






'''simple docstring'''







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__    :




  '''simple docstring'''





  def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=13   ,_lowerCAmelCase=7   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=99   ,_lowerCAmelCase=32   ,_lowerCAmelCase=5   ,_lowerCAmelCase=4   ,_lowerCAmelCase=37   ,_lowerCAmelCase="gelu"   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=5_12   ,_lowerCAmelCase=16   ,_lowerCAmelCase=2   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=3   ,_lowerCAmelCase=4   ,_lowerCAmelCase=None   ,):
        lowerCamelCase__         =  parent
        lowerCamelCase__         =  batch_size
        lowerCamelCase__         =  seq_length
        lowerCamelCase__         =  is_training
        lowerCamelCase__         =  use_input_mask
        lowerCamelCase__         =  use_token_type_ids
        lowerCamelCase__         =  use_labels
        lowerCamelCase__         =  vocab_size
        lowerCamelCase__         =  hidden_size
        lowerCamelCase__         =  num_hidden_layers
        lowerCamelCase__         =  num_attention_heads
        lowerCamelCase__         =  intermediate_size
        lowerCamelCase__         =  hidden_act
        lowerCamelCase__         =  hidden_dropout_prob
        lowerCamelCase__         =  attention_probs_dropout_prob
        lowerCamelCase__         =  max_position_embeddings
        lowerCamelCase__         =  type_vocab_size
        lowerCamelCase__         =  type_sequence_label_size
        lowerCamelCase__         =  initializer_range
        lowerCamelCase__         =  num_labels
        lowerCamelCase__         =  num_choices
        lowerCamelCase__         =  scope






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size       )

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

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

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

        lowerCamelCase__         =  self.get_config()

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






  def 			UpperCamelCase_					(      self       ):
        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=UpperCAmelCase__   ,initializer_range=self.initializer_range   ,)






  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  NystromformerModel(config=UpperCAmelCase__       )
        model.to(UpperCAmelCase__       )
        model.eval()
        lowerCamelCase__         =  model(UpperCAmelCase__   ,attention_mask=UpperCAmelCase__   ,token_type_ids=UpperCAmelCase__       )
        lowerCamelCase__         =  model(UpperCAmelCase__   ,token_type_ids=UpperCAmelCase__       )
        lowerCamelCase__         =  model(UpperCAmelCase__       )
        self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)       )






  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  NystromformerForMaskedLM(config=UpperCAmelCase__       )
        model.to(UpperCAmelCase__       )
        model.eval()
        lowerCamelCase__         =  model(UpperCAmelCase__   ,attention_mask=UpperCAmelCase__   ,token_type_ids=UpperCAmelCase__   ,labels=UpperCAmelCase__       )
        self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)       )






  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  NystromformerForQuestionAnswering(config=UpperCAmelCase__       )
        model.to(UpperCAmelCase__       )
        model.eval()
        lowerCamelCase__         =  model(
            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   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  self.num_labels
        lowerCamelCase__         =  NystromformerForSequenceClassification(UpperCAmelCase__       )
        model.to(UpperCAmelCase__       )
        model.eval()
        lowerCamelCase__         =  model(UpperCAmelCase__   ,attention_mask=UpperCAmelCase__   ,token_type_ids=UpperCAmelCase__   ,labels=UpperCAmelCase__       )
        self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)       )






  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  self.num_labels
        lowerCamelCase__         =  NystromformerForTokenClassification(config=UpperCAmelCase__       )
        model.to(UpperCAmelCase__       )
        model.eval()
        lowerCamelCase__         =  model(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   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  self.num_choices
        lowerCamelCase__         =  NystromformerForMultipleChoice(config=UpperCAmelCase__       )
        model.to(UpperCAmelCase__       )
        model.eval()
        lowerCamelCase__         =  input_ids.unsqueeze(1       ).expand(-1   ,self.num_choices   ,-1       ).contiguous()
        lowerCamelCase__         =  token_type_ids.unsqueeze(1       ).expand(-1   ,self.num_choices   ,-1       ).contiguous()
        lowerCamelCase__         =  input_mask.unsqueeze(1       ).expand(-1   ,self.num_choices   ,-1       ).contiguous()
        lowerCamelCase__         =  model(
            UpperCAmelCase__   ,attention_mask=UpperCAmelCase__   ,token_type_ids=UpperCAmelCase__   ,labels=UpperCAmelCase__   ,)
        self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_choices)       )






