

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


a_    :						List[str]    		=      {
    '''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        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Optional[Any]	)		->							Tuple:
 '''simple docstring'''


 return torch.atana(_UpperCamelCase		,							_UpperCamelCase	) / math.pi * 2


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			torch.sin(t * math.pi / 2	) ** 2
 SCREAMING_SNAKE_CASE								=			(1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(_UpperCamelCase		,							_UpperCamelCase	)




class      UpperCamelCase (     UpperCamelCase__   ):
    pass




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Any							,    snake_case__ :      Any    ):

     """simple docstring"""






     super().__init__()

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any	)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			MODELS_MAP[model_name]['url']
 os.system(f"""wget {url} ./"""	)

 return f"""./{model_name}.ckpt"""


a_    :						List[Any]    		=      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
a_    :						List[str]    		=      {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
a_    :						List[Any]    		=      {
    '''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''',
}
a_    :						Optional[int]    		=      {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

a_    :						List[Any]    		=      {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]	)		->							str:
 '''simple docstring'''


 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        __lowerCAmelCase    (       _UpperCamelCase		:  str	)		->							Any:
 '''simple docstring'''


 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        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[int]=13	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			input_string

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

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

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

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

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

 if depth == max_depth:
  SCREAMING_SNAKE_CASE								=			MID_NUM_TO_LAYER[layer_num]
  SCREAMING_SNAKE_CASE								=			'mid_block'
 elif depth > 0 and int(_UpperCamelCase	) < 7:
  SCREAMING_SNAKE_CASE								=			DOWN_NUM_TO_LAYER[layer_num]
  SCREAMING_SNAKE_CASE								=			f"""down_blocks.{depth}"""
 elif depth > 0 and int(_UpperCamelCase	) > 7:
  SCREAMING_SNAKE_CASE								=			UP_NUM_TO_LAYER[layer_num]
  SCREAMING_SNAKE_CASE								=			f"""up_blocks.{max_depth - depth - 1}"""
 elif depth == 0:
  SCREAMING_SNAKE_CASE								=			DEPTH_0_TO_LAYER[layer_num]
  SCREAMING_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}."""	)

 SCREAMING_SNAKE_CASE								=			string_left[1:]

 if "resnets" in new_layer:
  SCREAMING_SNAKE_CASE								=			convert_resconv_naming(_UpperCamelCase	)
 elif "attentions" in new_layer:
  SCREAMING_SNAKE_CASE								=			convert_attn_naming(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			new_string_left

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]	)		->							str:
 '''simple docstring'''


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

  SCREAMING_SNAKE_CASE								=			rename(_UpperCamelCase	)

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

 return new_state_dict


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int	)		->							Tuple:
 '''simple docstring'''


 if len(_UpperCamelCase	) == 1:
  if len(v.shape	) == 3:
   # weight
   SCREAMING_SNAKE_CASE								=			v[:, :, 0]
  else:
   # bias
   SCREAMING_SNAKE_CASE								=			v
 else:
  # qkv matrices
  SCREAMING_SNAKE_CASE								=			v.shape[0]
  SCREAMING_SNAKE_CASE								=			trippled_shape // 3
  for i in range(3	):
   if len(v.shape	) == 3:
    SCREAMING_SNAKE_CASE								=			v[i * single_shape : (i + 1) * single_shape, :, 0]
   else:
    SCREAMING_SNAKE_CASE								=			v[i * single_shape : (i + 1) * single_shape]
 return new_state_dict


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

 SCREAMING_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()}"""
  SCREAMING_SNAKE_CASE								=			download(_UpperCamelCase	)

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

 SCREAMING_SNAKE_CASE								=			Object()
 SCREAMING_SNAKE_CASE								=			sample_size
 SCREAMING_SNAKE_CASE								=			sample_rate
 SCREAMING_SNAKE_CASE								=			0

 SCREAMING_SNAKE_CASE								=			UNetaDModel(sample_size=_UpperCamelCase		,							sample_rate=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			diffusers_model.state_dict()

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

 SCREAMING_SNAKE_CASE								=			set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
 SCREAMING_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":
   SCREAMING_SNAKE_CASE								=			value.squeeze()

  SCREAMING_SNAKE_CASE								=			value

 diffusers_model.load_state_dict(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			1_00
 SCREAMING_SNAKE_CASE								=			33

 SCREAMING_SNAKE_CASE								=			IPNDMScheduler(num_train_timesteps=_UpperCamelCase	)

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

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

 SCREAMING_SNAKE_CASE								=			DanceDiffusionPipeline(unet=_UpperCamelCase		,							scheduler=_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			torch.manual_seed(33	)
 SCREAMING_SNAKE_CASE								=			pipe(num_inference_steps=_UpperCamelCase		,							generator=_UpperCamelCase	).audios

