




import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=get_logger(__name__)

def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				List[dict]   , lowerCamelCase:				list   , lowerCamelCase:				list   , lowerCamelCase:				int   , lowerCamelCase:				GradScaler = None   , )    ->	Any:


      '''simple docstring'''




      for i, state in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Model weights saved in {output_model_file}"""  )
      # Optimizer states
      for i, opt in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = opt.state_dict()
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Optimizer state saved in {output_optimizer_file}"""  )
      # Scheduler states
      for i, scheduler in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = scheduler.state_dict()
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Scheduler state saved in {output_scheduler_file}"""  )
      # GradScaler state
      if scaler is not None:
            lowerCamelCase__					     = scaler.state_dict()
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            torch.save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""  )
      # Random number generator states
      lowerCamelCase__					     = {}
      lowerCamelCase__					     = f"""{RNG_STATE_NAME}_{process_index}.pkl"""
      lowerCamelCase__					     = random.getstate()
      lowerCamelCase__					     = np.random.get_state()
      lowerCamelCase__					     = torch.get_rng_state()
      if is_xpu_available():
            lowerCamelCase__					     = torch.xpu.get_rng_state_all()
      else:
            lowerCamelCase__					     = torch.cuda.get_rng_state_all()
      if is_tpu_available():
            lowerCamelCase__					     = xm.get_rng_state()
      lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
      torch.save(lowerCamelCase   , lowerCamelCase  )
      logger.info(f"""Random states saved in {output_states_file}"""  )
      return output_dir

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Dict   , lowerCamelCase:				Union[str, Any]   , lowerCamelCase:				Any   , lowerCamelCase:				Any   , lowerCamelCase:				Any=None   , lowerCamelCase:				Dict=None   , **lowerCamelCase:				Optional[Any]   , )    ->	Any:


      '''simple docstring'''




      if map_location not in [None, "cpu", "on_device"]:
            raise TypeError(
                'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'  )
      if map_location is None:
            lowerCamelCase__					     = 'cpu'
      elif map_location == "on_device":
            lowerCamelCase__					     = PartialState().device
      # Model states
      for i, model in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            models[i].load_state_dict(torch.load(lowerCamelCase   , map_location=lowerCamelCase  )   , **lowerCamelCase  )
      logger.info('All model weights loaded successfully'  )

      # Optimizer states
      for i, opt in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            lowerCamelCase__					     = torch.load(lowerCamelCase   , map_location=lowerCamelCase  )
            optimizers[i].load_state_dict(lowerCamelCase  )
      logger.info('All optimizer states loaded successfully'  )

      # Scheduler states
      for i, scheduler in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            scheduler.load_state_dict(torch.load(lowerCamelCase  )  )
      logger.info('All scheduler states loaded successfully'  )

      # GradScaler state
      if scaler is not None:
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            scaler.load_state_dict(torch.load(lowerCamelCase  )  )
            logger.info('GradScaler state loaded successfully'  )

      # Random states
      try:
            lowerCamelCase__					     = torch.load(os.path.join(lowerCamelCase   , f"""{RNG_STATE_NAME}_{process_index}.pkl"""  )  )
            random.setstate(states['random_state']  )
            np.random.set_state(states['numpy_random_seed']  )
            torch.set_rng_state(states['torch_manual_seed']  )
            if is_xpu_available():
                  torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']  )
            else:
                  torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']  )
            if is_tpu_available():
                  xm.set_rng_state(states['xm_seed']  )
            logger.info('All random states loaded successfully'  )
      except Exception:
            logger.info('Could not load random states'  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict   , lowerCamelCase:				Any   , lowerCamelCase:				int = 0  )    ->	Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__					     = Path(lowerCamelCase  ) / f"""custom_checkpoint_{index}.pkl"""
      logger.info(f"""Saving the state of {get_pretty_name(lowerCamelCase  )} to {save_location}"""  )
      torch.save(obj.state_dict()   , lowerCamelCase  )


def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				int = 0  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = f"""{path}/custom_checkpoint_{index}.pkl"""
      logger.info(f"""Loading the state of {get_pretty_name(lowerCamelCase  )} from {load_location}"""  )
      obj.load_state_dict(torch.load(lowerCamelCase   , map_location='cpu'  )  )





