


import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MaskFormerSwinBackbone
		from transformers.models.maskformer import MaskFormerSwinModel



class 						_UpperCAmelCase      :






		def __init__(						self				:     str   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     List[str]=13   ,  UpperCAmelCase				:     List[str]=32   ,  UpperCAmelCase				:     Tuple=2   ,  UpperCAmelCase				:     Optional[Any]=3   ,  UpperCAmelCase				:     List[str]=16   ,  UpperCAmelCase				:     int=[1, 2, 1]   ,  UpperCAmelCase				:     Any=[2, 2, 4]   ,  UpperCAmelCase				:     Any=2   ,  UpperCAmelCase				:     List[Any]=2.0   ,  UpperCAmelCase				:     Dict=True   ,  UpperCAmelCase				:     str=0.0   ,  UpperCAmelCase				:     str=0.0   ,  UpperCAmelCase				:     Optional[Any]=0.1   ,  UpperCAmelCase				:     List[str]="gelu"   ,  UpperCAmelCase				:     Optional[Any]=False   ,  UpperCAmelCase				:     Optional[int]=True   ,  UpperCAmelCase				:     Union[str, Any]=0.02   ,  UpperCAmelCase				:     str=1E-5   ,  UpperCAmelCase				:     str=True   ,  UpperCAmelCase				:     List[str]=None   ,  UpperCAmelCase				:     Any=True   ,  UpperCAmelCase				:     Optional[int]=10   ,  UpperCAmelCase				:     Union[str, Any]=8   ,  UpperCAmelCase				:     Optional[int]=["stage1", "stage2", "stage3"]   ,  UpperCAmelCase				:     Any=[1, 2, 3]   ,  ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						parent
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						batch_size
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						image_size
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						patch_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						num_channels
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						embed_dim
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						depths
				SCREAMING_SNAKE_CASE_				:List[str]				     =						num_heads
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						window_size
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						mlp_ratio
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						qkv_bias
				SCREAMING_SNAKE_CASE_				:List[str]				     =						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						drop_path_rate
				SCREAMING_SNAKE_CASE_				:Dict				     =						hidden_act
				SCREAMING_SNAKE_CASE_				:List[str]				     =						use_absolute_embeddings
				SCREAMING_SNAKE_CASE_				:Any				     =						patch_norm
				SCREAMING_SNAKE_CASE_				:Tuple				     =						layer_norm_eps
				SCREAMING_SNAKE_CASE_				:List[str]				     =						initializer_range
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						is_training
				SCREAMING_SNAKE_CASE_				:List[str]				     =						scope
				SCREAMING_SNAKE_CASE_				:Tuple				     =						use_labels
				SCREAMING_SNAKE_CASE_				:List[str]				     =						type_sequence_label_size
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						encoder_stride
				SCREAMING_SNAKE_CASE_				:int				     =						out_features
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						out_indices






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

				SCREAMING_SNAKE_CASE_				:int				     =						None
				if self.use_labels:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						ids_tensor([self.batch_size]   ,  self.type_sequence_label_size)

				SCREAMING_SNAKE_CASE_				:Any				     =						self.get_config()

				return config, pixel_values, labels






		def _snake_case		(						self				:     List[str]):
				return MaskFormerSwinConfig(
				    image_size=self.image_size   ,  patch_size=self.patch_size   ,  num_channels=self.num_channels   ,  embed_dim=self.embed_dim   ,  depths=self.depths   ,  num_heads=self.num_heads   ,  window_size=self.window_size   ,  mlp_ratio=self.mlp_ratio   ,  qkv_bias=self.qkv_bias   ,  hidden_dropout_prob=self.hidden_dropout_prob   ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,  drop_path_rate=self.drop_path_rate   ,  hidden_act=self.hidden_act   ,  use_absolute_embeddings=self.use_absolute_embeddings   ,  path_norm=self.patch_norm   ,  layer_norm_eps=self.layer_norm_eps   ,  initializer_range=self.initializer_range   ,  encoder_stride=self.encoder_stride   ,  out_features=self.out_features   ,  out_indices=self.out_indices   ,  )






