
'''simple docstring'''






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

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


UpperCAmelCase_       :   Tuple   =       1E-4

if is_torch_available():
 import torch

 from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
 from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder

@require_torch
class  UpperCAmelCase__						:



    def __init__(	self      :       Tuple,__A      :       List[str],__A      :       Dict=1_6,__A      :       Tuple=1_3,__A      :       Any=7,__A      :       Tuple=1_4,__A      :       Optional[Any]=1_0,__A      :       int=1_9,__A      :       int=5,__A      :       Union[str, Any]=4,__A      :       Optional[int]=True,__A      :       List[Any]=1_6,__A      :       Optional[Any]=2,__A      :       Optional[int]=4,__A      :       Union[str, Any]=4,__A      :       List[Any]="gelu",__A      :       Union[str, Any]=0.1,__A      :       Any=0.1,__A      :       List[str]=[1, 2, 3, 4, 5],__A      :       Tuple=2_5,__A      :       List[Any]=5,):
          _lowerCamelCase				:   Optional[int]								=   d_model
          _lowerCamelCase				:   List[str]								=   parent
          _lowerCamelCase				:   int								=   batch_size
          _lowerCamelCase				:   Union[str, Any]								=   prediction_length
          _lowerCamelCase				:   Optional[Any]								=   context_length
          _lowerCamelCase				:   Tuple								=   cardinality
          _lowerCamelCase				:   Any								=   num_time_features
          _lowerCamelCase				:   str								=   lags_sequence
          _lowerCamelCase				:   List[Any]								=   embedding_dimension
          _lowerCamelCase				:   Any								=   is_training
          _lowerCamelCase				:   Union[str, Any]								=   hidden_size
          _lowerCamelCase				:   int								=   num_hidden_layers
          _lowerCamelCase				:   List[str]								=   num_attention_heads
          _lowerCamelCase				:   List[Any]								=   intermediate_size
          _lowerCamelCase				:   List[str]								=   hidden_act
          _lowerCamelCase				:   Any								=   hidden_dropout_prob
          _lowerCamelCase				:   Dict								=   attention_probs_dropout_prob

          _lowerCamelCase				:   Optional[int]								=   context_length
          _lowerCamelCase				:   Union[str, Any]								=   prediction_length + label_length
          _lowerCamelCase				:   str								=   label_length

          _lowerCamelCase				:   List[Any]								=   moving_average
          _lowerCamelCase				:   Optional[Any]								=   autocorrelation_factor



    def   lowerCamelCase_  (	self      :       Tuple     ):
          return AutoformerConfig(
              d_model=self.d_model,encoder_layers=self.num_hidden_layers,decoder_layers=self.num_hidden_layers,encoder_attention_heads=self.num_attention_heads,decoder_attention_heads=self.num_attention_heads,encoder_ffn_dim=self.intermediate_size,decoder_ffn_dim=self.intermediate_size,dropout=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,prediction_length=self.prediction_length,context_length=self.context_length,label_length=self.label_length,lags_sequence=self.lags_sequence,num_time_features=self.num_time_features,num_static_categorical_features=1,cardinality=[self.cardinality],embedding_dimension=[self.embedding_dimension],moving_average=self.moving_average,)



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       int     ):
          _lowerCamelCase				:   List[str]								=   config.context_length + max(config.lags_sequence     )

          _lowerCamelCase				:   str								=   ids_tensor([self.batch_size, 1],config.cardinality[0]     )
          _lowerCamelCase				:   Dict								=   floats_tensor([self.batch_size, _past_length, config.num_time_features]     )
          _lowerCamelCase				:   int								=   floats_tensor([self.batch_size, _past_length]     )
          _lowerCamelCase				:   Optional[int]								=   floats_tensor([self.batch_size, _past_length]     ) > 0.5

          # decoder inputs
          _lowerCamelCase				:   int								=   floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]     )
          _lowerCamelCase				:   List[str]								=   floats_tensor([self.batch_size, config.prediction_length]     )

          _lowerCamelCase				:   Optional[int]								=   {
              "past_values": past_values,
              "static_categorical_features": static_categorical_features,
              "past_time_features": past_time_features,
              "past_observed_mask": past_observed_mask,
              "future_time_features": future_time_features,
              "future_values": future_values,
          }
          return inputs_dict



    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.get_config()
          _lowerCamelCase				:   Union[str, Any]								=   self.prepare_autoformer_inputs_dict(__A     )
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   self.prepare_config_and_inputs()
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       str,__A      :       Any,__A      :       Union[str, Any]     ):
          _lowerCamelCase				:   Any								=   AutoformerModel(config=__A     ).to(__A     ).eval()
          _lowerCamelCase				:   Optional[Any]								=   model(**__A     )

