





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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							):
        super().__init__()
        self.register_modules(transformer=_UpperCamelCase				,							vae=_UpperCamelCase				,							scheduler=_UpperCamelCase					)

        # create a imagenet -> id dictionary for easier use
        _UpperCAmelCase           =   {}
        if idalabel is not None:
           for key, value in idalabel.items():
              for label in value.split(''','''					):
                 _UpperCAmelCase           =   int(_UpperCamelCase					)
           _UpperCAmelCase           =   dict(sorted(self.labels.items()					)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):

        if not isinstance(_UpperCamelCase				,							_UpperCamelCase					):
           _UpperCAmelCase           =   list(_UpperCamelCase					)

        for l in label:
           if l not in self.labels:
              raise ValueError(
                  f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''					)

        return [self.labels[l] for l in label]





     @torch.no_grad()
     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = 4.0				,							_UpperCamelCase = None				,							_UpperCamelCase = 50				,							_UpperCamelCase = "pil"				,							_UpperCamelCase = True				,							):
        _UpperCAmelCase           =   len(_UpperCamelCase					)
        _UpperCAmelCase           =   self.transformer.config.sample_size
        _UpperCAmelCase           =   self.transformer.config.in_channels

        _UpperCAmelCase           =   randn_tensor(
            shape=(batch_size, latent_channels, latent_size, latent_size)				,							generator=_UpperCamelCase				,							device=self.device				,							dtype=self.transformer.dtype				,							)
        _UpperCAmelCase           =   torch.cat([latents] * 2					) if guidance_scale > 1 else latents

        _UpperCAmelCase           =   torch.tensor(_UpperCamelCase				,							device=self.device					).reshape(-1					)
        _UpperCAmelCase           =   torch.tensor([1000] * batch_size				,							device=self.device					)
        _UpperCAmelCase           =   torch.cat([class_labels, class_null]				,							0					) if guidance_scale > 1 else class_labels

        # set step values
        self.scheduler.set_timesteps(_UpperCamelCase					)

        for t in self.progress_bar(self.scheduler.timesteps					):
           if guidance_scale > 1:
              _UpperCAmelCase           =   latent_model_input[: len(_UpperCamelCase					) // 2]
              _UpperCAmelCase           =   torch.cat([half, half]				,							dim=0					)
           _UpperCAmelCase           =   self.scheduler.scale_model_input(_UpperCamelCase				,							_UpperCamelCase					)

           _UpperCAmelCase           =   t
           if not torch.is_tensor(_UpperCamelCase					):
              # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
              # This would be a good case for the `match` statement (Python 3.10+)
              _UpperCAmelCase           =   latent_model_input.device.type == '''mps'''
              if isinstance(_UpperCamelCase				,							_UpperCamelCase					):
                 _UpperCAmelCase           =   torch.floataa if is_mps else torch.floataa
              else:
                 _UpperCAmelCase           =   torch.intaa if is_mps else torch.intaa
              _UpperCAmelCase           =   torch.tensor([timesteps]				,							dtype=_UpperCamelCase				,							device=latent_model_input.device					)
           elif len(timesteps.shape					) == 0:
              _UpperCAmelCase           =   timesteps[None].to(latent_model_input.device					)
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           _UpperCAmelCase           =   timesteps.expand(latent_model_input.shape[0]					)
           # predict noise model_output
           _UpperCAmelCase           =   self.transformer(
               _UpperCamelCase				,							timestep=_UpperCamelCase				,							class_labels=_UpperCamelCase					).sample

           # perform guidance
           if guidance_scale > 1:
              _UpperCAmelCase       ,      _UpperCAmelCase           =   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              _UpperCAmelCase       ,      _UpperCAmelCase           =   torch.split(_UpperCamelCase				,							len(_UpperCamelCase					) // 2				,							dim=0					)

              _UpperCAmelCase           =   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              _UpperCAmelCase           =   torch.cat([half_eps, half_eps]				,							dim=0					)

              _UpperCAmelCase           =   torch.cat([eps, rest]				,							dim=1					)

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
              _UpperCAmelCase       ,      _UpperCAmelCase           =   torch.split(_UpperCamelCase				,							_UpperCamelCase				,							dim=1					)
           else:
              _UpperCAmelCase           =   noise_pred

           # compute previous image: x_t -> x_t-1
           _UpperCAmelCase           =   self.scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					).prev_sample

        if guidance_scale > 1:
           _UpperCAmelCase       ,      _UpperCAmelCase           =   latent_model_input.chunk(2				,							dim=0					)
        else:
           _UpperCAmelCase           =   latent_model_input

        _UpperCAmelCase           =   1 / self.vae.config.scaling_factor * latents
        _UpperCAmelCase           =   self.vae.decode(_UpperCamelCase					).sample

        _UpperCAmelCase           =   (samples / 2 + 0.5).clamp(0				,							1					)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        _UpperCAmelCase           =   samples.cpu().permute(0				,							2				,							3				,							1					).float().numpy()

        if output_type == "pil":
           _UpperCAmelCase           =   self.numpy_to_pil(_UpperCamelCase					)

        if not return_dict:
           return (samples,)

        return ImagePipelineOutput(images=_UpperCamelCase					)






import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       LiltForQuestionAnswering,
       LiltForSequenceClassification,
       LiltForTokenClassification,
       LiltModel,
   )
   from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class 			__UpperCamelCase    :





