


import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      # TODO: is there an appropriate internal test set?
      snake_case__				:     Any        =						'''ssube/stable-diffusion-x4-upscaler-onnx'''
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int=0     )		->			Tuple:
             a_					:		Union[str, Any]							=  floats_tensor((1, 3, 1_2_8, 1_2_8)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     )
             a_					:		Dict							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  {
                 'prompt': 'A painting of a squirrel eating a burger',
                 'image': image,
                 'generator': generator,
                 'num_inference_steps': 3,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  self.get_dummy_inputs()
             a_					:		int							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		Tuple							=  image[0, -3:, -3:, -1].flatten()

             # started as 128, should now be 512
             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		List[Any]							=  np.array(
                 [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
             assert np.abs(image_slice - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Dict:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  PNDMScheduler.from_config(pipe.scheduler.config     ,							skip_prk_steps=SCREAMING_SNAKE_CASE__     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		List[str]							=  self.get_dummy_inputs()
             a_					:		List[str]							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		Optional[Any]							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		str							=  np.array(
                 [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]     )
             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Dict:
             a_					:		Optional[Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[Any]							=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  self.get_dummy_inputs()
             a_					:		Dict							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		str							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Optional[Any]							=  np.array(
                 [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			int:
             a_					:		List[str]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[Any]							=  self.get_dummy_inputs()
             a_					:		Dict							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		List[str]							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		int							=  np.array(
                 [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1

      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[int]							=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  self.get_dummy_inputs()
             a_					:		List[str]							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		List[str]							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Union[str, Any]							=  np.array(
                 [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Union[str, Any]:
             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[str]:
             a_					:		List[str]							=  ort.SessionOptions()
             a_					:		int							=  False
             return options
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Tuple:
             a_					:		str							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		int							=  init_image.resize((1_2_8, 1_2_8)     )
             # using the PNDM scheduler by default
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  'A fantasy landscape, trending on artstation'

             a_					:		str							=  torch.manual_seed(0     )
             a_					:		List[str]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=1_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		Dict							=  output.images
             a_					:		Any							=  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		str							=  np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]     )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2E-2

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             a_					:		Dict							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		List[str]							=  init_image.resize((1_2_8, 1_2_8)     )
             a_					:		Dict							=  LMSDiscreteScheduler.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							subfolder='scheduler'     )
             a_					:		Union[str, Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							scheduler=SCREAMING_SNAKE_CASE__     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  'A fantasy landscape, trending on artstation'

             a_					:		Tuple							=  torch.manual_seed(0     )
             a_					:		Optional[Any]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=2_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		str							=  output.images
             a_					:		List[Any]							=  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Tuple							=  np.array(
                 [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]     )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2E-2









import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


a__:      int									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None,__lowerCamelCase=None ):
        A__											=						self.layer[current_layer](__lowerCamelCase,__lowerCamelCase,head_mask[current_layer] )

        A__											=						layer_outputs[0]

        return hidden_states




@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''				,					UpperCamelCase__				,					)
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )

        A__											=						BertEncoderWithPabee(__lowerCamelCase )

        self.init_weights()
        A__											=						0
        A__											=						0
        A__											=						0

        A__											=						0



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						threshold



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						patience



 def 				UpperCamelCase			(							self ):
        A__											=						0
        A__											=						0



 def 				UpperCamelCase			(							self ):
        A__											=						self.inference_layers_num / self.inference_instances_num
        A__											=						(
            f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="
            f" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
        )
        print(__lowerCamelCase )




 @add_start_docstrings_to_model_forward(__lowerCamelCase )
 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=False,):

        if input_ids is not None and inputs_embeds is not None:
               raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
        elif input_ids is not None:
               A__											=						input_ids.size()
        elif inputs_embeds is not None:
               A__											=						inputs_embeds.size()[:-1]
        else:
               raise ValueError('''You have to specify either input_ids or inputs_embeds''' )

        A__											=						input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
               A__											=						torch.ones(__lowerCamelCase,device=__lowerCamelCase )
        if token_type_ids is None:
               A__											=						torch.zeros(__lowerCamelCase,dtype=torch.long,device=__lowerCamelCase )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        A__											=						self.get_extended_attention_mask(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
               A__					,	A__					,	A__											=						encoder_hidden_states.size()
               A__											=						(encoder_batch_size, encoder_sequence_length)
               if encoder_attention_mask is None:
                      A__											=						torch.ones(__lowerCamelCase,device=__lowerCamelCase )
               A__											=						self.invert_attention_mask(__lowerCamelCase )
        else:
               A__											=						None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        A__											=						self.get_head_mask(__lowerCamelCase,self.config.num_hidden_layers )

