"""simple docstring"""


import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__UpperCamelCase									=			{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 13_1072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
}







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      str:
     return torch.atana(UpperCAmelCase						,					UpperCAmelCase					) / math.pi * 2







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Union[str, Any]:
     snake_case_					       =					torch.sin(t * math.pi / 2					) ** 2
     snake_case_					       =					(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(UpperCAmelCase						,					UpperCAmelCase					)




class  UpperCamelCase					(				lowerCAmelCase__   ):
       pass




class  UpperCamelCase					(				nn.Module   ):







       def __init__( self,							lowerCAmelCase__)      ->      Tuple:
            super().__init__()

            snake_case_					       =					DiffusionAttnUnetaD(lowerCAmelCase__,							n_attn_layers=4)
            snake_case_					       =					deepcopy(self.diffusion)
            snake_case_					       =					torch.quasirandom.SobolEngine(1,							scramble=lowerCAmelCase__)







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Dict:
     snake_case_					       =					MODELS_MAP[model_name]['url']
     os.system(f'wget {url} ./'					)

     return f'./{model_name}.ckpt'


__UpperCamelCase									=			{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
__UpperCamelCase									=			{
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__UpperCamelCase									=			{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
__UpperCamelCase									=			{
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

__UpperCamelCase									=			{
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

__UpperCamelCase									=			{
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Dict:
     if name.startswith('skip'					):
          return name.replace('skip'						,					RES_CONV_MAP['skip']					)

     # name has to be of format main.{digit}
     if not name.startswith('main.'					):
          raise ValueError(f'ResConvBlock error with {name}'					)

     return name.replace(name[:6]						,					RES_CONV_MAP[name[:6]]					)







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      str:
     for key, value in ATTN_MAP.items():
          if name.startswith(UpperCAmelCase					) and not isinstance(UpperCAmelCase						,					UpperCAmelCase					):
               return name.replace(UpperCAmelCase						,					UpperCAmelCase					)
          elif name.startswith(UpperCAmelCase					):
               return [name.replace(UpperCAmelCase						,					UpperCAmelCase					) for v in value]
     raise ValueError(f'Attn error with {name}'					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=13					)				->      Optional[int]:
     snake_case_					       =					input_string

     if string.split('.'					)[0] == "timestep_embed":
          return string.replace('timestep_embed'						,					'time_proj'					)

     snake_case_					       =					0
     if string.startswith('net.3.'					):
          depth += 1
          snake_case_					       =					string[6:]
     elif string.startswith('net.'					):
          snake_case_					       =					string[4:]

     while string.startswith('main.7.'					):
          depth += 1
          snake_case_					       =					string[7:]

     if string.startswith('main.'					):
          snake_case_					       =					string[5:]

     # mid block
     if string[:2].isdigit():
          snake_case_					       =					string[:2]
          snake_case_					       =					string[2:]
     else:
          snake_case_					       =					string[0]
          snake_case_					       =					string[1:]

     if depth == max_depth:
          snake_case_					       =					MID_NUM_TO_LAYER[layer_num]
          snake_case_					       =					'mid_block'
     elif depth > 0 and int(UpperCAmelCase					) < 7:
          snake_case_					       =					DOWN_NUM_TO_LAYER[layer_num]
          snake_case_					       =					f'down_blocks.{depth}'
     elif depth > 0 and int(UpperCAmelCase					) > 7:
          snake_case_					       =					UP_NUM_TO_LAYER[layer_num]
          snake_case_					       =					f'up_blocks.{max_depth - depth - 1}'
     elif depth == 0:
          snake_case_					       =					DEPTH_0_TO_LAYER[layer_num]
          snake_case_					       =					f'up_blocks.{max_depth - 1}' if int(UpperCAmelCase					) > 3 else 'down_blocks.0'

     if not string_left.startswith('.'					):
          raise ValueError(f'Naming error with {input_string} and string_left: {string_left}.'					)

     snake_case_					       =					string_left[1:]

     if "resnets" in new_layer:
          snake_case_					       =					convert_resconv_naming(UpperCAmelCase					)
     elif "attentions" in new_layer:
          snake_case_					       =					convert_attn_naming(UpperCAmelCase					)
          snake_case_					       =					new_string_left

     if not isinstance(UpperCAmelCase						,					UpperCAmelCase					):
          snake_case_					       =					prefix + '.' + new_layer + '.' + string_left
     else:
          snake_case_					       =					[prefix + '.' + new_layer + '.' + s for s in string_left]
     return new_string







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      int:
     snake_case_					       =					{}
     for k, v in state_dict.items():
          if k.endswith('kernel'					):
               # up- and downsample layers, don't have trainable weights
               continue

          snake_case_					       =					rename(UpperCAmelCase					)