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase=13				,							_UpperCamelCase=7				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=99				,							_UpperCamelCase=24				,							_UpperCamelCase=2				,							_UpperCamelCase=6				,							_UpperCamelCase=37				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=512				,							_UpperCamelCase=16				,							_UpperCamelCase=2				,							_UpperCamelCase=0.02				,							_UpperCamelCase=3				,							_UpperCamelCase=None				,							_UpperCamelCase=1000				,							):
        _UpperCAmelCase           =   parent
        _UpperCAmelCase           =   batch_size
        _UpperCAmelCase           =   seq_length
        _UpperCAmelCase           =   is_training
        _UpperCAmelCase           =   use_input_mask
        _UpperCAmelCase           =   use_token_type_ids
        _UpperCAmelCase           =   use_labels
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   type_vocab_size
        _UpperCAmelCase           =   type_sequence_label_size
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   num_labels
        _UpperCAmelCase           =   scope
        _UpperCAmelCase           =   range_bbox





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size					)

        _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length, 4]				,							self.range_bbox					)
        # Ensure that bbox is legal
        for i in range(bbox.shape[0]					):
           for j in range(bbox.shape[1]					):
              if bbox[i, j, 3] < bbox[i, j, 1]:
                 _UpperCAmelCase           =   bbox[i, j, 3]
                 _UpperCAmelCase           =   bbox[i, j, 1]
                 _UpperCAmelCase           =   t
              if bbox[i, j, 2] < bbox[i, j, 0]:
                 _UpperCAmelCase           =   bbox[i, j, 2]
                 _UpperCAmelCase           =   bbox[i, j, 0]
                 _UpperCAmelCase           =   t

        _UpperCAmelCase           =   None
        if self.use_input_mask:
           _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2					)

        _UpperCAmelCase           =   None
        if self.use_token_type_ids:
           _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size					)

        _UpperCAmelCase           =   None
        _UpperCAmelCase           =   None
        if self.use_labels:
           _UpperCAmelCase           =   ids_tensor([self.batch_size]				,							self.type_sequence_label_size					)
           _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels					)

        _UpperCAmelCase           =   self.get_config()

        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels





     def 		UpperCamelCase(     self					):
        return LiltConfig(
            vocab_size=self.vocab_size				,							hidden_size=self.hidden_size				,							num_hidden_layers=self.num_hidden_layers				,							num_attention_heads=self.num_attention_heads				,							intermediate_size=self.intermediate_size				,							hidden_act=self.hidden_act				,							hidden_dropout_prob=self.hidden_dropout_prob				,							attention_probs_dropout_prob=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							type_vocab_size=self.type_vocab_size				,							initializer_range=self.initializer_range				,							)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							):
        _UpperCAmelCase           =   LiltModel(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							bbox=_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase					)
        _UpperCAmelCase           =   model(_UpperCamelCase				,							bbox=_UpperCamelCase				,							token_type_ids=_UpperCamelCase					)
        _UpperCAmelCase           =   model(_UpperCamelCase				,							bbox=_UpperCamelCase					)

        self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)					)
        self.parent.assertEqual(result.pooler_output.shape				,							(self.batch_size, self.hidden_size)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.num_labels
        _UpperCAmelCase           =   LiltForTokenClassification(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							bbox=_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase				,							labels=_UpperCamelCase					)
        self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							):
        _UpperCAmelCase           =   LiltForQuestionAnswering(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							bbox=_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase				,							start_positions=_UpperCamelCase				,							end_positions=_UpperCamelCase				,							)
        self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length)					)
        self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length)					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      
        )           =   config_and_inputs
        _UpperCAmelCase           =   {
            '''input_ids''': input_ids,
            '''bbox''': bbox,
            '''token_type_ids''': token_type_ids,
            '''attention_mask''': input_mask,
        }
        return config, inputs_dict




@require_torch
class 			__UpperCamelCase    (							A__	, A__	, A__	, unittest.TestCase    ):
     __A	:  Dict							  =   (
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     __A	:  Optional[Any]							  =   (
         {
             """feature-extraction""": LiltModel,
             """question-answering""": LiltForQuestionAnswering,
             """text-classification""": LiltForSequenceClassification,
             """token-classification""": LiltForTokenClassification,
             """zero-shot""": LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __A	:  List[Any]							  =   False
     __A	:  Optional[int]							  =   False





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        return True





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   LiltModelTester(self					)
        _UpperCAmelCase           =   ConfigTester(self				,							config_class=_UpperCamelCase				,							hidden_size=37					)





     def 		UpperCamelCase(     self					):
        self.config_tester.run_common_tests()





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           _UpperCAmelCase           =   type
           self.model_tester.create_and_check_model(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase					)





     @slow
     def 		UpperCamelCase(     self					):
        for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase           =   LiltModel.from_pretrained(_UpperCamelCase					)
           self.assertIsNotNone(_UpperCamelCase					)




@require_torch
@slow
class 			__UpperCamelCase    (							unittest.TestCase    ):





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''					).to(_UpperCamelCase					)

        _UpperCAmelCase           =   torch.tensor([[1, 2]]				,							device=_UpperCamelCase					)
        _UpperCAmelCase           =   torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,							device=_UpperCamelCase					)

        # forward pass
        with torch.no_grad():
           _UpperCAmelCase           =   model(input_ids=_UpperCamelCase				,							bbox=_UpperCamelCase					)

        _UpperCAmelCase           =   torch.Size([1, 2, 768]					)
        _UpperCAmelCase           =   torch.tensor(
            [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]				,							device=_UpperCamelCase				,							)

        self.assertTrue(outputs.last_hidden_state.shape				,							_UpperCamelCase					)
        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,							_UpperCamelCase				,							atol=1e-3					)					)