
import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
     from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
     from tensorflow.keras.optimizers import Adam



class       _snake_case   (	tf.keras.optimizers.schedules.LearningRateSchedule							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = 1.0					,  SCREAMING_SNAKE_CASE_ = None					,  ):


   '''simple docstring'''




   super().__init__()
   lowercase__  :   int    		=      initial_learning_rate
   lowercase__  :   Union[str, Any]    		=      warmup_steps
   lowercase__  :   List[Any]    		=      power
   lowercase__  :   Dict    		=      decay_schedule_fn
   lowercase__  :   Optional[Any]    		=      name


 def __call__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   with tf.name_scope(self.name or """WarmUp""") as name:
     # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
     # learning rate will be `global_step/num_warmup_steps * init_lr`.
     lowercase__  :   List[str]    		=      tf.cast(SCREAMING_SNAKE_CASE_					,  tf.floataa)
     lowercase__  :   List[str]    		=      tf.cast(self.warmup_steps					,  tf.floataa)
     lowercase__  :   Dict    		=      global_step_float / warmup_steps_float
     lowercase__  :   Any    		=      self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE_					,  self.power)
     return tf.cond(
         global_step_float < warmup_steps_float					,  lambda: warmup_learning_rate					,  lambda: self.decay_schedule_fn(step - self.warmup_steps)					,  name=SCREAMING_SNAKE_CASE_					,  )



 def      lowercase__     (					self):


   '''simple docstring'''




   return {
       "initial_learning_rate": self.initial_learning_rate,
       "decay_schedule_fn": self.decay_schedule_fn,
       "warmup_steps": self.warmup_steps,
       "power": self.power,
       "name": self.name,
   }

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ = 0.0 , lowercase_ = 0.9 , lowercase_ = 0.999 , lowercase_ = 1E-8 , lowercase_ = None , lowercase_ = None , lowercase_ = 0.0 , lowercase_ = 1.0 , lowercase_ = None , )		->	List[Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=lowercase_ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=lowercase_ , )
  if num_warmup_steps:
    lowercase__  :   str    		=      WarmUp(
        initial_learning_rate=lowercase_ , decay_schedule_fn=lowercase_ , warmup_steps=lowercase_ , )
  if weight_decay_rate > 0.0:
    lowercase__  :   int    		=      AdamWeightDecay(
        learning_rate=lowercase_ , weight_decay_rate=lowercase_ , beta_a=lowercase_ , beta_a=lowercase_ , epsilon=lowercase_ , clipnorm=lowercase_ , global_clipnorm=lowercase_ , exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""] , include_in_weight_decay=lowercase_ , )
  else:
    lowercase__  :   Dict    		=      tf.keras.optimizers.Adam(
        learning_rate=lowercase_ , beta_a=lowercase_ , beta_a=lowercase_ , epsilon=lowercase_ , clipnorm=lowercase_ , global_clipnorm=lowercase_ , )
  # We return the optimizer and the LR scheduler in order to better track the
  # evolution of the LR independently of the optimizer.
  return optimizer, lr_schedule



class       _snake_case   (	UpperCAmelCase_							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = 0.0_0_1					,  SCREAMING_SNAKE_CASE_ = 0.9					,  SCREAMING_SNAKE_CASE_ = 0.9_9_9					,  SCREAMING_SNAKE_CASE_ = 1E-7					,  SCREAMING_SNAKE_CASE_ = False					,  SCREAMING_SNAKE_CASE_ = 0.0					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = "AdamWeightDecay"					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
   lowercase__  :   Dict    		=      weight_decay_rate
   lowercase__  :   List[Any]    		=      include_in_weight_decay
   lowercase__  :   str    		=      exclude_from_weight_decay


 @classmethod
 def      lowercase__     (					cls					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      {"""WarmUp""": WarmUp}
   return super(SCREAMING_SNAKE_CASE_					,  cls).from_config(SCREAMING_SNAKE_CASE_					,  custom_objects=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super(SCREAMING_SNAKE_CASE_					,  self)._prepare_local(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      tf.constant(
       self.weight_decay_rate					,  name="""adam_weight_decay_rate""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      self._do_use_weight_decay(var.name)
   if do_decay:
     return var.assign_sub(
         learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""]					,  use_locking=self._use_locking					,  )
   return tf.no_op()