		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						MaskFormerSwinModel(config=UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
				SCREAMING_SNAKE_CASE_				:Dict				     =						int(config.embed_dim * 2 ** (len(config.depths) - 1))

				self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, expected_seq_len, expected_dim))






		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						MaskFormerSwinBackbone(config=UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:int				     =						model(UpperCAmelCase)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps)   ,  len(config.out_features))
				self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,  [13, 16, 16, 16])

				# verify channels
				self.parent.assertEqual(len(model.channels)   ,  len(config.out_features))
				self.parent.assertListEqual(model.channels   ,  [16, 32, 64])

				# verify ValueError
				with self.parent.assertRaises(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						["stem"]
						SCREAMING_SNAKE_CASE_				:Any				     =						MaskFormerSwinBackbone(config=UpperCAmelCase)







		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:int				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						config_and_inputs
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class 						_UpperCAmelCase      (     lowercase		,		lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			List[str]       			=  (
		    (
		        MaskFormerSwinModel,
		        MaskFormerSwinBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		lowerCamelCase_	:			Union[str, Any]       			=  {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
		lowerCamelCase_	:			Optional[Any]       			=  False
		lowerCamelCase_	:			str       			=  False
		lowerCamelCase_	:			Optional[Any]       			=  False
		lowerCamelCase_	:			Any       			=  False
		lowerCamelCase_	:			Tuple       			=  False






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						MaskFormerSwinModelTester(self)
				SCREAMING_SNAKE_CASE_				:str				     =						ConfigTester(self   ,  config_class=UpperCAmelCase   ,  embed_dim=37)






		@require_torch_multi_gpu
		@unittest.skip(
		    reason=(
		        "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
		        " `nn.DataParallel`"
		    ))
		def _snake_case		(						self				:     Any):
				pass






		def _snake_case		(						self				:     Tuple):
				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()






		def _snake_case		(						self				:     Optional[int]):
				return






		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase)






		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*UpperCAmelCase)






		@unittest.skip("Swin does not use inputs_embeds")
		def _snake_case		(						self				:     Optional[int]):
				pass






		@unittest.skip("Swin does not support feedforward chunking")
		def _snake_case		(						self				:     str):
				pass






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						model_class(UpperCAmelCase)
						self.assertIsInstance(model.get_input_embeddings()   ,  (nn.Module))
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(UpperCAmelCase   ,  nn.Linear))






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model_tester.prepare_config_and_inputs_for_common()

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

						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						["pixel_values"]
						self.assertListEqual(arg_names[:1]   ,  UpperCAmelCase)






		@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions")
		def _snake_case		(						self				:     Tuple):
				pass






		@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone")
		def _snake_case		(						self				:     Tuple):
				pass






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_class(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()

				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						model(**self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase))

				SCREAMING_SNAKE_CASE_				:str				     =						outputs.hidden_states

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						getattr(
				    self.model_tester   ,  "expected_num_hidden_layers"   ,  len(self.model_tester.depths) + 1)
				self.assertEqual(len(UpperCAmelCase)   ,  UpperCAmelCase)

				# Swin has a different seq_length
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						(
				    config.patch_size
				    if isinstance(config.patch_size   ,  collections.abc.Iterable)
				    else (config.patch_size, config.patch_size)
				)

				SCREAMING_SNAKE_CASE_				:List[str]				     =						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:])   ,  [num_patches, self.model_tester.embed_dim]   ,  )






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Dict				     =						self.model_tester.prepare_config_and_inputs_for_common()

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						(
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size   ,  collections.abc.Iterable)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Dict				     =						True
						self.check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						SCREAMING_SNAKE_CASE_				:int				     =						True

						self.check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE_				:Dict				     =						3

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size   ,  collections.abc.Iterable)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						(
				    config.patch_size
				    if isinstance(config.patch_size   ,  collections.abc.Iterable)
				    else (config.patch_size, config.patch_size)
				)

				SCREAMING_SNAKE_CASE_				:int				     =						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						True
						self.check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  (padded_height, padded_width))

