





"""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


SCREAMING_SNAKE_CASE__					=   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 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase=16      ,						lowercase=13      ,						lowercase=7      ,						lowercase=14      ,						lowercase=10      ,						lowercase=19      ,						lowercase=5      ,						lowercase=4      ,						lowercase=True      ,						lowercase=16      ,						lowercase=2      ,						lowercase=4      ,						lowercase=4      ,						lowercase="gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=[1, 2, 3, 4, 5]      ,						lowercase=25      ,						lowercase=5      ,						)		->     Any:
            lowerCAmelCase  						=       d_model
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       prediction_length
            lowerCAmelCase  						=       context_length
            lowerCAmelCase  						=       cardinality
            lowerCAmelCase  						=       num_time_features
            lowerCAmelCase  						=       lags_sequence
            lowerCAmelCase  						=       embedding_dimension
            lowerCAmelCase  						=       is_training
            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  						=       context_length
            lowerCAmelCase  						=       prediction_length + label_length
            lowerCAmelCase  						=       label_length

            lowerCAmelCase  						=       moving_average
            lowerCAmelCase  						=       autocorrelation_factor





      def     _snake_case			(       self )		->     Optional[int]:
            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     _snake_case			(       self      ,						lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       config.context_length + max(config.lags_sequence )

            lowerCAmelCase  						=       ids_tensor([self.batch_size, 1]      ,						config.cardinality[0] )
            lowerCAmelCase  						=       floats_tensor([self.batch_size, _past_length, config.num_time_features] )
            lowerCAmelCase  						=       floats_tensor([self.batch_size, _past_length] )
            lowerCAmelCase  						=       floats_tensor([self.batch_size, _past_length] ) > 0.5

            # decoder inputs
            lowerCAmelCase  						=       floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
            lowerCAmelCase  						=       floats_tensor([self.batch_size, config.prediction_length] )

            lowerCAmelCase  						=       {
                """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     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       self.get_config()
            lowerCAmelCase  						=       self.prepare_autoformer_inputs_dict(lowercase )
            return config, inputs_dict





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.prepare_config_and_inputs()
            return config, inputs_dict





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       AutoformerModel(config=lowercase ).to(lowercase ).eval()
            lowerCAmelCase  						=       model(**lowercase )

            lowerCAmelCase  						=       outputs.encoder_last_hidden_state
            lowerCAmelCase  						=       outputs.last_hidden_state

            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowerCAmelCase  						=       model.get_encoder()
                  encoder.save_pretrained(lowercase )
                  lowerCAmelCase  						=       AutoformerEncoder.from_pretrained(lowercase ).to(lowercase )

            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       model.create_network_inputs(**lowercase )
            lowerCAmelCase						,		lowerCAmelCase  						=       model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )

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

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

            lowerCAmelCase  						=       torch.cat(
                (
                    torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)      ,						dim=1 ),
                    feature[:, config.context_length - config.label_length :, ...],
                )      ,						dim=-1      ,						)
            lowerCAmelCase  						=       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  						=       model.get_decoder()
                  decoder.save_pretrained(lowercase )
                  lowerCAmelCase  						=       AutoformerDecoder.from_pretrained(lowercase ).to(lowercase )

            lowerCAmelCase  						=       decoder(
                trend=lowercase      ,						inputs_embeds=lowercase      ,						encoder_hidden_states=lowercase      ,						)[0]

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







@require_torch
class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE					=      (AutoformerForPrediction,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE					=      {'feature-extraction': AutoformerModel} if is_torch_available() else {}
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       AutoformerModelTester(self )
            lowerCAmelCase  						=       ConfigTester(self      ,						config_class=lowercase      ,						has_text_modality=lowercase )





      def     _snake_case			(       self )		->     Optional[Any]:
            self.config_tester.run_common_tests()





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                  lowerCAmelCase  						=       model_class(lowercase )

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





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_encoder_decoder_model_standalone(*lowercase )





      @unittest.skip(reason="""Model has no tokens embeddings""" )
      def     _snake_case			(       self )		->     str:
            pass





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       inspect.signature(getattr(lowercase      ,						"""forward""" ) )
            # The main input is the name of the argument after `self`
            lowerCAmelCase  						=       list(model_signature.parameters.keys() )[1]
            self.assertEqual(AutoformerModel.main_input_name      ,						lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowerCAmelCase  						=       [
                      """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(lowercase )]      ,						lowercase )