 SCREAMING_SNAKE_CASE								=			sampling.iplms_sample(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							{}	)
 SCREAMING_SNAKE_CASE								=			generated.clamp(-1		,							1	)

 SCREAMING_SNAKE_CASE								=			(generated - audio).abs().sum()
 SCREAMING_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__":
    a_    :						List[str]    		=      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.")
    a_    :						Any    		=      parser.parse_args()

    main(args)




import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          NystromformerForMaskedLM,
          NystromformerForMultipleChoice,
          NystromformerForQuestionAnswering,
          NystromformerForSequenceClassification,
          NystromformerForTokenClassification,
          NystromformerModel,
      )
      from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCamelCase :

    def __init__(			self :      List[str]							,    snake_case__ :      Dict							,    snake_case__ :      Optional[Any]=1_3							,    snake_case__ :      Union[str, Any]=7							,    snake_case__ :      List[str]=True							,    snake_case__ :      Any=True							,    snake_case__ :      List[str]=True							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      List[str]=9_9							,    snake_case__ :      str=3_2							,    snake_case__ :      Dict=5							,    snake_case__ :      str=4							,    snake_case__ :      int=3_7							,    snake_case__ :      Union[str, Any]="gelu"							,    snake_case__ :      Any=0.1							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Optional[Any]=5_1_2							,    snake_case__ :      List[Any]=1_6							,    snake_case__ :      str=2							,    snake_case__ :      int=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      Dict=4							,    snake_case__ :      str=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			seq_length
     SCREAMING_SNAKE_CASE								=			is_training
     SCREAMING_SNAKE_CASE								=			use_input_mask
     SCREAMING_SNAKE_CASE								=			use_token_type_ids
     SCREAMING_SNAKE_CASE								=			use_labels
     SCREAMING_SNAKE_CASE								=			vocab_size
     SCREAMING_SNAKE_CASE								=			hidden_size
     SCREAMING_SNAKE_CASE								=			num_hidden_layers
     SCREAMING_SNAKE_CASE								=			num_attention_heads
     SCREAMING_SNAKE_CASE								=			intermediate_size
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
     SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE								=			max_position_embeddings
     SCREAMING_SNAKE_CASE								=			type_vocab_size
     SCREAMING_SNAKE_CASE								=			type_sequence_label_size
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			num_labels
     SCREAMING_SNAKE_CASE								=			num_choices
     SCREAMING_SNAKE_CASE								=			scope

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE								=			random_attention_mask([self.batch_size, self.seq_length]    )

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

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

     SCREAMING_SNAKE_CASE								=			self.get_config()

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

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     return NystromformerConfig(
         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							,    is_decoder=snake_case__							,    initializer_range=self.initializer_range							,    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Tuple							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Dict							,    snake_case__ :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    token_type_ids=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[Any]							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForQuestionAnswering(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    start_positions=snake_case__							,    end_positions=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 UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)    )

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Tuple							,    snake_case__ :      List[str]							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForTokenClassification(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_choices
     SCREAMING_SNAKE_CASE								=			NystromformerForMultipleChoice(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			input_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			token_type_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			input_mask.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__							,    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)    )




    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	
     )								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (
            NystromformerModel,
            NystromformerForMaskedLM,
            NystromformerForMultipleChoice,
            NystromformerForQuestionAnswering,
            NystromformerForSequenceClassification,
            NystromformerForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    __UpperCamelCase         =(
        {
            "feature-extraction": NystromformerModel,
            "fill-mask": NystromformerForMaskedLM,
            "question-answering": NystromformerForQuestionAnswering,
            "text-classification": NystromformerForSequenceClassification,
            "token-classification": NystromformerForTokenClassification,
            "zero-shot": NystromformerForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      SCREAMING_SNAKE_CASE								=			type
      self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case__    )




    @slow
    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE								=			NystromformerModel.from_pretrained(snake_case__    )
      self.assertIsNotNone(snake_case__    )




@require_torch
class      UpperCamelCase (     unittest.TestCase   ):

    @slow
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModel.from_pretrained('uw-madison/nystromformer-512'    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([[0, 1, 2, 3, 4, 5]]    )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE								=			model(snake_case__    )[0]

     SCREAMING_SNAKE_CASE								=			torch.Size((1, 6, 7_6_8)    )
     self.assertEqual(output.shape							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			torch.tensor(
         [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]    )

     self.assertTrue(torch.allclose(output[:, :3, :3]							,    snake_case__							,    atol=1E-4    )    )




    @slow
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'the [MASK] of Belgium is Brussels'

     SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'    )
     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'    )

     SCREAMING_SNAKE_CASE								=			tokenizer(snake_case__							,    return_tensors='pt'    )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE								=			model(encoding.input_ids    ).logits

     SCREAMING_SNAKE_CASE								=			token_logits[:, 2, :].argmax(-1    )[0]

     self.assertEqual(tokenizer.decode(snake_case__    )							,    'capital'    )

