"""simple docstring"""


import copy

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


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__=16,							lowerCAmelCase__=4,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   int:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      image_size // patch_size
            snake_case_       =      mask_input_channels
            snake_case_       =      num_point_embeddings
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__="relu",							lowerCAmelCase__=2048,							lowerCAmelCase__=2,							lowerCAmelCase__=8,							lowerCAmelCase__=2,							lowerCAmelCase__=3,							lowerCAmelCase__=3,							lowerCAmelCase__=256,							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      hidden_act
            snake_case_       =      mlp_dim
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      attention_downsample_rate
            snake_case_       =      num_multimask_outputs
            snake_case_       =      iou_head_depth
            snake_case_       =      iou_head_hidden_dim
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=768,							lowerCAmelCase__=256,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-06,							lowerCAmelCase__=0.0,							lowerCAmelCase__=1e-10,							lowerCAmelCase__=True,							lowerCAmelCase__=4.0,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=14,							lowerCAmelCase__=[2, 5, 8, 11],							lowerCAmelCase__=128,							lowerCAmelCase__=None,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)

            snake_case_       =      hidden_size
            snake_case_       =      output_channels
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      num_channels
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps
            snake_case_       =      attention_dropout
            snake_case_       =      initializer_range
            snake_case_       =      qkv_bias
            snake_case_       =      mlp_ratio
            snake_case_       =      use_abs_pos
            snake_case_       =      use_rel_pos
            snake_case_       =      window_size
            snake_case_       =      global_attn_indexes
            snake_case_       =      num_pos_feats
            snake_case_       =      int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "sam"
       SCREAMING_SNAKE_CASE_				=   True







       def __init__(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=0.02,							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      vision_config if vision_config is not None else {}
            snake_case_       =      prompt_encoder_config if prompt_encoder_config is not None else {}
            snake_case_       =      mask_decoder_config if mask_decoder_config is not None else {}

            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      vision_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      prompt_encoder_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      mask_decoder_config.to_dict()

            snake_case_       =      SamVisionConfig(**lowerCAmelCase__)
            snake_case_       =      SamPromptEncoderConfig(**lowerCAmelCase__)
            snake_case_       =      SamMaskDecoderConfig(**lowerCAmelCase__)
            snake_case_       =      initializer_range







       def 	a_		(				self)   ->   Dict:
            snake_case_       =      copy.deepcopy(self.__dict__)
            snake_case_       =      self.vision_config.to_dict()
            snake_case_       =      self.prompt_encoder_config.to_dict()
            snake_case_       =      self.mask_decoder_config.to_dict()
            snake_case_       =      self.__class__.model_type
            return output


"""simple docstring"""


from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
    from .data.data_collator import DataCollator
    from .modeling_utils import PreTrainedModel
    from .tokenization_utils_base import PreTrainedTokenizerBase
    from .trainer_callback import TrainerCallback
    from .trainer_utils import EvalPrediction, PredictionOutput
    from .training_args import TrainingArguments


__UpperCamelCase								=    logging.get_logger(__name__)




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = (None, None),							lowerCAmelCase__ = None,							)   ->   Dict:
            super().__init__(
                model=lowerCAmelCase__,							args=lowerCAmelCase__,							data_collator=lowerCAmelCase__,							train_dataset=lowerCAmelCase__,							eval_dataset=lowerCAmelCase__,							tokenizer=lowerCAmelCase__,							model_init=lowerCAmelCase__,							compute_metrics=lowerCAmelCase__,							callbacks=lowerCAmelCase__,							optimizers=lowerCAmelCase__,							preprocess_logits_for_metrics=lowerCAmelCase__,							)

            # Override self.model.generation_config if a GenerationConfig is specified in args.
            # Priority: args.generation_config > model.generation_config > default GenerationConfig.
            if self.args.generation_config is not None:
                 snake_case_       =      self.load_generation_config(self.args.generation_config)
                 snake_case_       =      gen_config







       @staticmethod
       def 	a_		(				lowerCAmelCase__)   ->   GenerationConfig:

            # GenerationConfig provided, nothing to do
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 return deepcopy(lowerCAmelCase__)

            # str or Path
            snake_case_       =      Path(lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else gen_config_arg
            snake_case_       =      None

            # Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
            # This step is required in order to determine config_file_name
            if pretrained_model_name.is_file():
                 snake_case_       =      pretrained_model_name.name
                 snake_case_       =      pretrained_model_name.parent
            # dir path
            elif pretrained_model_name.is_dir():
                 pass
            # model id or URL
            else:
                 snake_case_       =      gen_config_arg

            snake_case_       =      GenerationConfig.from_pretrained(lowerCAmelCase__,							lowerCAmelCase__)
            return gen_config







       def 	a_		(				self,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = "eval",							**lowerCAmelCase__,							)   ->   Dict[str, float]:
            snake_case_       =      gen_kwargs.copy()
            if gen_kwargs.get('max_length') is None and gen_kwargs.get('max_new_tokens') is None:
                 snake_case_       =      self.args.generation_max_length
            snake_case_       =      (
                gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.args.generation_num_beams
            )
            snake_case_       =      gen_kwargs

            return super().evaluate(lowerCAmelCase__,							ignore_keys=lowerCAmelCase__,							metric_key_prefix=lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = "test",							**lowerCAmelCase__,							)   ->   "PredictionOutput":
            snake_case_       =      gen_kwargs.copy()
            if gen_kwargs.get('max_length') is None and gen_kwargs.get('max_new_tokens') is None:
                 snake_case_       =      self.args.generation_max_length
            snake_case_       =      (
                gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.args.generation_num_beams
            )
            snake_case_       =      gen_kwargs

            return super().predict(lowerCAmelCase__,							ignore_keys=lowerCAmelCase__,							metric_key_prefix=lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)   ->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

            if not self.args.predict_with_generate or prediction_loss_only:
                 return super().prediction_step(
                     lowerCAmelCase__,							lowerCAmelCase__,							prediction_loss_only=lowerCAmelCase__,							ignore_keys=lowerCAmelCase__)

            snake_case_       =      'labels' in inputs
            snake_case_       =      self._prepare_inputs(lowerCAmelCase__)

            # XXX: adapt synced_gpus for fairscale as well
            # Priority (handled in generate):
            # gen_kwargs > model.generation_config > default GenerationConfig()

            if len(lowerCAmelCase__) == 0 and hasattr(self,							'_gen_kwargs'):
                 snake_case_       =      self._gen_kwargs.copy()

            if gen_kwargs.get('max_length') is None and gen_kwargs.get('max_new_tokens') is None:
                 snake_case_       =      self.model.config.max_length
            snake_case_       =      (
                gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.model.config.num_beams
            )
            snake_case_       =      True if is_deepspeed_zeroa_enabled() else False
            snake_case_       =      (
                gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus') is not None else default_synced_gpus
            )

            # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
            # (otherwise, it would continue generating from the padded `decoder_input_ids`)
            if (
                "labels" in inputs
                and "decoder_input_ids" in inputs
                and inputs["labels"].shape == inputs["decoder_input_ids"].shape
            ):
                 snake_case_       =      {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
            snake_case_       =      self.model.generate(**lowerCAmelCase__,							**lowerCAmelCase__)

            # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
            # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
            # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
            if self.model.generation_config._from_model_config:
                 snake_case_       =      False

            # Retrieves GenerationConfig from model.generation_config
            snake_case_       =      self.model.generation_config
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_config.max_length:
                 snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_length)
            elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
                 snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_new_tokens + 1)

            with torch.no_grad():
                 if has_labels:
                      with self.compute_loss_context_manager():
                           snake_case_       =      model(**lowerCAmelCase__)
                      if self.label_smoother is not None:
                           snake_case_       =      self.label_smoother(lowerCAmelCase__,							inputs['labels']).mean().detach()
                      else:
                           snake_case_       =      (outputs['loss'] if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else outputs[0]).mean().detach()
                 else:
                      snake_case_       =      None

            if self.args.prediction_loss_only:
                 return loss, None, None

            if has_labels:
                 snake_case_       =      inputs['labels']
                 if labels.shape[-1] < gen_config.max_length:
                      snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_length)
                 elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                      snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_new_tokens + 1)
            else:
                 snake_case_       =      None

            return loss, generated_tokens, labels







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Any:
            if self.tokenizer is not None and hasattr(self.tokenizer,							'pad_token_id'):
                 # If PAD token is not defined at least EOS token has to be defined
                 snake_case_       =      (
                     self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
                 )
            else:
                 if self.model.config.pad_token_id is not None:
                      snake_case_       =      self.model.config.pad_token_id
                 else:
                      raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors')

            snake_case_       =      pad_token_id * torch.ones(
                (tensor.shape[0], max_length),							dtype=tensor.dtype,							device=tensor.device)
            snake_case_       =      tensor
            return padded_tensor