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						SCREAMING_SNAKE_CASE_				:int				     =						True
						self.check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  (padded_height, padded_width))






		@unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints")
		def _snake_case		(						self				:     Optional[int]):
				pass






		@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
		def _snake_case		(						self				:     Dict):
				pass






		@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
		def _snake_case		(						self				:     List[Any]):
				pass







		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model_tester.prepare_config_and_inputs_for_common()

				def set_nan_tensor_to_zero(UpperCAmelCase				:     Dict):
						SCREAMING_SNAKE_CASE_				:Dict				     =						0
						return t

				def check_equivalence(UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     List[Any]={}):
						with torch.no_grad():
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(**UpperCAmelCase   ,  return_dict=UpperCAmelCase   ,  **UpperCAmelCase)
								SCREAMING_SNAKE_CASE_				:List[str]				     =						model(**UpperCAmelCase   ,  return_dict=UpperCAmelCase   ,  **UpperCAmelCase).to_tuple()

								def recursive_check(UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     str):
										if isinstance(UpperCAmelCase   ,  (List, Tuple)):
												for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase   ,  UpperCAmelCase):
														recursive_check(UpperCAmelCase   ,  UpperCAmelCase)
										elif isinstance(UpperCAmelCase   ,  UpperCAmelCase):
												for tuple_iterable_value, dict_iterable_value in zip(
												    tuple_object.values()   ,  dict_object.values()):
														recursive_check(UpperCAmelCase   ,  UpperCAmelCase)
										elif tuple_object is None:
												return
										else:
												self.assertTrue(
												    torch.allclose(
												        set_nan_tensor_to_zero(UpperCAmelCase)   ,  set_nan_tensor_to_zero(UpperCAmelCase)   ,  atol=1E-5)   ,  msg=(
												        "Tuple and dict output are not equal. Difference:"
												        F" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
												        F" {torch.isnan(UpperCAmelCase).any()} and `inf`: {torch.isinf(UpperCAmelCase)}. Dict has"
												        F" `nan`: {torch.isnan(UpperCAmelCase).any()} and `inf`: {torch.isinf(UpperCAmelCase)}."
												    )   ,  )

								recursive_check(UpperCAmelCase   ,  UpperCAmelCase)

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						model_class(UpperCAmelCase)
						model.to(UpperCAmelCase)
						model.eval()

						SCREAMING_SNAKE_CASE_				:str				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Dict				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase)
						check_equivalence(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)

						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase   ,  return_labels=UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Any				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase   ,  return_labels=UpperCAmelCase)
						check_equivalence(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)

						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase)
						check_equivalence(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  {"output_hidden_states": True})

						SCREAMING_SNAKE_CASE_				:Any				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase   ,  return_labels=UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:int				     =						self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase   ,  return_labels=UpperCAmelCase)
						check_equivalence(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  {"output_hidden_states": True})




@require_torch
class 						_UpperCAmelCase      (     unittest.TestCase		,		lowercase							):
		lowerCamelCase_	:			Union[str, Any]       			=  (MaskFormerSwinBackbone,) if is_torch_available() else ()
		lowerCamelCase_	:			Optional[int]       			=  MaskFormerSwinConfig






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						MaskFormerSwinModelTester(self)







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE_				:int				     =						inputs_dict["pixel_values"].shape[0]

				for backbone_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						backbone_class(UpperCAmelCase)
						backbone.to(UpperCAmelCase)
						backbone.eval()

						SCREAMING_SNAKE_CASE_				:List[Any]				     =						backbone(**UpperCAmelCase)

						# Test default outputs and verify feature maps
						self.assertIsInstance(outputs.feature_maps   ,  UpperCAmelCase)
						self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
						for feature_map, n_channels in zip(outputs.feature_maps   ,  backbone.channels):
								self.assertTrue(feature_map.shape[:2]   ,  (batch_size, n_channels))
						self.assertIsNone(outputs.hidden_states)
						self.assertIsNone(outputs.attentions)

