






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_						:



      """simple docstring"""
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=13    ,				SCREAMING_SNAKE_CASE_=7    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=99    ,				SCREAMING_SNAKE_CASE_=24    ,				SCREAMING_SNAKE_CASE_=2    ,				SCREAMING_SNAKE_CASE_=6    ,				SCREAMING_SNAKE_CASE_=37    ,				SCREAMING_SNAKE_CASE_="gelu"    ,				SCREAMING_SNAKE_CASE_=0.1    ,				SCREAMING_SNAKE_CASE_=0.1    ,				SCREAMING_SNAKE_CASE_=512    ,				SCREAMING_SNAKE_CASE_=16    ,				SCREAMING_SNAKE_CASE_=2    ,				SCREAMING_SNAKE_CASE_=0.02    ,				SCREAMING_SNAKE_CASE_=3    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=1000    ,				)				->      Tuple:
          UpperCamelCase       :int								=   parent
          UpperCamelCase       :Optional[int]								=   batch_size
          UpperCamelCase       :Dict								=   seq_length
          UpperCamelCase       :Dict								=   is_training
          UpperCamelCase       :List[str]								=   use_input_mask
          UpperCamelCase       :Optional[int]								=   use_token_type_ids
          UpperCamelCase       :str								=   use_labels
          UpperCamelCase       :List[Any]								=   vocab_size
          UpperCamelCase       :Any								=   hidden_size
          UpperCamelCase       :Union[str, Any]								=   num_hidden_layers
          UpperCamelCase       :Optional[int]								=   num_attention_heads
          UpperCamelCase       :int								=   intermediate_size
          UpperCamelCase       :Any								=   hidden_act
          UpperCamelCase       :Any								=   hidden_dropout_prob
          UpperCamelCase       :int								=   attention_probs_dropout_prob
          UpperCamelCase       :Union[str, Any]								=   max_position_embeddings
          UpperCamelCase       :List[Any]								=   type_vocab_size
          UpperCamelCase       :int								=   type_sequence_label_size
          UpperCamelCase       :List[str]								=   initializer_range
          UpperCamelCase       :List[str]								=   num_labels
          UpperCamelCase       :int								=   scope
          UpperCamelCase       :Dict								=   range_bbox
      def     UpperCAmelCase						(				self )				->      Any:
          UpperCamelCase       :List[Any]								=   ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size )

          UpperCamelCase       :Tuple								=   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       :Tuple								=   bbox[i, j, 3]
                      UpperCamelCase       :str								=   bbox[i, j, 1]
                      UpperCamelCase       :List[Any]								=   t
                  if bbox[i, j, 2] < bbox[i, j, 0]:
                      UpperCamelCase       :Optional[Any]								=   bbox[i, j, 2]
                      UpperCamelCase       :Union[str, Any]								=   bbox[i, j, 0]
                      UpperCamelCase       :str								=   t

          UpperCamelCase       :List[Any]								=   None
          if self.use_input_mask:
              UpperCamelCase       :Optional[Any]								=   ids_tensor([self.batch_size, self.seq_length]    ,				vocab_size=2 )

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

          UpperCamelCase       :Any								=   None
          UpperCamelCase       :Optional[int]								=   None
          if self.use_labels:
              UpperCamelCase       :str								=   ids_tensor([self.batch_size]    ,				self.type_sequence_label_size )
              UpperCamelCase       :str								=   ids_tensor([self.batch_size, self.seq_length]    ,				self.num_labels )

          UpperCamelCase       :Tuple								=   self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          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    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				)				->      Dict:
          UpperCamelCase       :Optional[Any]								=   LiltModel(config=A_ )
          model.to(A_ )
          model.eval()
          UpperCamelCase       :Union[str, Any]								=   model(A_    ,				bbox=A_    ,				attention_mask=A_    ,				token_type_ids=A_ )
          UpperCamelCase       :Tuple								=   model(A_    ,				bbox=A_    ,				token_type_ids=A_ )
          UpperCamelCase       :Optional[int]								=   model(A_    ,				bbox=A_ )