          # check if we need to transform from Conv => Linear for attention
          if isinstance(UpperCAmelCase						,					UpperCAmelCase					):
               snake_case_					       =					transform_conv_attns(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
          else:
               snake_case_					       =					v

     return new_state_dict







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      Optional[int]:
     if len(UpperCAmelCase					) == 1:
          if len(v.shape					) == 3:
               # weight
               snake_case_					       =					v[:, :, 0]
          else:
               # bias
               snake_case_					       =					v
     else:
          # qkv matrices
          snake_case_					       =					v.shape[0]
          snake_case_					       =					trippled_shape // 3
          for i in range(3					):
               if len(v.shape					) == 3:
                    snake_case_					       =					v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    snake_case_					       =					v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Union[str, Any]:
     snake_case_					       =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)

     snake_case_					       =					args.model_path.split('/'					)[-1].split('.'					)[0]
     if not os.path.isfile(args.model_path					):
          assert (
              model_name == args.model_path
          ), f'Make sure to provide one of the official model names {MODELS_MAP.keys()}'
          snake_case_					       =					download(UpperCAmelCase					)

     snake_case_					       =					MODELS_MAP[model_name]['sample_rate']
     snake_case_					       =					MODELS_MAP[model_name]['sample_size']

     snake_case_					       =					Object()
     snake_case_					       =					sample_size
     snake_case_					       =					sample_rate
     snake_case_					       =					0

     snake_case_					       =					UNetaDModel(sample_size=UpperCAmelCase						,					sample_rate=UpperCAmelCase					)
     snake_case_					       =					diffusers_model.state_dict()

     snake_case_					       =					DiffusionUncond(UpperCAmelCase					)
     orig_model.load_state_dict(torch.load(args.model_path						,					map_location=UpperCAmelCase					)['state_dict']					)
     snake_case_					       =					orig_model.diffusion_ema.eval()
     snake_case_					       =					orig_model.state_dict()
     snake_case_					       =					rename_orig_weights(UpperCAmelCase					)

     snake_case_					       =					set(renamed_state_dict.keys()					) - set(diffusers_state_dict.keys()					)
     snake_case_					       =					set(diffusers_state_dict.keys()					) - set(renamed_state_dict.keys()					)

     assert len(UpperCAmelCase					) == 0, f'Problem with {renamed_minus_diffusers}'
     assert all(k.endswith('kernel'					) for k in list(UpperCAmelCase					)					), f'Problem with {diffusers_minus_renamed}'

     for key, value in renamed_state_dict.items():
          assert (
              diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
          ), f'Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'
          if key == "time_proj.weight":
               snake_case_					       =					value.squeeze()

          snake_case_					       =					value

     diffusers_model.load_state_dict(UpperCAmelCase					)

     snake_case_					       =					100
     snake_case_					       =					33

     snake_case_					       =					IPNDMScheduler(num_train_timesteps=UpperCAmelCase					)

     snake_case_					       =					torch.manual_seed(UpperCAmelCase					)
     snake_case_					       =					torch.randn([1, 2, config.sample_size]						,					generator=UpperCAmelCase					).to(UpperCAmelCase					)

     snake_case_					       =					torch.linspace(1						,					0						,					steps + 1						,					device=UpperCAmelCase					)[:-1]
     snake_case_					       =					get_crash_schedule(UpperCAmelCase					)

     snake_case_					       =					DanceDiffusionPipeline(unet=UpperCAmelCase						,					scheduler=UpperCAmelCase					)

     snake_case_					       =					torch.manual_seed(33					)
     snake_case_					       =					pipe(num_inference_steps=UpperCAmelCase						,					generator=UpperCAmelCase					).audios

     snake_case_					       =					sampling.iplms_sample(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					{}					)
     snake_case_					       =					generated.clamp(-1						,					1					)

     snake_case_					       =					(generated - audio).abs().sum()
     snake_case_					       =					(generated - audio).abs().max()

     if args.save:
          pipe.save_pretrained(args.checkpoint_path					)

     print('Diff sum'						,					UpperCAmelCase					)
     print('Diff max'						,					UpperCAmelCase					)

     assert diff_max < 1e-3, f'Diff max: {diff_max} is too much :-/'

     print(f'Conversion for {model_name} successful!'					)


if __name__ == "__main__":
    __UpperCamelCase									=			argparse.ArgumentParser()

    parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
    parser.add_argument(
        '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
    )
    parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
    __UpperCamelCase									=			parser.parse_args()

    main(args)