          _lowerCamelCase				:   Optional[Any]								=   outputs.encoder_last_hidden_state
          _lowerCamelCase				:   int								=   outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase				:   List[Any]								=   model.get_encoder()
                encoder.save_pretrained(__A     )
                _lowerCamelCase				:   List[Any]								=   AutoformerEncoder.from_pretrained(__A     ).to(__A     )

          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Any								=   model.create_network_inputs(**__A     )
          _lowerCamelCase						,		_lowerCamelCase				:   str								=   model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]     )

          _lowerCamelCase				:   int								=   torch.cat(
              (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]),dim=-1,)
          _lowerCamelCase				:   Tuple								=   encoder(inputs_embeds=__A     )[0]
          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3     )

          _lowerCamelCase				:   List[str]								=   (
              torch.mean(transformer_inputs[:, : config.context_length, ...],dim=1     )
              .unsqueeze(1     )
              .repeat(1,config.prediction_length,1     )
          )
          _lowerCamelCase				:   Union[str, Any]								=   torch.zeros(
              [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]],device=enc_input.device,)

          _lowerCamelCase				:   Dict								=   torch.cat(
              (
                  torch.cat((seasonal_input[:, -config.label_length :, ...], zeros),dim=1     ),
                  feature[:, config.context_length - config.label_length :, ...],
              ),dim=-1,)
          _lowerCamelCase				:   Tuple								=   torch.cat(
              (
                  torch.cat((trend_input[:, -config.label_length :, ...], mean),dim=1     ),
                  feature[:, config.context_length - config.label_length :, ...],
              ),dim=-1,)

          with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase				:   int								=   model.get_decoder()
                decoder.save_pretrained(__A     )
                _lowerCamelCase				:   Optional[Any]								=   AutoformerDecoder.from_pretrained(__A     ).to(__A     )

          _lowerCamelCase				:   List[Any]								=   decoder(
              trend=__A,inputs_embeds=__A,encoder_hidden_states=__A,)[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3     )


@require_torch
class  UpperCAmelCase__						(       A						,	A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
    lowerCAmelCase_				    =		(AutoformerForPrediction,) if is_torch_available() else ()
    lowerCAmelCase_				    =		{'feature-extraction': AutoformerModel} if is_torch_available() else {}
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   int								=   AutoformerModelTester(self     )
          _lowerCamelCase				:   List[str]								=   ConfigTester(self,config_class=__A,has_text_modality=__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          self.config_tester.run_common_tests()



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   str								=   self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class(__A     )

                with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(__A     )
                      _lowerCamelCase						,		_lowerCamelCase				:   str								=   model_class.from_pretrained(__A,output_loading_info=__A     )
                self.assertEqual(info["missing_keys"],[]     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*__A     )



    @unittest.skip(reason="Model has no tokens embeddings"     )
    def   lowerCamelCase_  (	self      :       List[str]     ):
          pass



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   str								=   inspect.signature(getattr(__A,"forward"     )     )
          # The main input is the name of the argument after `self`
          _lowerCamelCase				:   int								=   list(model_signature.parameters.keys()     )[1]
          self.assertEqual(AutoformerModel.main_input_name,__A     )



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Dict								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                _lowerCamelCase				:   Any								=   model_class(__A     )
                _lowerCamelCase				:   Any								=   inspect.signature(model.forward     )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _lowerCamelCase				:   Optional[Any]								=   [*signature.parameters.keys()]

                _lowerCamelCase				:   str								=   [
                    "past_values",
                    "past_time_features",
                    "past_observed_mask",
                    "static_categorical_features",
                    "static_real_features",
                    "future_values",
                    "future_time_features",
                ]

                if model.__class__.__name__ in ["AutoformerForPrediction"]:
                      expected_arg_names.append("future_observed_mask"     )

                expected_arg_names.extend(
                    [
                        "decoder_attention_mask",
                        "head_mask",
                        "decoder_head_mask",
                        "cross_attn_head_mask",
                        "encoder_outputs",
                        "past_key_values",
                        "output_hidden_states",
                        "output_attentions",
                        "use_cache",
                        "return_dict",
                    ]     )

                self.assertListEqual(arg_names[: len(__A     )],__A     )



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   self.model_tester.prepare_config_and_inputs_for_common()
          _lowerCamelCase				:   List[str]								=   True

