
"""simple docstring"""







import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm
		import torch_xla.debug.metrics as met







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Optional[Any]							,						*snake_case_   :							Tuple							,						snake_case_   :							Tuple=None							,						snake_case_   :							List[str]=None							,						**snake_case_   :							Dict    ):
												super().__init__(*snake_case_							,						**snake_case_    )
												__snake_case     			=       eval_examples
												__snake_case     			=       post_process_function





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Optional[Dataset] = None							,						snake_case_   :							Union[str, Any]=None							,						snake_case_   :							Optional[List[str]] = None							,						snake_case_   :							str = "eval"							,						**snake_case_   :							str							,						):
												__snake_case     			=       gen_kwargs.copy()
												__snake_case     			=       (
												    gen_kwargs["max_length"] if gen_kwargs.get("max_length"    ) is not None else self.args.generation_max_length
												)
												__snake_case     			=       (
												    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"    ) is not None else self.args.generation_num_beams
												)
												__snake_case     			=       gen_kwargs

												__snake_case     			=       self.eval_dataset if eval_dataset is None else eval_dataset
												__snake_case     			=       self.get_eval_dataloader(snake_case_    )
												__snake_case     			=       self.eval_examples if eval_examples is None else eval_examples

												# Temporarily disable metric computation, we will do it in the loop here.
												__snake_case     			=       self.compute_metrics
												__snake_case     			=       None
												__snake_case     			=       time.time()
												__snake_case     			=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																			__snake_case     			=       eval_loop(
																			    snake_case_							,						description="Evaluation"							,						prediction_loss_only=True if compute_metrics is None else None							,						ignore_keys=snake_case_							,						metric_key_prefix=snake_case_							,						)
												finally:
																			__snake_case     			=       compute_metrics
												__snake_case     			=       self.args.eval_batch_size * self.args.world_size
												if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																			start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
												output.metrics.update(
												    speed_metrics(
												        snake_case_							,						snake_case_							,						num_samples=output.num_samples							,						num_steps=math.ceil(output.num_samples / total_batch_size    )							,						)    )

												if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																			# Only the main node write the results by default
																			__snake_case     			=       self.post_process_function(snake_case_							,						snake_case_							,						snake_case_    )
																			__snake_case     			=       self.compute_metrics(snake_case_    )

																			# Prefix all keys with metric_key_prefix + '_'
																			for key in list(metrics.keys()    ):
																										if not key.startswith(F'''{metric_key_prefix}_'''    ):
																																	__snake_case     			=       metrics.pop(snake_case_    )

																			metrics.update(output.metrics    )
												else:
																			__snake_case     			=       output.metrics

												if self.args.should_log:
																			# Only the main node log the results by default
																			self.log(snake_case_    )

												if self.args.tpu_metrics_debug or self.args.debug:
																			# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																			xm.master_print(met.metrics_report()    )

												__snake_case     			=       self.callback_handler.on_evaluate(self.args							,						self.state							,						self.control							,						snake_case_    )
												return metrics





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							str							,						snake_case_   :							List[str]							,						snake_case_   :							str=None							,						snake_case_   :							str = "test"							,						**snake_case_   :							Union[str, Any]    ):
												__snake_case     			=       gen_kwargs.copy()

												__snake_case     			=       self.get_test_dataloader(snake_case_    )

												# Temporarily disable metric computation, we will do it in the loop here.
												__snake_case     			=       self.compute_metrics
												__snake_case     			=       None
												__snake_case     			=       time.time()
												__snake_case     			=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																			__snake_case     			=       eval_loop(
																			    snake_case_							,						description="Prediction"							,						prediction_loss_only=True if compute_metrics is None else None							,						ignore_keys=snake_case_							,						metric_key_prefix=snake_case_							,						)
												finally:
																			__snake_case     			=       compute_metrics

												__snake_case     			=       self.args.eval_batch_size * self.args.world_size
												if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																			start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
												output.metrics.update(
												    speed_metrics(
												        snake_case_							,						snake_case_							,						num_samples=output.num_samples							,						num_steps=math.ceil(output.num_samples / total_batch_size    )							,						)    )
												if self.post_process_function is None or self.compute_metrics is None:
																			return output

												__snake_case     			=       self.post_process_function(snake_case_							,						snake_case_							,						snake_case_							,						"predict"    )
												__snake_case     			=       self.compute_metrics(snake_case_    )

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys()    ):
																			if not key.startswith(F'''{metric_key_prefix}_'''    ):
																										__snake_case     			=       metrics.pop(snake_case_    )
												metrics.update(output.metrics    )
												return PredictionOutput(predictions=predictions.predictions							,						label_ids=predictions.label_ids							,						metrics=snake_case_    )


"""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


_SCREAMING_SNAKE_CASE	       =      ["""text""", """image""", """audio"""]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       []

							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_12, 5_12) ) )
														elif input_type == "audio":
																					inputs.append(torch.ones(30_00 ) )
														elif isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					inputs.append(create_inputs(SCREAMING_SNAKE_CASE ) )
														else:
																					raise ValueError(F'''Invalid type requested: {input_type}''' )

							return inputs
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []

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

							return output_types







@is_tool_test
class 							__magic_name__     :





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												self.assertTrue(hasattr(self.tool							,						"inputs"    )    )
												self.assertTrue(hasattr(self.tool							,						"outputs"    )    )

												__snake_case     			=       self.tool.inputs
												for _input in inputs:
																			if isinstance(_input							,						snake_case_    ):
																										for __input in _input:
																																	self.assertTrue(__input in authorized_types    )
																			else:
																										self.assertTrue(_input in authorized_types    )

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





					def 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

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

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





					def 							lowerCAmelCase      (				self   :							Union[str, 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 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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

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





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )

												__snake_case     			=       []

												for _input, input_type in zip(snake_case_							,						self.tool.inputs    ):
																			if isinstance(snake_case_							,						snake_case_    ):
																										_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
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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