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  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       str=1_3     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=7     ,							SCREAMING_SNAKE_CASE__				:       str=True     ,							SCREAMING_SNAKE_CASE__				:       int=True     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       str=True     ,							SCREAMING_SNAKE_CASE__				:       str=9_9     ,							SCREAMING_SNAKE_CASE__				:       str=2_4     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=6     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_7     ,							SCREAMING_SNAKE_CASE__				:       List[Any]="gelu"     ,							SCREAMING_SNAKE_CASE__				:       str=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[str]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       List[str]=1_6     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=2     ,							SCREAMING_SNAKE_CASE__				:       int=0.02     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=3     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_0_0_0     ,							)		->			str:
             a_					:		Optional[Any]							=  parent
             a_					:		List[str]							=  batch_size
             a_					:		List[str]							=  seq_length
             a_					:		str							=  is_training
             a_					:		str							=  use_input_mask
             a_					:		int							=  use_token_type_ids
             a_					:		List[str]							=  use_labels
             a_					:		Optional[int]							=  vocab_size
             a_					:		Any							=  hidden_size
             a_					:		int							=  num_hidden_layers
             a_					:		List[str]							=  num_attention_heads
             a_					:		str							=  intermediate_size
             a_					:		Union[str, Any]							=  hidden_act
             a_					:		List[str]							=  hidden_dropout_prob
             a_					:		int							=  attention_probs_dropout_prob
             a_					:		int							=  max_position_embeddings
             a_					:		Tuple							=  type_vocab_size
             a_					:		Optional[Any]							=  type_sequence_label_size
             a_					:		Tuple							=  initializer_range
             a_					:		Dict							=  num_labels
             a_					:		str							=  scope
             a_					:		Optional[int]							=  range_bbox
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			int:
             a_					:		Dict							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size     )

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

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

             a_					:		List[Any]							=  None
             if self.use_token_type_ids:
                    a_					:		Optional[Any]							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.type_vocab_size     )

             a_					:		int							=  None
             a_					:		Tuple							=  None
             if self.use_labels:
                    a_					:		List[Any]							=  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size     )
                    a_					:		int							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.num_labels     )

             a_					:		Optional[int]							=  self.get_config()

             return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             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        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							)		->			str:
             a_					:		Any							=  LiltModel(config=SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()
             a_					:		Any							=  model(SCREAMING_SNAKE_CASE__     ,							bbox=SCREAMING_SNAKE_CASE__     ,							attention_mask=SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  model(SCREAMING_SNAKE_CASE__     ,							bbox=SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  model(SCREAMING_SNAKE_CASE__     ,							bbox=SCREAMING_SNAKE_CASE__     )

             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        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							)		->			int:
             a_					:		Any							=  self.num_labels
             a_					:		str							=  LiltForTokenClassification(config=SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()
             a_					:		str							=  model(
                 SCREAMING_SNAKE_CASE__     ,							bbox=SCREAMING_SNAKE_CASE__     ,							attention_mask=SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.num_labels)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			str:
             a_					:		Union[str, Any]							=  LiltForQuestionAnswering(config=SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()
             a_					:		List[str]							=  model(
                 SCREAMING_SNAKE_CASE__     ,							bbox=SCREAMING_SNAKE_CASE__     ,							attention_mask=SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							start_positions=SCREAMING_SNAKE_CASE__     ,							end_positions=SCREAMING_SNAKE_CASE__     ,							)
             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        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			List[str]:
             a_					:		int							=  self.prepare_config_and_inputs()
             (
                 (
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		
             )					:		List[Any]							=  config_and_inputs
             a_					:		Optional[int]							=  {
                 'input_ids': input_ids,
                 'bbox': bbox,
                 'token_type_ids': token_type_ids,
                 'attention_mask': input_mask,
             }
             return config, inputs_dict
@require_torch
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__						,    lowercase__						,    unittest.TestCase			):
      snake_case__				:     Union[str, Any]        =						(
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      snake_case__				:     str        =						(
          {
              '''feature-extraction''': LiltModel,
              '''question-answering''': LiltForQuestionAnswering,
              '''text-classification''': LiltForSequenceClassification,
              '''token-classification''': LiltForTokenClassification,
              '''zero-shot''': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__				:     List[str]        =						False
      snake_case__				:     str        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			int:
             return True
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Tuple:
             a_					:		str							=  LiltModelTester(self     )
             a_					:		List[Any]							=  ConfigTester(self     ,							config_class=SCREAMING_SNAKE_CASE__     ,							hidden_size=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[Any]:
             self.config_tester.run_common_tests()
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			str:
             a_					:		Dict							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_					:		Tuple							=  self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    a_					:		List[str]							=  type
                    self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Optional[Any]:
             a_					:		List[Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[str]:
             a_					:		Optional[Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__     )

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Union[str, Any]:
             for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_					:		List[Any]							=  LiltModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )
@require_torch
@slow
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Union[str, Any]:
             a_					:		List[str]							=  LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'     ).to(SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  torch.tensor([[1, 2]]     ,							device=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]     ,							device=SCREAMING_SNAKE_CASE__     )

             # forward pass
             with torch.no_grad():
                    a_					:		str							=  model(input_ids=SCREAMING_SNAKE_CASE__     ,							bbox=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  torch.Size([1, 2, 7_6_8]     )
             a_					:		int							=  torch.tensor(
                 [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]     ,							device=SCREAMING_SNAKE_CASE__     ,							)

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


