



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


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





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []

    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   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []

    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 					_A :



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

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

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

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



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       create_inputs(self.tool.inputs)
           __a		 =       self.tool(*__SCREAMING_SNAKE_CASE)

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

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



       def 	_lowerCamelCase    (	self							:  Optional[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 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       create_inputs(self.tool.inputs)
           __a		 =       self.tool(*__SCREAMING_SNAKE_CASE)

           if not isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       [outputs]

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

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





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       create_inputs(self.tool.inputs)

           __a		 =       []

           for _input, input_type in zip(__SCREAMING_SNAKE_CASE  ,	self.tool.inputs):
               if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_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
           __a		 =       self.tool(*__SCREAMING_SNAKE_CASE)

           if not isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       [outputs]

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










# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

__snake_case		:str   	=  get_logger()

__snake_case		:Optional[dict]   	=  None

class 					_A (			TensorFormatter[Mapping, '''jax.Array''', Mapping]					):



       def __init__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  int=None  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=None  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           super().__init__(features=__SCREAMING_SNAKE_CASE)
           import jax
           from jaxlib.xla_client import Device

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               raise ValueError(
                   F'Expected {device} to be a `str` not {type(__SCREAMING_SNAKE_CASE)}, as `jaxlib.xla_extension.Device` '
                   '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
                   '''the device with `str()` to get its string identifier that will be internally mapped '''
                   '''to the actual `jaxlib.xla_extension.Device`.''')
           __a		 =       device if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) else str(jax.devices()[0])
           # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
           # with `pickle` nor with `dill`, so we need to use a global variable instead
           global DEVICE_MAPPING
           if DEVICE_MAPPING is None:
               __a		 =       self._map_devices_to_str()
           if self.device not in list(DEVICE_MAPPING.keys()):
               logger.warning(
                   F'Device with string identifier {self.device} not listed among the available '
                   F'devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default '
                   F'device: {str(jax.devices()[0])}.')
               __a		 =       str(jax.devices()[0])
           __a		 =       jnp_array_kwargs



       @staticmethod
       def 	_lowerCamelCase    (	):


           '''simple docstring'''

           import jax

           return {str(__SCREAMING_SNAKE_CASE): device for device in jax.devices()}



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           import jax
           import jax.numpy as jnp

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) and column:
               if all(
                   isinstance(__SCREAMING_SNAKE_CASE  ,	jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
                   return jnp.stack(__SCREAMING_SNAKE_CASE  ,	axis=0)
           return column



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           import jax
           import jax.numpy as jnp

           if isinstance(__SCREAMING_SNAKE_CASE  ,	(str, bytes, type(__SCREAMING_SNAKE_CASE))):
               return value
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	(np.character, np.ndarray)) and np.issubdtype(value.dtype  ,	np.character):
               return value.tolist()

           __a		 =       {}

           if isinstance(__SCREAMING_SNAKE_CASE  ,	(np.number, np.ndarray)) and np.issubdtype(value.dtype  ,	np.integer):
               # the default int precision depends on the jax config
               # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
               if jax.config.jax_enable_xaa:
                   __a		 =       {'''dtype''': jnp.intaa}
               else:
                   __a		 =       {'''dtype''': jnp.intaa}
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	(np.number, np.ndarray)) and np.issubdtype(value.dtype  ,	np.floating):
               __a		 =       {'''dtype''': jnp.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
                   __a		 =       np.asarray(__SCREAMING_SNAKE_CASE)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
           global DEVICE_MAPPING
           if DEVICE_MAPPING is None:
               __a		 =       self._map_devices_to_str()

           with jax.default_device(DEVICE_MAPPING[self.device]):
               # calling jnp.array on a np.ndarray does copy the data
               # see https://github.com/google/jax/issues/4486
               return jnp.array(__SCREAMING_SNAKE_CASE  ,	**{**default_dtype, **self.jnp_array_kwargs})



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           import jax

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch

               if isinstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()])
           if hasattr(__SCREAMING_SNAKE_CASE  ,	'''__array__''') and not isinstance(__SCREAMING_SNAKE_CASE  ,	jax.Array):
               __a		 =       data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(__SCREAMING_SNAKE_CASE  ,	np.ndarray):
               if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__SCREAMING_SNAKE_CASE) for substruct in data_struct])
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	(list, tuple)):
               return self._consolidate([self.recursive_tensorize(__SCREAMING_SNAKE_CASE) for substruct in data_struct])
           return self._tensorize(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  dict):


           '''simple docstring'''

           return map_nested(self._recursive_tensorize  ,	__SCREAMING_SNAKE_CASE  ,	map_list=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  pa.Table):


           '''simple docstring'''

           __a		 =       self.numpy_arrow_extractor().extract_row(__SCREAMING_SNAKE_CASE)
           __a		 =       self.python_features_decoder.decode_row(__SCREAMING_SNAKE_CASE)
           return self.recursive_tensorize(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  pa.Table):


           '''simple docstring'''

           __a		 =       self.numpy_arrow_extractor().extract_column(__SCREAMING_SNAKE_CASE)
           __a		 =       self.python_features_decoder.decode_column(__SCREAMING_SNAKE_CASE  ,	pa_table.column_names[0])
           __a		 =       self.recursive_tensorize(__SCREAMING_SNAKE_CASE)
           __a		 =       self._consolidate(__SCREAMING_SNAKE_CASE)
           return column





       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  pa.Table):


           '''simple docstring'''

           __a		 =       self.numpy_arrow_extractor().extract_batch(__SCREAMING_SNAKE_CASE)
           __a		 =       self.python_features_decoder.decode_batch(__SCREAMING_SNAKE_CASE)
           __a		 =       self.recursive_tensorize(__SCREAMING_SNAKE_CASE)
           for column_name in batch:
               __a		 =       self._consolidate(batch[column_name])
           return batch