          _lowerCamelCase				:   Optional[int]								=   getattr(self.model_tester,"seq_length",__A     )
          _lowerCamelCase				:   List[str]								=   getattr(self.model_tester,"decoder_seq_length",__A     )
          _lowerCamelCase				:   List[str]								=   getattr(self.model_tester,"encoder_seq_length",__A     )
          _lowerCamelCase				:   Dict								=   getattr(self.model_tester,"d_model",__A     )
          _lowerCamelCase				:   List[str]								=   getattr(self.model_tester,"num_attention_heads",__A     )
          _lowerCamelCase				:   Optional[Any]								=   d_model // num_attention_heads

          for model_class in self.all_model_classes:
                _lowerCamelCase				:   List[str]								=   True
                _lowerCamelCase				:   Any								=   False
                _lowerCamelCase				:   Dict								=   True
                _lowerCamelCase				:   Optional[Any]								=   model_class(__A     )
                model.to(__A     )
                model.eval()
                with torch.no_grad():
                      _lowerCamelCase				:   str								=   model(**self._prepare_for_class(__A,__A     )     )
                _lowerCamelCase				:   Any								=   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                self.assertEqual(len(__A     ),self.model_tester.num_hidden_layers     )

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                _lowerCamelCase				:   List[Any]								=   True
                _lowerCamelCase				:   Any								=   model_class(__A     )
                model.to(__A     )
                model.eval()
                with torch.no_grad():
                      _lowerCamelCase				:   Optional[Any]								=   model(**self._prepare_for_class(__A,__A     )     )
                _lowerCamelCase				:   int								=   outputs.encoder_attentions
                self.assertEqual(len(__A     ),self.model_tester.num_hidden_layers     )

                self.assertListEqual(
                    list(attentions[0].shape[-3:]     ),[self.model_tester.num_attention_heads, encoder_seq_length, dim],)
                _lowerCamelCase				:   str								=   len(__A     )

                _lowerCamelCase				:   Optional[Any]								=   7

                if "last_hidden_state" in outputs:
                      correct_outlen += 1

                if "trend" in outputs:
                      correct_outlen += 1

                if "past_key_values" in outputs:
                      correct_outlen += 1  # past_key_values have been returned

                if "loss" in outputs:
                      correct_outlen += 1

                if "params" in outputs:
                      correct_outlen += 1

                self.assertEqual(__A,__A     )

                # decoder attentions
                _lowerCamelCase				:   Optional[Any]								=   outputs.decoder_attentions
                self.assertIsInstance(__A,(list, tuple)     )
                self.assertEqual(len(__A     ),self.model_tester.num_hidden_layers     )
                self.assertListEqual(
                    list(decoder_attentions[0].shape[-3:]     ),[self.model_tester.num_attention_heads, decoder_seq_length, dim],)

                # cross attentions
                _lowerCamelCase				:   int								=   outputs.cross_attentions
                self.assertIsInstance(__A,(list, tuple)     )
                self.assertEqual(len(__A     ),self.model_tester.num_hidden_layers     )
                self.assertListEqual(
                    list(cross_attentions[0].shape[-3:]     ),[self.model_tester.num_attention_heads, decoder_seq_length, dim],)

          # Check attention is always last and order is fine
          _lowerCamelCase				:   Dict								=   True
          _lowerCamelCase				:   Union[str, Any]								=   True
          _lowerCamelCase				:   List[Any]								=   model_class(__A     )
          model.to(__A     )
          model.eval()
          with torch.no_grad():
                _lowerCamelCase				:   List[Any]								=   model(**self._prepare_for_class(__A,__A     )     )

          self.assertEqual(out_len + 2,len(__A     )     )

          _lowerCamelCase				:   List[str]								=   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

          self.assertEqual(len(__A     ),self.model_tester.num_hidden_layers     )
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]     ),[self.model_tester.num_attention_heads, encoder_seq_length, dim],)



    @is_flaky()
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          super().test_retain_grad_hidden_states_attentions()







def 		A_       (	_lowerCAmelCase   :    List[str]="train-batch.pt"							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch"				,	filename=_lowerCAmelCase				,	repo_type="dataset"							)
      _lowerCamelCase				:   Optional[int]								=   torch.load(_lowerCAmelCase				,	map_location=_lowerCAmelCase							)
      return batch



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



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Dict								=   AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(__A     )
          _lowerCamelCase				:   Tuple								=   prepare_batch()

          with torch.no_grad():
                _lowerCamelCase				:   List[Any]								=   model(
                    past_values=batch["past_values"],past_time_features=batch["past_time_features"],past_observed_mask=batch["past_observed_mask"],static_categorical_features=batch["static_categorical_features"],future_values=batch["future_values"],future_time_features=batch["future_time_features"],)[0]

