
import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
  from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=3		,snake_case=32		,snake_case=3		,snake_case=10		,snake_case=[8, 16, 32, 64]		,snake_case=[1, 1, 2, 1]		,snake_case=True		,snake_case=True		,snake_case="relu"		,snake_case=3		,snake_case=None		,snake_case=["stage2", "stage3", "stage4"]		,snake_case=[2, 3, 4]		,snake_case=1		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      parent
           lowercase							:     List[Any]       	=      batch_size
           lowercase							:     Optional[Any]       	=      image_size
           lowercase							:     str       	=      num_channels
           lowercase							:     Union[str, Any]       	=      embeddings_size
           lowercase							:     Any       	=      hidden_sizes
           lowercase							:     Union[str, Any]       	=      depths
           lowercase							:     int       	=      is_training
           lowercase							:     List[Any]       	=      use_labels
           lowercase							:     List[str]       	=      hidden_act
           lowercase							:     Tuple       	=      num_labels
           lowercase							:     Optional[Any]       	=      scope
           lowercase							:     str       	=      len(snake_case      )
           lowercase							:     int       	=      out_features
           lowercase							:     Any       	=      out_indices
           lowercase							:     Any       	=      num_groups
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           lowercase							:     int       	=      None
           if self.use_labels:
                  lowercase							:     Dict       	=      ids_tensor([self.batch_size]		,self.num_labels      )

           lowercase							:     Tuple       	=      self.get_config()

           return config, pixel_values, labels
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return BitConfig(
               num_channels=self.num_channels		,embeddings_size=self.embeddings_size		,hidden_sizes=self.hidden_sizes		,depths=self.depths		,hidden_act=self.hidden_act		,num_labels=self.num_labels		,out_features=self.out_features		,out_indices=self.out_indices		,num_groups=self.num_groups		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      BitModel(config=snake_case      )
           model.to(snake_case      )
           model.eval()
           lowercase							:     List[Any]       	=      model(snake_case      )
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.num_labels
           lowercase							:     List[str]       	=      BitForImageClassification(snake_case      )
           model.to(snake_case      )
           model.eval()
           lowercase							:     List[str]       	=      model(snake_case		,labels=snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      BitBackbone(config=snake_case      )
           model.to(snake_case      )
           model.eval()
           lowercase							:     List[str]       	=      model(snake_case      )

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps      )		,len(config.out_features      )      )
           self.parent.assertListEqual(list(result.feature_maps[0].shape      )		,[self.batch_size, self.hidden_sizes[1], 4, 4]      )

           # verify channels
           self.parent.assertEqual(len(model.channels      )		,len(config.out_features      )      )
           self.parent.assertListEqual(model.channels		,config.hidden_sizes[1:]      )

           # verify backbone works with out_features=None
           lowercase							:     Optional[Any]       	=      None
           lowercase							:     List[str]       	=      BitBackbone(config=snake_case      )
           model.to(snake_case      )
           model.eval()
           lowercase							:     Any       	=      model(snake_case      )

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps      )		,1      )
           self.parent.assertListEqual(list(result.feature_maps[0].shape      )		,[self.batch_size, self.hidden_sizes[-1], 1, 1]      )

           # verify channels
           self.parent.assertEqual(len(model.channels      )		,1      )
           self.parent.assertListEqual(model.channels		,[config.hidden_sizes[-1]]      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.prepare_config_and_inputs()
           lowercase	,			lowercase	,			lowercase							:     str       	=      config_and_inputs
           lowercase							:     Tuple       	=      {"""pixel_values""": pixel_values}
           return config, inputs_dict






@require_torch
class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Optional[int]=				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    _a		:						Any=				(
        {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
        if is_torch_available()
        else {}
    )

    _a		:						List[Any]=				False
    _a		:						List[Any]=				False
    _a		:						Optional[int]=				False
    _a		:						Tuple=				False
    _a		:						Optional[Any]=				False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      BitModelTester(self      )
           lowercase							:     Tuple       	=      ConfigTester(self		,config_class=snake_case		,has_text_modality=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return
    @unittest.skip(reason="""Bit does not output attentions"""      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""Bit does not use inputs_embeds"""      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""Bit does not support input and output embeddings"""      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Tuple       	=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:     List[str]       	=      model_class(snake_case      )
                  lowercase							:     Any       	=      inspect.signature(model.forward      )
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowercase							:     int       	=      [*signature.parameters.keys()]

                  lowercase							:     List[Any]       	=      ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Any       	=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:     int       	=      model_class(config=snake_case      )
                  for name, module in model.named_modules():
                         if isinstance(snake_case		,(nn.BatchNormad, nn.GroupNorm)      ):
                                self.assertTrue(
                                    torch.all(module.weight == 1      )		,msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,)
                                self.assertTrue(
                                    torch.all(module.bias == 0      )		,msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           def check_hidden_states_output(snake_case		,snake_case		,snake_case      ):
                  lowercase							:     Any       	=      model_class(snake_case      )
                  model.to(snake_case      )
                  model.eval()

                  with torch.no_grad():
                         lowercase							:     str       	=      model(**self._prepare_for_class(snake_case		,snake_case      )      )

                  lowercase							:     Optional[int]       	=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  lowercase							:     Optional[int]       	=      self.model_tester.num_stages
                  self.assertEqual(len(snake_case      )		,expected_num_stages + 1      )