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase  						=       True

            lowerCAmelCase  						=       getattr(self.model_tester      ,						"""seq_length"""      ,						lowercase )
            lowerCAmelCase  						=       getattr(self.model_tester      ,						"""decoder_seq_length"""      ,						lowercase )
            lowerCAmelCase  						=       getattr(self.model_tester      ,						"""encoder_seq_length"""      ,						lowercase )
            lowerCAmelCase  						=       getattr(self.model_tester      ,						"""d_model"""      ,						lowercase )
            lowerCAmelCase  						=       getattr(self.model_tester      ,						"""num_attention_heads"""      ,						lowercase )
            lowerCAmelCase  						=       d_model // num_attention_heads

            for model_class in self.all_model_classes:
                  lowerCAmelCase  						=       True
                  lowerCAmelCase  						=       False
                  lowerCAmelCase  						=       True
                  lowerCAmelCase  						=       model_class(lowercase )
                  model.to(lowercase )
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase  						=       model(**self._prepare_for_class(lowercase      ,						lowercase ) )
                  lowerCAmelCase  						=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(lowercase )      ,						self.model_tester.num_hidden_layers )

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

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

                  lowerCAmelCase  						=       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(lowercase      ,						lowercase )

                  # decoder attentions
                  lowerCAmelCase  						=       outputs.decoder_attentions
                  self.assertIsInstance(lowercase      ,						(list, tuple) )
                  self.assertEqual(len(lowercase )      ,						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  						=       outputs.cross_attentions
                  self.assertIsInstance(lowercase      ,						(list, tuple) )
                  self.assertEqual(len(lowercase )      ,						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  						=       True
            lowerCAmelCase  						=       True
            lowerCAmelCase  						=       model_class(lowercase )
            model.to(lowercase )
            model.eval()
            with torch.no_grad():
                  lowerCAmelCase  						=       model(**self._prepare_for_class(lowercase      ,						lowercase ) )

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

            lowerCAmelCase  						=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

            self.assertEqual(len(lowercase )      ,						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     _snake_case			(       self )		->     Any:
            super().test_retain_grad_hidden_states_attentions()







def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[str]="train-batch.pt"			):







      '''simple docstring'''




      lowerCAmelCase  						=       hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""							,						filename=SCREAMING_SNAKE_CASE							,						repo_type="""dataset"""			)
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=SCREAMING_SNAKE_CASE			)
      return batch






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





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(lowercase )
            lowerCAmelCase  						=       prepare_batch()

            with torch.no_grad():
                  lowerCAmelCase  						=       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  						=       torch.Size(
                (64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
            self.assertEqual(output.shape      ,						lowercase )

            lowerCAmelCase  						=       torch.tensor(
                [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]      ,						device=lowercase )
            self.assertTrue(torch.allclose(output[0, :3, :3]      ,						lowercase      ,						atol=lowercase ) )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(lowercase )
            lowerCAmelCase  						=       prepare_batch("""val-batch.pt""" )
            with torch.no_grad():
                  lowerCAmelCase  						=       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  						=       torch.Size((64, model.config.context_length, model.config.d_model) )
            self.assertEqual(output.shape      ,						lowercase )

            lowerCAmelCase  						=       torch.tensor(
                [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]      ,						device=lowercase )
            self.assertTrue(torch.allclose(output[0, :3, :3]      ,						lowercase      ,						atol=lowercase ) )





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(lowercase )
            lowerCAmelCase  						=       prepare_batch("""val-batch.pt""" )
            with torch.no_grad():
                  lowerCAmelCase  						=       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  						=       torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
            self.assertEqual(outputs.sequences.shape      ,						lowercase )

            lowerCAmelCase  						=       torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]      ,						device=lowercase )
            lowerCAmelCase  						=       outputs.sequences.mean(dim=1 )
            self.assertTrue(torch.allclose(mean_prediction[0, -3:]      ,						lowercase      ,						rtol=1e-1 ) )













