





# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


__A     =    Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

__A     =    {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

__A     =    "zero2"
__A     =    "zero3"
__A     =    [ZEROa, ZEROa]





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =parameterized.to_safe_name("_".join(str(__a		) for x in param.args		)		)
  return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__A     =    list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @parameterized.expand(UpperCAmelCase_	,			name_func=UpperCAmelCase_)
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       int)	->str:



   '''simple docstring'''



   self.run_and_check(
       stage=UpperCAmelCase_	,			model=UpperCAmelCase_	,			distributed=UpperCAmelCase_	,			fpaa=UpperCAmelCase_	,			)

 @require_torch_multi_gpu
 @parameterized.expand(UpperCAmelCase_	,			name_func=UpperCAmelCase_)
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       List[str])	->str:



   '''simple docstring'''



   self.run_and_check(
       stage=UpperCAmelCase_	,			model=UpperCAmelCase_	,			distributed=UpperCAmelCase_	,			fpaa=UpperCAmelCase_	,			)

 @parameterized.expand(UpperCAmelCase_	,			name_func=UpperCAmelCase_)
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       Any)	->List[str]:



   '''simple docstring'''



   self.run_and_check(
       stage=UpperCAmelCase_	,			model=UpperCAmelCase_	,			distributed=UpperCAmelCase_	,			fpaa=UpperCAmelCase_	,			)

 @require_torch_multi_gpu
 @parameterized.expand(UpperCAmelCase_	,			name_func=UpperCAmelCase_)
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Optional[Any])	->Dict:



   '''simple docstring'''



   self.run_and_check(
       stage=UpperCAmelCase_	,			model=UpperCAmelCase_	,			distributed=UpperCAmelCase_	,			fpaa=UpperCAmelCase_	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       str)	->Dict:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       int = 10	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       bool = True	,			)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =models[model]

   lowerCamelCase__:				Dict        =self.run_trainer(
       stage=UpperCAmelCase_	,			model_name=UpperCAmelCase_	,			eval_steps=UpperCAmelCase_	,			num_train_epochs=1	,			distributed=UpperCAmelCase_	,			fpaa=UpperCAmelCase_	,			)

   self.do_checks(UpperCAmelCase_)

   return output_dir

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       int = 10	,			UpperCAmelCase_    :       int = 1	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       bool = True	,			)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.get_auto_remove_tmp_dir("./xxx"	,			after=UpperCAmelCase_)
   lowerCamelCase__:				Dict        =F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(UpperCAmelCase_)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

   if fpaa:
     args.extend(["--fp16"])

   # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
   # hence the separate config files
   lowerCamelCase__:				Dict        =F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
   lowerCamelCase__:				List[Any]        =[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
   lowerCamelCase__:				Dict        =self.get_launcher(UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =launcher + script + args + ds_args
   # keep for quick debug
   # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
   execute_subprocess_async(UpperCAmelCase_	,			env=self.get_env())

   return output_dir







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Tuple=False)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =min(2	,			get_gpu_count()) if distributed else 1
   return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()








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


__A     =    ["text", "image", "audio"]





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =[]

  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(__a     ,   __a		):
      inputs.append(create_inputs(__a		)		)
    else:
      raise ValueError(F"""Invalid type requested: {input_type}"""		)

  return inputs





def 					lowerCAmelCase_			(	__a		)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

  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        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



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

   lowerCamelCase__:				Tuple        =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)

   lowerCamelCase__:				Optional[Any]        =self.tool.outputs
   for _output in outputs:
     self.assertTrue(_output in authorized_types)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				str        =self.tool(*UpperCAmelCase_)

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

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				str        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				Dict        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Tuple        =[outputs]

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

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =create_inputs(self.tool.inputs)

   lowerCamelCase__:				int        =[]

   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
   lowerCamelCase__:				Union[str, Any]        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				str        =[outputs]

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