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   Union[str, Any]    		=      list(zip(*SCREAMING_SNAKE_CASE_))
   return super(SCREAMING_SNAKE_CASE_					,  self).apply_gradients(zip(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)					,  name=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if apply_state is None:
     return self._decayed_lr_t[var_dtype], {}

   lowercase__  :   Any    		=      apply_state or {}
   lowercase__  :   Union[str, Any]    		=      apply_state.get((var_device, var_dtype))
   if coefficients is None:
     lowercase__  :   Tuple    		=      self._fallback_apply_state(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     lowercase__  :   Dict    		=      coefficients

   return coefficients["lr_t"], {"apply_state": apply_state}


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   List[str]    		=      self._get_lr(var.device					,  var.dtype.base_dtype					,  SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      self._decay_weights_op(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   with tf.control_dependencies([decay]):
     return super(SCREAMING_SNAKE_CASE_					,  self)._resource_apply_dense(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   str    		=      self._get_lr(var.device					,  var.dtype.base_dtype					,  SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      self._decay_weights_op(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   with tf.control_dependencies([decay]):
     return super(SCREAMING_SNAKE_CASE_					,  self)._resource_apply_sparse(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      super().get_config()
   config.update({"""weight_decay_rate""": self.weight_decay_rate})
   return config



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.weight_decay_rate == 0:
     return False

   if self._include_in_weight_decay:
     for r in self._include_in_weight_decay:
       if re.search(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) is not None:
         return True

   if self._exclude_from_weight_decay:
     for r in self._exclude_from_weight_decay:
       if re.search(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) is not None:
         return False
   return True



class       _snake_case   (	UpperCAmelCase_							):


 def __init__(					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      []
   lowercase__  :   int    		=      None


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   if self._accum_steps is None:
     lowercase__  :   Tuple    		=      tf.Variable(
         tf.constant(0					,  dtype=tf.intaa)					,  trainable=SCREAMING_SNAKE_CASE_					,  synchronization=tf.VariableSynchronization.ON_READ					,  aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA					,  )

   return self._accum_steps.value()


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   if not self._gradients:
     raise ValueError("""The accumulator should be called first to initialize the gradients""")
   return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]


 def __call__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if not self._gradients:
     lowercase__  :   Union[str, Any]    		=      self.step  # Create the step variable.
     self._gradients.extend(
         [
             tf.Variable(
                 tf.zeros_like(SCREAMING_SNAKE_CASE_)					,  trainable=SCREAMING_SNAKE_CASE_					,  synchronization=tf.VariableSynchronization.ON_READ					,  aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA					,  )
             if gradient is not None
             else gradient
             for gradient in gradients
         ])
   if len(SCREAMING_SNAKE_CASE_) != len(self._gradients):
     raise ValueError(f'Expected {len(self._gradients)} gradients, but got {len(SCREAMING_SNAKE_CASE_)}')

   for accum_gradient, gradient in zip(self._gradients					,  SCREAMING_SNAKE_CASE_):
     if accum_gradient is not None and gradient is not None:
       accum_gradient.assign_add(SCREAMING_SNAKE_CASE_)

   self._accum_steps.assign_add(1)



 def      lowercase__     (					self):


   '''simple docstring'''




   if not self._gradients:
     return
   self._accum_steps.assign(0)
   for gradient in self._gradients:
     if gradient is not None:
       gradient.assign(tf.zeros_like(SCREAMING_SNAKE_CASE_))








# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			'dandelin/vilt-b32-finetuned-vqa'
 __lowerCAmelCase  :     Union[str, Any]        =			(
     'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
     'image containing the information, as well as a `question` which should be the question in English. It '
     'returns a text that is the answer to the question.'
 )
 __lowerCAmelCase  :     str        =			'image_qa'
 __lowerCAmelCase  :     int        =			AutoProcessor
 __lowerCAmelCase  :     Optional[Any]        =			AutoModelForVisualQuestionAnswering

 __lowerCAmelCase  :     str        =			['image', 'text']
 __lowerCAmelCase  :     Optional[int]        =			['text']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""vision"""])
   super().__init__(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.pre_processor(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  return_tensors="""pt""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   with torch.no_grad():
     return self.model(**SCREAMING_SNAKE_CASE_).logits



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      outputs.argmax(-1).item()
   return self.model.config.idalabel[idx]