                  # Bit's feature maps are of shape (batch_size, num_channels, height, width)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:]      )		,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,)

           lowercase	,			lowercase							:     int       	=      self.model_tester.prepare_config_and_inputs_for_common()
           lowercase							:     Tuple       	=      ["""preactivation""", """bottleneck"""]
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         lowercase							:     Dict       	=      layer_type
                         lowercase							:     int       	=      True
                         check_hidden_states_output(snake_case		,snake_case		,snake_case      )

                         # check that output_hidden_states also work using config
                         del inputs_dict["output_hidden_states"]
                         lowercase							:     Union[str, Any]       	=      True

                         check_hidden_states_output(snake_case		,snake_case		,snake_case      )
    @unittest.skip(reason="""Bit does not use feedforward chunking"""      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:     Optional[Any]       	=      BitModel.from_pretrained(snake_case      )
                  self.assertIsNotNone(snake_case      )







def 						_snake_case(  ) ->    Optional[int]:
       lowercase							:     str       	=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
       return image

@require_torch
@require_vision
class __snake_case		(      unittest.TestCase     ):
    @cached_property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return (
               BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ) if is_vision_available() else None
           )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(snake_case      )

           lowercase							:     Union[str, Any]       	=      self.default_image_processor
           lowercase							:     int       	=      prepare_img()
           lowercase							:     str       	=      image_processor(images=snake_case		,return_tensors="""pt"""      ).to(snake_case      )

           # forward pass
           with torch.no_grad():
                  lowercase							:     Optional[int]       	=      model(**snake_case      )

           # verify the logits
           lowercase							:     int       	=      torch.Size((1, 1000)      )
           self.assertEqual(outputs.logits.shape		,snake_case      )

           lowercase							:     Optional[Any]       	=      torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]      ).to(snake_case      )

           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,snake_case		,atol=1e-4      )      )






@require_torch
class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Tuple=				(BitBackbone,) if is_torch_available() else ()
    _a		:						str=				BitConfig

    _a		:						str=				False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      BitModelTester(self      )

"""simple docstring"""






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Tuple							  =	1

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 1000		,       __lowerCAmelCase = None						):







            """simple docstring"""



            self.set_timesteps(__lowerCAmelCase						)

            # standard deviation of the initial noise distribution
            lowercase														=	1.0

            # For now we only support F-PNDM, i.e. the runge-kutta method
            # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
            # mainly at formula (9), (12), (13) and the Algorithm 2.
            lowercase														=	4

            # running values
            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	torch.linspace(1		,       0		,       num_inference_steps + 1						)[:-1]
            lowercase														=	torch.cat([steps, torch.tensor([0.0]						)]						)

            if self.config.trained_betas is not None:
                  lowercase														=	torch.tensor(self.config.trained_betas		,       dtype=torch.floataa						)
            else:
                  lowercase														=	torch.sin(steps * math.pi / 2						) ** 2

            lowercase														=	(1.0 - self.betas**2) ** 0.5

            lowercase														=	(torch.atana(self.betas		,       self.alphas						) / math.pi * 2)[:-1]
            lowercase														=	timesteps.to(__lowerCAmelCase						)

            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       ):







            """simple docstring"""



            if self.num_inference_steps is None:
                  raise ValueError(
                      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

            lowercase														=	(self.timesteps == timestep).nonzero().item()
            lowercase														=	timestep_index + 1

            lowercase														=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
            self.ets.append(__lowerCAmelCase						)

            if len(self.ets						) == 1:
                  lowercase														=	self.ets[-1]
            elif len(self.ets						) == 2:
                  lowercase														=	(3 * self.ets[-1] - self.ets[-2]) / 2
            elif len(self.ets						) == 3:
                  lowercase														=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
            else:
                  lowercase														=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

            lowercase														=	self._get_prev_sample(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.alphas[timestep_index]
            lowercase														=	self.betas[timestep_index]

            lowercase														=	self.alphas[prev_timestep_index]
            lowercase														=	self.betas[prev_timestep_index]

            lowercase														=	(sample - sigma * ets) / max(__lowerCAmelCase		,       1E-8						)
            lowercase														=	next_alpha * pred + ets * next_sigma

            return prev_sample







      def __len__(						self						):







            """simple docstring"""



            return self.config.num_train_timesteps