        A__											=						self.embeddings(
            input_ids=__lowerCamelCase,position_ids=__lowerCamelCase,token_type_ids=__lowerCamelCase,inputs_embeds=__lowerCamelCase )
        A__											=						embedding_output

        if self.training:
               A__											=						[]
               for i in range(self.config.num_hidden_layers ):
                      A__											=						self.encoder.adaptive_forward(
                          __lowerCamelCase,current_layer=__lowerCamelCase,attention_mask=__lowerCamelCase,head_mask=__lowerCamelCase )

                      A__											=						self.pooler(__lowerCamelCase )
                      A__											=						output_layers[i](output_dropout(__lowerCamelCase ) )
                      res.append(__lowerCamelCase )
        elif self.patience == 0:  # Use all layers for inference
               A__											=						self.encoder(
                   __lowerCamelCase,attention_mask=__lowerCamelCase,head_mask=__lowerCamelCase,encoder_hidden_states=__lowerCamelCase,encoder_attention_mask=__lowerCamelCase,)
               A__											=						self.pooler(encoder_outputs[0] )
               A__											=						[output_layers[self.config.num_hidden_layers - 1](__lowerCamelCase )]
        else:
               A__											=						0
               A__											=						None
               A__											=						0
               for i in range(self.config.num_hidden_layers ):
                      calculated_layer_num += 1
                      A__											=						self.encoder.adaptive_forward(
                          __lowerCamelCase,current_layer=__lowerCamelCase,attention_mask=__lowerCamelCase,head_mask=__lowerCamelCase )

                      A__											=						self.pooler(__lowerCamelCase )
                      A__											=						output_layers[i](__lowerCamelCase )
                      if regression:
                             A__											=						logits.detach()
                             if patient_result is not None:
                                    A__											=						patient_result.detach()
                             if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
                                    patient_counter += 1
                             else:
                                    A__											=						0
                      else:
                             A__											=						logits.detach().argmax(dim=1 )
                             if patient_result is not None:
                                    A__											=						patient_result.detach().argmax(dim=1 )
                             if (patient_result is not None) and torch.all(labels.eq(__lowerCamelCase ) ):
                                    patient_counter += 1
                             else:
                                    A__											=						0

                      A__											=						logits
                      if patient_counter == self.patience:
                             break
               A__											=						[patient_result]
               self.inference_layers_num += calculated_layer_num
               self.inference_instances_num += 1

        return res




@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''				,					UpperCamelCase__				,					)
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )
        A__											=						config.num_labels

        A__											=						BertModelWithPabee(__lowerCamelCase )
        A__											=						nn.Dropout(config.hidden_dropout_prob )
        A__											=						nn.ModuleList(
            [nn.Linear(config.hidden_size,self.config.num_labels ) for _ in range(config.num_hidden_layers )] )

        self.init_weights()




 @add_start_docstrings_to_model_forward(__lowerCamelCase )
 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,):
        A__											=						self.bert(
            input_ids=__lowerCamelCase,attention_mask=__lowerCamelCase,token_type_ids=__lowerCamelCase,position_ids=__lowerCamelCase,head_mask=__lowerCamelCase,inputs_embeds=__lowerCamelCase,output_dropout=self.dropout,output_layers=self.classifiers,regression=self.num_labels == 1,)

        A__											=						(logits[-1],)

        if labels is not None:
               A__											=						None
               A__											=						0
               for ix, logits_item in enumerate(__lowerCamelCase ):
                      if self.num_labels == 1:
                             #  We are doing regression
                             A__											=						MSELoss()
                             A__											=						loss_fct(logits_item.view(-1 ),labels.view(-1 ) )
                      else:
                             A__											=						CrossEntropyLoss()
                             A__											=						loss_fct(logits_item.view(-1,self.num_labels ),labels.view(-1 ) )
                      if total_loss is None:
                             A__											=						loss
                      else:
                             total_loss += loss * (ix + 1)
                      total_weights += ix + 1
               A__											=						(total_loss / total_weights,) + outputs

        return outputs