"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      lowerCAmelCase  						=       OrderedDict()
      for key, value in state_dict.items():
            if key.startswith("""module.encoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.encoder"""							,						"""glpn.encoder"""			)
            if key.startswith("""module.decoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.decoder"""							,						"""decoder.stages"""			)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCAmelCase  						=       key[key.find("""patch_embed"""			) + len("""patch_embed"""			)]
                  lowerCAmelCase  						=       key.replace(F'patch_embed{idx}'							,						F'patch_embeddings.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "norm" in key:
                  lowerCAmelCase  						=       key.replace("""norm"""							,						"""layer_norm"""			)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCAmelCase  						=       key[key.find("""glpn.encoder.layer_norm"""			) + len("""glpn.encoder.layer_norm"""			)]
                  lowerCAmelCase  						=       key.replace(F'layer_norm{idx}'							,						F'layer_norm.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "layer_norm1" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm1"""							,						"""layer_norm_1"""			)
            if "layer_norm2" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm2"""							,						"""layer_norm_2"""			)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCAmelCase  						=       key[key.find("""block"""			) + len("""block"""			)]
                  lowerCAmelCase  						=       key.replace(F'block{idx}'							,						F'block.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "attn.q" in key:
                  lowerCAmelCase  						=       key.replace("""attn.q"""							,						"""attention.self.query"""			)
            if "attn.proj" in key:
                  lowerCAmelCase  						=       key.replace("""attn.proj"""							,						"""attention.output.dense"""			)
            if "attn" in key:
                  lowerCAmelCase  						=       key.replace("""attn"""							,						"""attention.self"""			)
            if "fc1" in key:
                  lowerCAmelCase  						=       key.replace("""fc1"""							,						"""dense1"""			)
            if "fc2" in key:
                  lowerCAmelCase  						=       key.replace("""fc2"""							,						"""dense2"""			)
            if "linear_pred" in key:
                  lowerCAmelCase  						=       key.replace("""linear_pred"""							,						"""classifier"""			)
            if "linear_fuse" in key:
                  lowerCAmelCase  						=       key.replace("""linear_fuse.conv"""							,						"""linear_fuse"""			)
                  lowerCAmelCase  						=       key.replace("""linear_fuse.bn"""							,						"""batch_norm"""			)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCAmelCase  						=       key[key.find("""linear_c"""			) + len("""linear_c"""			)]
                  lowerCAmelCase  						=       key.replace(F'linear_c{idx}'							,						F'linear_c.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "bot_conv" in key:
                  lowerCAmelCase  						=       key.replace("""bot_conv"""							,						"""0.convolution"""			)
            if "skip_conv1" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv1"""							,						"""1.convolution"""			)
            if "skip_conv2" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv2"""							,						"""2.convolution"""			)
            if "fusion1" in key:
                  lowerCAmelCase  						=       key.replace("""fusion1"""							,						"""1.fusion"""			)
            if "fusion2" in key:
                  lowerCAmelCase  						=       key.replace("""fusion2"""							,						"""2.fusion"""			)
            if "fusion3" in key:
                  lowerCAmelCase  						=       key.replace("""fusion3"""							,						"""3.fusion"""			)
            if "fusion" in key and "conv" in key:
                  lowerCAmelCase  						=       key.replace("""conv"""							,						"""convolutional_layer"""			)
            if key.startswith("""module.last_layer_depth"""			):
                  lowerCAmelCase  						=       key.replace("""module.last_layer_depth"""							,						"""head.head"""			)
            lowerCAmelCase  						=       value

      return new_state_dict


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      for i in range(config.num_encoder_blocks			):
            for j in range(config.depths[i]			):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'			)
                  # next, add keys and values (in that order) to the state dict
                  lowerCAmelCase  						=       kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase  						=       kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase  						=       kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase  						=       kv_bias[config.hidden_sizes[i] :]


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE			).raw			)

      return image


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=None			):







      '''simple docstring'''




      lowerCAmelCase  						=       GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]							,						decoder_hidden_size=64							,						depths=[3, 8, 27, 3]			)

      # load image processor (only resize + rescale)
      lowerCAmelCase  						=       GLPNImageProcessor()

      # prepare image
      lowerCAmelCase  						=       prepare_img()
      lowerCAmelCase  						=       image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			).pixel_values

      logger.info("""Converting model..."""			)

      # load original state dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=torch.device("""cpu"""			)			)

      # rename keys
      lowerCAmelCase  						=       rename_keys(SCREAMING_SNAKE_CASE			)

      # key and value matrices need special treatment
      read_in_k_v(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # create HuggingFace model and load state dict
      lowerCAmelCase  						=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

      # forward pass
      lowerCAmelCase  						=       model(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]			)
            elif "kitti" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]			)
            else:
                  raise ValueError(F'Unknown model name: {model_name}'			)

            lowerCAmelCase  						=       torch.Size([1, 4_80, 6_40]			)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]							,						SCREAMING_SNAKE_CASE							,						atol=1e-4			)
            print("""Looks ok!"""			)

      # finally, push to hub if required
      if push_to_hub:
            logger.info("""Pushing model and image processor to the hub..."""			)
            model.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add model"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)
            image_processor.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add image processor"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)


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

       parser.add_argument(
           "--checkpoint_path",
           default=None,
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
       )
       parser.add_argument(
           "--model_name",
           default="glpn-kitti",
           type=str,
           help="Name of the model in case you're pushing to the hub.",
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