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




@require_torch
class 		UpperCamelCase__    (lowercase__   ,lowercase__   ,unittest.TestCase			):




  '''simple docstring'''





  _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       ):
        lowerCamelCase__         =  NystromformerModelTester(self       )
        lowerCamelCase__         =  ConfigTester(self   ,config_class=UpperCAmelCase__   ,hidden_size=37       )






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






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__       )






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              lowerCamelCase__         =  type
              self.model_tester.create_and_check_model(*UpperCAmelCase__       )






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__       )






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__       )






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__       )






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__       )






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__       )






  @slow
  def 			UpperCamelCase_					(      self       ):
        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCamelCase__         =  NystromformerModel.from_pretrained(UpperCAmelCase__       )
              self.assertIsNotNone(UpperCAmelCase__       )




@require_torch
class 		UpperCamelCase__    (unittest.TestCase			):




  '''simple docstring'''





  @slow
  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""       )
        lowerCamelCase__         =  torch.tensor([[0, 1, 2, 3, 4, 5]]       )

        with torch.no_grad():
              lowerCamelCase__         =  model(UpperCAmelCase__       )[0]

        lowerCamelCase__         =  torch.Size((1, 6, 7_68)       )
        self.assertEqual(output.shape   ,UpperCAmelCase__       )

        lowerCamelCase__         =  torch.tensor(
            [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]       )

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






  @slow
  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  '''the [MASK] of Belgium is Brussels'''

        lowerCamelCase__         =  AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""       )
        lowerCamelCase__         =  NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""       )

        lowerCamelCase__         =  tokenizer(UpperCAmelCase__   ,return_tensors="""pt"""       )

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

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

        self.assertEqual(tokenizer.decode(UpperCAmelCase__       )   ,"""capital"""       )









'''simple docstring'''







import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						if hor == 128:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""")

						elif hor == 32:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 64, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
						lowerCamelCase__         =  torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
						lowerCamelCase__         =  model.state_dict()
						lowerCamelCase__         =  {
						    """down_block_types""": down_block_types,
						    """block_out_channels""": block_out_channels,
						    """up_block_types""": up_block_types,
						    """layers_per_block""": 1,
						    """use_timestep_embedding""": True,
						    """out_block_type""": """OutConv1DBlock""",
						    """norm_num_groups""": 8,
						    """downsample_each_block""": False,
						    """in_channels""": 14,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """sample_size""": 6_5536,
						    """mid_block_type""": """MidResTemporalBlock1D""",
						    """act_fn""": """mish""",
						}
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)
						lowerCamelCase__         =  dict(zip(model.state_dict().keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)
						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    F'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''						)
						with open(F'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				):
						lowerCamelCase__         =  {
						    """in_channels""": 14,
						    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
						    """up_block_types""": (),
						    """out_block_type""": """ValueFunction""",
						    """mid_block_type""": """ValueFunctionMidBlock1D""",
						    """block_out_channels""": (32, 64, 128, 256),
						    """layers_per_block""": 1,
						    """downsample_each_block""": True,
						    """sample_size""": 6_5536,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """use_timestep_embedding""": True,
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """norm_num_groups""": 8,
						    """act_fn""": """mish""",
						}

						lowerCamelCase__         =  torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
						lowerCamelCase__         =  model
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)

						lowerCamelCase__         =  dict(zip(state_dict.keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)

						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""						)
						with open("""hub/hopper-medium-v2/value_function/config.json"""   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()

