase__	,    self.tool.outputs							):
																				lowercase__    :	List[Any]     					=			AGENT_TYPE_MAPPING[output_type]
																				self.assertTrue(isinstance(UpperCamelCase__	,    UpperCamelCase__							)							)


						def 	_UpperCAmelCase (     self							) ->      Optional[int]:
													lowercase__    :	Dict     					=			create_inputs(self.tool.inputs							)

													lowercase__    :	str     					=			[]

													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
													lowercase__    :	Union[str, Any]     					=			self.tool(*UpperCamelCase__							)

													if not isinstance(UpperCamelCase__	,    UpperCamelCase__							):
																				lowercase__    :	List[Any]     					=			[outputs]

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


"""simple docstring"""





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             for model_result in results.values():
                    for batch_size, sequence_length in zip(model_result['bs']	,    model_result['ss']							):
                           lowercase__    :	str     					=			model_result['result'][batch_size][sequence_length]
                           self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	List[str]     					=			'sgugger/tiny-distilbert-classification'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    only_pretrain_model=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	List[str]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[Any]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	Tuple     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Dict     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[str]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	List[str]     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Any     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Tuple     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result							)
             self.check_results_dict_not_empty(results.memory_train_result							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[int]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	str     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result							)
             self.check_results_dict_not_empty(results.memory_train_result							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[str]     					=			'patrickvonplaten/t5-tiny-random'
             lowercase__    :	Any     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	List[str]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	int     					=			TensorFlowBenchmark(a	,    configs=[config]							)
             lowercase__    :	Union[str, Any]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'							)							) == 0	,    'Cannot do xla on CPU.'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    use_xla=a	,    multi_process=a	,    )
             lowercase__    :	Any     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Dict     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[Any]     					=			TensorFlowBenchmarkArguments(
                        models=[MODEL_ID]	,    inference=a	,    save_to_csv=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    inference_time_csv_file=os.path.join(a	,    'inf_time.csv'							)	,    inference_memory_csv_file=os.path.join(a	,    'inf_mem.csv'							)	,    env_info_csv_file=os.path.join(a	,    'env.csv'							)	,    multi_process=a	,    )
                    lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmark(a							)
                    benchmark.run()
                    self.assertTrue(Path(os.path.join(a	,    'inf_time.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'inf_mem.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'env.csv'							)							).exists()							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Tuple     					=			'sshleifer/tiny-gpt2'

             def _check_summary_is_not_empty(a							):
                    self.assertTrue(hasattr(a	,    'sequential'							)							)
                    self.assertTrue(hasattr(a	,    'cumulative'							)							)
                    self.assertTrue(hasattr(a	,    'current'							)							)
                    self.assertTrue(hasattr(a	,    'total'							)							)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                        models=[MODEL_ID]	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    log_filename=os.path.join(a	,    'log.txt'							)	,    log_print=a	,    trace_memory_line_by_line=a	,    eager_mode=a	,    multi_process=a	,    )
                    lowercase__    :	Optional[int]     					=			TensorFlowBenchmark(a							)
                    lowercase__    :	Optional[Any]     					=			benchmark.run()
                    _check_summary_is_not_empty(result.inference_summary							)
                    self.assertTrue(Path(os.path.join(a	,    'log.txt'							)							).exists()							)