          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    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				)				->      int:
          UpperCamelCase       :str								=   self.num_labels
          UpperCamelCase       :Dict								=   LiltForTokenClassification(config=A_ )
          model.to(A_ )
          model.eval()
          UpperCamelCase       :int								=   model(
              A_    ,				bbox=A_    ,				attention_mask=A_    ,				token_type_ids=A_    ,				labels=A_ )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.num_labels) )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				)				->      Union[str, Any]:
          UpperCamelCase       :Dict								=   LiltForQuestionAnswering(config=A_ )
          model.to(A_ )
          model.eval()
          UpperCamelCase       :Optional[Any]								=   model(
              A_    ,				bbox=A_    ,				attention_mask=A_    ,				token_type_ids=A_    ,				start_positions=A_    ,				end_positions=A_    ,				)
          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 )				->      Optional[Any]:
          UpperCamelCase       :Tuple								=   self.prepare_config_and_inputs()
          (
              UpperCamelCase  
          )       :List[Any]								=   config_and_inputs
          UpperCamelCase       :Union[str, Any]								=   {
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict







@require_torch
class 			UpperCAmelCase_						(					UpperCamelCase_,	UpperCamelCase_,	UpperCamelCase_,	unittest.TestCase ):



      """simple docstring"""
      UpperCamelCase_				:					Union[str, Any]           =(
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase_				:					Optional[int]           =(
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCamelCase_				:					Optional[Any]           =False
      UpperCamelCase_				:					Optional[Any]           =False
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          return True
      def     UpperCAmelCase						(				self )				->      Tuple:
          UpperCamelCase       :Dict								=   LiltModelTester(self )
          UpperCamelCase       :Optional[int]								=   ConfigTester(self    ,				config_class=A_    ,				hidden_size=37 )
      def     UpperCAmelCase						(				self )				->      List[str]:
          self.config_tester.run_common_tests()
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :Optional[int]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A_ )
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          UpperCamelCase       :Any								=   self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCamelCase       :int								=   type
              self.model_tester.create_and_check_model(*A_ )
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :Tuple								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A_ )
      def     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :List[str]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A_ )




      @slow
      def     UpperCAmelCase						(				self )				->      str:
          for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       :Union[str, Any]								=   LiltModel.from_pretrained(A_ )
              self.assertIsNotNone(A_ )







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



      """simple docstring"""
      def     UpperCAmelCase						(				self )				->      Any:
          UpperCamelCase       :Optional[int]								=   LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''' ).to(A_ )

          UpperCamelCase       :Optional[int]								=   torch.tensor([[1, 2]]    ,				device=A_ )
          UpperCamelCase       :Any								=   torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]    ,				device=A_ )

          # forward pass
          with torch.no_grad():
              UpperCamelCase       :Dict								=   model(input_ids=A_    ,				bbox=A_ )

          UpperCamelCase       :int								=   torch.Size([1, 2, 768] )
          UpperCamelCase       :Any								=   torch.tensor(
              [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]    ,				device=A_    ,				)

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










import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard



@slow
@require_flax
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Tuple):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png''')
									lowerCAmelCase_			:     str									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

									lowerCAmelCase_			:     List[Any]									= '''xvjiarui/stable-diffusion-2-inpainting'''
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= FlaxStableDiffusionInpaintPipeline.from_pretrained(A_				,				safety_checker=A_)

									lowerCAmelCase_			:     List[str]									= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowerCAmelCase_			:     List[Any]									= jax.random.PRNGKey(0)
									lowerCAmelCase_			:     str									= 5_0

									lowerCAmelCase_			:     List[Any]									= jax.device_count()
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [prompt]
									lowerCAmelCase_			:     str									= num_samples * [init_image]
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [mask_image]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pipeline.prepare_inputs(A_				,				A_				,				A_)

									# shard inputs and rng
									lowerCAmelCase_			:     str									= replicate(A_)
									lowerCAmelCase_			:     str									= jax.random.split(A_				,				jax.device_count())
									lowerCAmelCase_			:     List[Any]									= shard(A_)
									lowerCAmelCase_			:     str									= shard(A_)
									lowerCAmelCase_			:     Tuple									= shard(A_)

									lowerCAmelCase_			:     int									= pipeline(
									    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				jit=A_)

									lowerCAmelCase_			:     Optional[int]									= output.images.reshape(A_				,				5_1_2				,				5_1_2				,				3)

									lowerCAmelCase_			:     List[str]									= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									lowerCAmelCase_			:     int									= jnp.asarray(jax.device_get(image_slice.flatten()))
									lowerCAmelCase_			:     str									= jnp.array(
									    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084])
									print(F"""output_slice: {output_slice}""")
									assert jnp.abs(output_slice - expected_slice).max() < 1e-2