          _lowerCamelCase				:   List[Any]								=   torch.Size(
              (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)     )
          self.assertEqual(output.shape,__A     )

          _lowerCamelCase				:   List[str]								=   torch.tensor(
              [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]],device=__A     )
          self.assertTrue(torch.allclose(output[0, :3, :3],__A,atol=__A     )     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(__A     )
          _lowerCamelCase				:   Dict								=   prepare_batch("val-batch.pt"     )
          with torch.no_grad():
                _lowerCamelCase				:   Optional[int]								=   model(
                    past_values=batch["past_values"],past_time_features=batch["past_time_features"],past_observed_mask=batch["past_observed_mask"],static_categorical_features=batch["static_categorical_features"],).encoder_last_hidden_state
          _lowerCamelCase				:   Optional[int]								=   torch.Size((6_4, model.config.context_length, model.config.d_model)     )
          self.assertEqual(output.shape,__A     )

          _lowerCamelCase				:   Optional[int]								=   torch.tensor(
              [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]],device=__A     )
          self.assertTrue(torch.allclose(output[0, :3, :3],__A,atol=__A     )     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(__A     )
          _lowerCamelCase				:   Any								=   prepare_batch("val-batch.pt"     )
          with torch.no_grad():
                _lowerCamelCase				:   List[str]								=   model.generate(
                    static_categorical_features=batch["static_categorical_features"],past_time_features=batch["past_time_features"],past_values=batch["past_values"],future_time_features=batch["future_time_features"],past_observed_mask=batch["past_observed_mask"],)
          _lowerCamelCase				:   Optional[Any]								=   torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)     )
          self.assertEqual(outputs.sequences.shape,__A     )

          _lowerCamelCase				:   Any								=   torch.tensor([3130.6763, 4056.5293, 7053.0786],device=__A     )
          _lowerCamelCase				:   Union[str, Any]								=   outputs.sequences.mean(dim=1     )
          self.assertTrue(torch.allclose(mean_prediction[0, -3:],__A,rtol=1e-1     )     )

'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_       :   Optional[int]   =       logging.get_logger(__name__)







def 		A_       (	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)
      if "model" in sd.keys():
            _lowerCamelCase				:   Optional[Any]								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]

      # pop unnecessary weights
      _lowerCamelCase				:   Optional[Any]								=   [
          "decoder.version",
          "decoder.output_projection.weight",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Union[str, Any]								=   {
          "decoder.project_in_dim.weight": "decoder.project_in.weight",
          "decoder.project_out_dim.weight": "decoder.project_out.weight",
          "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
          "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  _lowerCamelCase				:   Optional[Any]								=   sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Any								=   list(sd.keys()							)
      for key in keys:
            if ".qkv_proj." in key:
                  _lowerCamelCase				:   Optional[Any]								=   sd[key]
                  # We split QKV in separate Q,K,V

                  _lowerCamelCase				:   int								=   key.replace(".qkv_proj."				,	".q_proj."							)
                  _lowerCamelCase				:   List[Any]								=   key.replace(".qkv_proj."				,	".k_proj."							)
                  _lowerCamelCase				:   Dict								=   key.replace(".qkv_proj."				,	".v_proj."							)

                  _lowerCamelCase				:   Any								=   value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   torch.split(_lowerCAmelCase				,	depth // 3				,	dim=0							)

                  _lowerCamelCase				:   str								=   q
                  _lowerCamelCase				:   Dict								=   k
                  _lowerCamelCase				:   int								=   v
                  del sd[key]

      return sd







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    List[str]=None							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   load_checkpoint(_lowerCAmelCase							)

      if config is not None:
            _lowerCamelCase				:   str								=   OPTConfig.from_pretrained(_lowerCAmelCase							)
      else:
            _lowerCamelCase				:   Any								=   OPTConfig()

      _lowerCamelCase				:   Optional[int]								=   OPTModel(_lowerCAmelCase							).half().eval()
      model.load_state_dict(_lowerCAmelCase							)

      # Check results
      Path(_lowerCAmelCase							).mkdir(exist_ok=_lowerCAmelCase							)
      model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   List[str]   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--fairseq_path',
     type=str,
     help=(
         'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
         ' https://huggingface.co/models?other=opt_metasq'
     ),
 )
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
 UpperCAmelCase_       :   str   =       parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)