						# Test output_hidden_states=True
						SCREAMING_SNAKE_CASE_				:Dict				     =						backbone(**UpperCAmelCase   ,  output_hidden_states=UpperCAmelCase)
						self.assertIsNotNone(outputs.hidden_states)
						self.assertTrue(len(outputs.hidden_states)   ,  len(backbone.stage_names))
						# We skip the stem layer
						for hidden_states, n_channels in zip(outputs.hidden_states[1:]   ,  backbone.channels):
								for hidden_state in hidden_states:
										# Hidden states are in the format (batch_size, (height * width), n_channels)
										SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Any				     =						hidden_state.shape
										self.assertTrue((h_batch_size, h_n_channels)   ,  (batch_size, n_channels))

            # Test output_attentions=True
						if self.has_attentions:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						backbone(**UpperCAmelCase   ,  output_attentions=UpperCAmelCase)
								self.assertIsNotNone(outputs.attentions)










import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


SCREAMING_SNAKE_CASE__     					=			get_logger(__name__)


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		os.makedirs(a     , exist_ok=a )
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						SCREAMING_SNAKE_CASE_				:str				     =						os.path.join(a     , a )
						if accelerator.process_index == 0:
								logger.info(F"Saving model to {output_model_file}" )
								torch.save(a     , a )
								logger.info(F"Model saved to {output_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						(
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , a )
						logger.info(F"Saving model to {output_model_file}" )
						torch.save(a     , a )
						logger.info(F"Model saved to {output_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , F"{MODEL_NAME}_{model_index}" )
						os.makedirs(a     , exist_ok=a )
						logger.info(F"Saving model to {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:int				     =						{"model": state_dict}

						dist_cp.save_state_dict(
						    state_dict=a     , storage_writer=dist_cp.FileSystemWriter(a )     , planner=DefaultSavePlanner()     , )
						logger.info(F"Model saved to {ckpt_dir}" )


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(a ) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
										    "initializing FSDP object" )
								return
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:int				     =						(
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						(
						    os.path.join(a     , F"{MODEL_NAME}_{model_index}" )
						    if F"{MODEL_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading model from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Any				     =						{"model": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=a     , storage_reader=dist_cp.FileSystemReader(a )     , planner=DefaultLoadPlanner()     , )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						state_dict["model"]
						logger.info(F"Model loaded from {ckpt_dir}" )
				model.load_state_dict(a )


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		os.makedirs(a     , exist_ok=a )
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				SCREAMING_SNAKE_CASE_				:str				     =						FSDP.optim_state_dict(a     , a )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								SCREAMING_SNAKE_CASE_				:List[str]				     =						(
								    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
								)
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , a )
								logger.info(F"Saving Optimizer state to {output_optimizer_file}" )
								torch.save(a     , a )
								logger.info(F"Optimizer state saved in {output_optimizer_file}" )
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						os.path.join(a     , F"{OPTIMIZER_NAME}_{optimizer_index}" )
						os.makedirs(a     , exist_ok=a )
						logger.info(F"Saving Optimizer state to {ckpt_dir}" )
						dist_cp.save_state_dict(
						    state_dict={"optimizer": optim_state}     , storage_writer=dist_cp.FileSystemWriter(a )     , planner=DefaultSavePlanner()     , )
						logger.info(F"Optimizer state saved in {ckpt_dir}" )


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						(
						    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , a )
						logger.info(F"Loading Optimizer state from {input_optimizer_file}" )
						SCREAMING_SNAKE_CASE_				:str				     =						torch.load(a )
						logger.info(F"Optimizer state loaded from {input_optimizer_file}" )
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						(
						    os.path.join(a     , F"{OPTIMIZER_NAME}_{optimizer_index}" )
						    if F"{OPTIMIZER_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading Optimizer from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()     , optimizer_key="optimizer"     , storage_reader=dist_cp.FileSystemReader(a )     , )
						SCREAMING_SNAKE_CASE_				:List[str]				     =						optim_state["optimizer"]
						logger.info(F"Optimizer loaded from {ckpt_dir}" )
				SCREAMING_SNAKE_CASE_				:int				     =						FSDP.optim_state_dict_to_load(a     , a     , a )
				optimizer.load_state_dict(a )






