
"""simple docstring"""



import math

import flax.linen as nn
import jax.numpy as jnp
def    _UpperCAmelCase (					__lowerCamelCase  :			List[Any]					,   __lowerCamelCase  :			Any					,   __lowerCamelCase  :			Tuple = 1					,   __lowerCamelCase  :			Dict = 1					,   __lowerCamelCase  :			List[str] = 1.0E4					,   __lowerCamelCase  :			Optional[int] = False					,   __lowerCamelCase  :			Union[str, Any] = 1.0					,   )	->   Optional[int]:
  assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
  assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
  _snake_case   	=			float(embedding_dim // 2						)
  _snake_case   	=			math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
  _snake_case   	=			min_timescale * jnp.exp(jnp.arange(lowerCamelCase_					,   dtype=jnp.floataa						) * -log_timescale_increment						)
  _snake_case   	=			jnp.expand_dims(lowerCamelCase_					,   1						) * jnp.expand_dims(lowerCamelCase_					,   0						)

  # scale embeddings
  _snake_case   	=			scale * emb

  if flip_sin_to_cos:
    _snake_case   	=			jnp.concatenate([jnp.cos(lowerCamelCase_						), jnp.sin(lowerCamelCase_						)]					,   axis=1						)
  else:
    _snake_case   	=			jnp.concatenate([jnp.sin(lowerCamelCase_						), jnp.cos(lowerCamelCase_						)]					,   axis=1						)
  _snake_case   	=			jnp.reshape(lowerCamelCase_					,   [jnp.shape(lowerCamelCase_						)[0], embedding_dim]						)
  return signal

class  lowerCAmelCase__							(		nn.Module      ):
     __a         =   32
     __a         =   jnp.floataa







     @nn.compact
     def __call__(				self     :     Any       , _lowerCamelCase     :     Union[str, Any]     ):
       _snake_case   	=			nn.Dense(self.time_embed_dim       , dtype=self.dtype       , name='''linear_1'''     )(a__     )
       _snake_case   	=			nn.silu(a__     )
       _snake_case   	=			nn.Dense(self.time_embed_dim       , dtype=self.dtype       , name='''linear_2'''     )(a__     )
       return temb






class  lowerCAmelCase__							(		nn.Module      ):
     __a         =   32
     __a         =   False
     __a         =   1







     @nn.compact
     def __call__(				self     :     Optional[int]       , _lowerCamelCase     :     Union[str, Any]     ):
       return get_sinusoidal_embeddings(
           a__       , embedding_dim=self.dim       , flip_sin_to_cos=self.flip_sin_to_cos       , freq_shift=self.freq_shift     )





'''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


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


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

 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_1_2, 5_1_2)			)			)
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0			)			)
  elif isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
   inputs.append(create_inputs(lowerCamelCase_			)			)
  else:
   raise ValueError(f'''Invalid type requested: {input_type}'''			)

 return inputs


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

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

 return output_types







@is_tool_test
class    lowerCAmelCase       :


 def    _A	(    self  : Any    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   Dict     		=	self.tool.inputs
  for _input in inputs:
   if isinstance(_input			,							a__    ):
    for __input in _input:
     self.assertTrue(__input in authorized_types    )
   else:
    self.assertTrue(_input in authorized_types    )

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


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

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

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


 def    _A	(    self  : List[str]    ):
  '''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  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   List[str]     		=	[outputs]

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

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


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	create_inputs(self.tool.inputs    )

  lowerCAmelCase__     :   Dict     		=	[]

  for _input, input_type in zip(a__			,							self.tool.inputs    ):
   if isinstance(a__			,							a__    ):
    _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__     :   Any     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   Tuple     		=	[outputs]

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





