


'''simple docstring'''
import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met




class      lowerCamelCase_  (      __a ):






      def __init__(   self							:       Any		,		*_A							:       str		,		_A							:       str=None		,		_A							:       int=None		,		**_A							:       Optional[Any]					):


          '''simple docstring'''


          super().__init__(*__lowerCAmelCase		,		**__lowerCAmelCase					)
          UpperCAmelCase__     :						Optional[int]						=							eval_examples
          UpperCAmelCase__     :						Union[str, Any]						=							post_process_function






      def lowercase_				(   self							:       Union[str, Any]		,		_A							:       Optional[Dataset] = None		,		_A							:       int=None		,		_A							:       Optional[List[str]] = None		,		_A							:       str = "eval"		,		**_A							:       str		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							gen_kwargs.copy()
          UpperCAmelCase__     :						Union[str, Any]						=							(
              gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''					) is not None else self.args.generation_max_length
          )
          UpperCAmelCase__     :						Optional[int]						=							(
              gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''					) is not None else self.args.generation_num_beams
          )
          UpperCAmelCase__     :						int						=							gen_kwargs

          UpperCAmelCase__     :						int						=							self.eval_dataset if eval_dataset is None else eval_dataset
          UpperCAmelCase__     :						Optional[Any]						=							self.get_eval_dataloader(__lowerCAmelCase					)
          UpperCAmelCase__     :						Optional[int]						=							self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          UpperCAmelCase__     :						Optional[Any]						=							self.compute_metrics
          UpperCAmelCase__     :						str						=							None
          UpperCAmelCase__     :						Tuple						=							time.time()
          UpperCAmelCase__     :						Optional[Any]						=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
              UpperCAmelCase__     :						Tuple						=							eval_loop(
                  __lowerCAmelCase		,		description='''Evaluation'''		,		prediction_loss_only=True if compute_metrics is None else None		,		ignore_keys=__lowerCAmelCase		,		metric_key_prefix=__lowerCAmelCase		,		)
          finally:
              UpperCAmelCase__     :						int						=							compute_metrics
          UpperCAmelCase__     :						Optional[Any]						=							self.args.eval_batch_size * self.args.world_size
          if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
              start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
          output.metrics.update(
              speed_metrics(
                  __lowerCAmelCase		,		__lowerCAmelCase		,		num_samples=output.num_samples		,		num_steps=math.ceil(output.num_samples / total_batch_size					)		,		)					)

          if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
              # Only the main node write the results by default
              UpperCAmelCase__     :						List[str]						=							self.post_process_function(__lowerCAmelCase		,		__lowerCAmelCase		,		__lowerCAmelCase					)
              UpperCAmelCase__     :						List[Any]						=							self.compute_metrics(__lowerCAmelCase					)

              # Prefix all keys with metric_key_prefix + '_'
              for key in list(metrics.keys()					):
                  if not key.startswith(f"""{metric_key_prefix}_"""					):
                      UpperCAmelCase__     :						Optional[int]						=							metrics.pop(__lowerCAmelCase					)

              metrics.update(output.metrics					)
          else:
              UpperCAmelCase__     :						List[Any]						=							output.metrics

          if self.args.should_log:
              # Only the main node log the results by default
              self.log(__lowerCAmelCase					)

          if self.args.tpu_metrics_debug or self.args.debug:
              # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
              xm.master_print(met.metrics_report()					)

          UpperCAmelCase__     :						List[Any]						=							self.callback_handler.on_evaluate(self.args		,		self.state		,		self.control		,		__lowerCAmelCase					)
          return metrics






      def lowercase_				(   self							:       Optional[Any]		,		_A							:       List[str]		,		_A							:       Optional[Any]		,		_A							:       Optional[Any]=None		,		_A							:       str = "test"		,		**_A							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							gen_kwargs.copy()

          UpperCAmelCase__     :						Union[str, Any]						=							self.get_test_dataloader(__lowerCAmelCase					)

          # Temporarily disable metric computation, we will do it in the loop here.
          UpperCAmelCase__     :						Optional[int]						=							self.compute_metrics
          UpperCAmelCase__     :						Union[str, Any]						=							None
          UpperCAmelCase__     :						int						=							time.time()
          UpperCAmelCase__     :						List[str]						=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
              UpperCAmelCase__     :						Any						=							eval_loop(
                  __lowerCAmelCase		,		description='''Prediction'''		,		prediction_loss_only=True if compute_metrics is None else None		,		ignore_keys=__lowerCAmelCase		,		metric_key_prefix=__lowerCAmelCase		,		)
          finally:
              UpperCAmelCase__     :						Union[str, Any]						=							compute_metrics

          UpperCAmelCase__     :						Optional[Any]						=							self.args.eval_batch_size * self.args.world_size
          if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
              start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
          output.metrics.update(
              speed_metrics(
                  __lowerCAmelCase		,		__lowerCAmelCase		,		num_samples=output.num_samples		,		num_steps=math.ceil(output.num_samples / total_batch_size					)		,		)					)
          if self.post_process_function is None or self.compute_metrics is None:
              return output

          UpperCAmelCase__     :						Tuple						=							self.post_process_function(__lowerCAmelCase		,		__lowerCAmelCase		,		__lowerCAmelCase		,		'''predict'''					)
          UpperCAmelCase__     :						Any						=							self.compute_metrics(__lowerCAmelCase					)

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()					):
              if not key.startswith(f"""{metric_key_prefix}_"""					):
                  UpperCAmelCase__     :						Optional[Any]						=							metrics.pop(__lowerCAmelCase					)
          metrics.update(output.metrics					)
          return PredictionOutput(predictions=predictions.predictions		,		label_ids=predictions.label_ids		,		metrics=__lowerCAmelCase					)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

A       :   Dict					= {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       '''glpn'''






			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Union[str, Any]=3				,      __lowerCAmelCase  :       Any=4				,      __lowerCAmelCase  :       Tuple=[2, 2, 2, 2]				,      __lowerCAmelCase  :       int=[8, 4, 2, 1]				,      __lowerCAmelCase  :       int=[32, 64, 1_60, 2_56]				,      __lowerCAmelCase  :       str=[7, 3, 3, 3]				,      __lowerCAmelCase  :       Dict=[4, 2, 2, 2]				,      __lowerCAmelCase  :       List[str]=[1, 2, 5, 8]				,      __lowerCAmelCase  :       List[Any]=[4, 4, 4, 4]				,      __lowerCAmelCase  :       str="gelu"				,      __lowerCAmelCase  :       Union[str, Any]=0.0				,      __lowerCAmelCase  :       Tuple=0.0				,      __lowerCAmelCase  :       List[Any]=0.0_2				,      __lowerCAmelCase  :       Dict=0.1				,      __lowerCAmelCase  :       Dict=1e-6				,      __lowerCAmelCase  :       Tuple=64				,      __lowerCAmelCase  :       Tuple=10				,      __lowerCAmelCase  :       Union[str, Any]=-1				,      **__lowerCAmelCase  :       Any				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )

					A__     =  num_channels
					A__     =  num_encoder_blocks
					A__     =  depths
					A__     =  sr_ratios
					A__     =  hidden_sizes
					A__     =  patch_sizes
					A__     =  strides
					A__     =  mlp_ratios
					A__     =  num_attention_heads
					A__     =  hidden_act
					A__     =  hidden_dropout_prob
					A__     =  attention_probs_dropout_prob
					A__     =  initializer_range
					A__     =  drop_path_rate
					A__     =  layer_norm_eps
					A__     =  decoder_hidden_size
					A__     =  max_depth
					A__     =  head_in_index



