
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowerCamelCase_						     =					'''scheduler_config.json'''




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      1
  SCREAMING_SNAKE_CASE__         =      2
  SCREAMING_SNAKE_CASE__         =      3
  SCREAMING_SNAKE_CASE__         =      4
  SCREAMING_SNAKE_CASE__         =      5






@dataclass
class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      42






class      __A:

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      SCHEDULER_CONFIG_NAME
  SCREAMING_SNAKE_CASE__         =      ["""dtype"""]
  SCREAMING_SNAKE_CASE__         =      []
  SCREAMING_SNAKE_CASE__         =      True







  @classmethod
  def 	UpperCAmelCase_ (cls	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_=False	,      **SCREAMING_SNAKE_CASE_	,      ):
       UpperCamelCase__		,	UpperCamelCase__				=    cls.load_config(
           pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_	,      subfolder=SCREAMING_SNAKE_CASE_	,      return_unused_kwargs=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_	,      )
       UpperCamelCase__		,	UpperCamelCase__				=    cls.from_config(SCREAMING_SNAKE_CASE_	,      return_unused_kwargs=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)

       if hasattr(SCREAMING_SNAKE_CASE_	,      """create_state"""						) and getattr(SCREAMING_SNAKE_CASE_	,      """has_state"""	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    scheduler.create_state()

       if return_unused_kwargs:
            return scheduler, state, unused_kwargs

       return scheduler, state







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = False	,      **SCREAMING_SNAKE_CASE_						):
       self.save_config(save_directory=SCREAMING_SNAKE_CASE_	,      push_to_hub=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self._get_compatibles()







  @classmethod
  def 	UpperCAmelCase_ (cls						):
       UpperCamelCase__				=    list(set([cls.__name__] + cls._compatibles						)						)
       UpperCamelCase__				=    importlib.import_module(__name__.split("""."""						)[0]						)
       UpperCamelCase__				=    [
           getattr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       ]
       return compatible_classes






def 						__magic_name__			(   __a      :	jnp.ndarray	,		__a      :	Tuple[int]						):



     '''simple docstring'''




     assert len(__a						) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__a						) - x.ndim)						)	,		__a						)






def 						__magic_name__			(   __a      :	int	,		__a      :	Optional[int]=0.999	,		__a      :	Union[str, Any]=jnp.floataa						):



     '''simple docstring'''




     def alpha_bar(__a      :	Tuple						):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2						) ** 2

     UpperCamelCase__				=    []
     for i in range(__a						):
          UpperCamelCase__				=    i / num_diffusion_timesteps
          UpperCamelCase__				=    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(__a						) / alpha_bar(__a						)	,		__a						)						)
     return jnp.array(__a	,		dtype=__a						)




@flax.struct.dataclass
class      __A:

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      42
  SCREAMING_SNAKE_CASE__         =      42
  SCREAMING_SNAKE_CASE__         =      42







  @classmethod
  def 	UpperCAmelCase_ (cls	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    scheduler.config

       if config.trained_betas is not None:
            UpperCamelCase__				=    jnp.asarray(config.trained_betas	,      dtype=scheduler.dtype						)
       elif config.beta_schedule == "linear":
            UpperCamelCase__				=    jnp.linspace(config.beta_start	,      config.beta_end	,      config.num_train_timesteps	,      dtype=scheduler.dtype						)
       elif config.beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            UpperCamelCase__				=    (
                jnp.linspace(
                    config.beta_start**0.5	,      config.beta_end**0.5	,      config.num_train_timesteps	,      dtype=scheduler.dtype						)
                ** 2
            )
       elif config.beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            UpperCamelCase__				=    betas_for_alpha_bar(config.num_train_timesteps	,      dtype=scheduler.dtype						)
       else:
            raise NotImplementedError(
                F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"						)

       UpperCamelCase__				=    1.0 - betas

       UpperCamelCase__				=    jnp.cumprod(SCREAMING_SNAKE_CASE_	,      axis=0						)

       return cls(
           alphas=SCREAMING_SNAKE_CASE_	,      betas=SCREAMING_SNAKE_CASE_	,      alphas_cumprod=SCREAMING_SNAKE_CASE_	,      )






def 						__magic_name__			(   __a      :	CommonSchedulerState	,		__a      :	jnp.ndarray	,		__a      :	jnp.ndarray	,		__a      :	jnp.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    state.alphas_cumprod

     UpperCamelCase__				=    alphas_cumprod[timesteps] ** 0.5
     UpperCamelCase__				=    sqrt_alpha_prod.flatten()
     UpperCamelCase__				=    broadcast_to_shape_from_left(__a	,		original_samples.shape						)

     UpperCamelCase__				=    (1 - alphas_cumprod[timesteps]) ** 0.5
     UpperCamelCase__				=    sqrt_one_minus_alpha_prod.flatten()
     UpperCamelCase__				=    broadcast_to_shape_from_left(__a	,		original_samples.shape						)

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def 						__magic_name__			(   __a      :	CommonSchedulerState	,		__a      :	jnp.ndarray	,		__a      :	jnp.ndarray	,		__a      :	jnp.ndarray						):



     '''simple docstring'''




     UpperCamelCase__		,	UpperCamelCase__				=    get_sqrt_alpha_prod(__a	,		__a	,		__a	,		__a						)
     UpperCamelCase__				=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples






def 						__magic_name__			(   __a      :	CommonSchedulerState	,		__a      :	jnp.ndarray	,		__a      :	jnp.ndarray	,		__a      :	jnp.ndarray						):



     '''simple docstring'''




     UpperCamelCase__		,	UpperCamelCase__				=    get_sqrt_alpha_prod(__a	,		__a	,		__a	,		__a						)
     UpperCamelCase__				=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity







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


lowerCamelCase_						     =					['''text''', '''image''', '''audio''']






def 						__magic_name__			(   __a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    []

     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(3_000						)						)
          elif isinstance(__a	,		__a						):
               inputs.append(create_inputs(__a						)						)
          else:
               raise ValueError(f"Invalid type requested: {input_type}"						)

     return inputs






def 						__magic_name__			(   __a      :	List						):



     '''simple docstring'''




     UpperCamelCase__				=    []

     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      __A:

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       self.assertTrue(hasattr(self.tool	,      """inputs"""						)						)
       self.assertTrue(hasattr(self.tool	,      """outputs"""						)						)

       UpperCamelCase__				=    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						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

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

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







  def 	UpperCAmelCase_ (self						):
       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						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

       if not isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    [outputs]

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)

       UpperCamelCase__				=    []

       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
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

       if not isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    [outputs]

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