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import 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


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.config_class(**self.inputs_dict	)
           self.parent.assertTrue(hasattr(A__		,     'hidden_sizes'	)	)
           self.parent.assertTrue(hasattr(A__		,     'num_encoder_blocks'	)	)






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int=1_3		,     A__     :      Optional[Any]=6_4		,     A__     :      List[Any]=3		,     A__     :      Dict=4		,     A__     :      int=[2, 2, 2, 2]		,     A__     :      int=[8, 4, 2, 1]		,     A__     :      Any=[1_6, 3_2, 6_4, 1_2_8]		,     A__     :      List[str]=[1, 4, 8, 1_6]		,     A__     :      List[str]=False		,     A__     :      Optional[int]=True		,     A__     :      Any="gelu"		,     A__     :      List[str]=0.1		,     A__     :      Union[str, Any]=0.02		,     A__     :      Any=3		,     A__     :      Dict=None		,     ) ->				List[str]:
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = num_encoder_blocks
           lowerCamelCase__					     = sr_ratios
           lowerCamelCase__					     = depths
           lowerCamelCase__					     = hidden_sizes
           lowerCamelCase__					     = downsampling_rates
           lowerCamelCase__					     = is_training
           lowerCamelCase__					     = use_labels
           lowerCamelCase__					     = hidden_act
           lowerCamelCase__					     = hidden_dropout_prob
           lowerCamelCase__					     = initializer_range
           lowerCamelCase__					     = num_labels
           lowerCamelCase__					     = scope




     def        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase__					     = None
           if self.use_labels:
                 lowerCamelCase__					     = ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels	)

           lowerCamelCase__					     = PoolFormerConfig(
               image_size=self.image_size		,     num_channels=self.num_channels		,     num_encoder_blocks=self.num_encoder_blocks		,     depths=self.depths		,     hidden_sizes=self.hidden_sizes		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     initializer_range=self.initializer_range		,     )

           return config, pixel_values, labels




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Optional[Any]	) ->				str:
           lowerCamelCase__					     = PoolFormerModel(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)
           lowerCamelCase__					     = lowerCamelCase__					     = self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)




     def        __snake_case      (			self     :      List[str]	) ->				Any:
           lowerCamelCase__					     = self.prepare_config_and_inputs()
           lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = config_and_inputs
           lowerCamelCase__					     = {'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Dict   =(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _snake_case:		Dict   =(
         {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _snake_case:		str   =False
     _snake_case:		Tuple   =False
     _snake_case:		Optional[int]   =False
     _snake_case:		Dict   =False
     _snake_case:		Tuple   =False




     def        __snake_case      (			self     :      List[Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = PoolFormerModelTester(self	)
           lowerCamelCase__					     = PoolFormerConfigTester(self		,     config_class=A__	)




     def        __snake_case      (			self     :      str	) ->				List[str]:
           self.config_tester.run_common_tests()




     def        __snake_case      (			self     :      int	) ->				List[str]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__	)




     @unittest.skip('PoolFormer does not use inputs_embeds'	)
     def        __snake_case      (			self     :      Dict	) ->				Tuple:
           pass




     @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'	)
     def        __snake_case      (			self     :      List[str]	) ->				str:
           pass




     def        __snake_case      (			self     :      Dict	) ->				Optional[int]:
           def check_hidden_states_output(A__     :      str		,     A__     :      List[Any]		,     A__     :      List[Any]	):
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase__					     = model(**self._prepare_for_class(A__		,     A__	)	)

                 lowerCamelCase__					     = outputs.hidden_states

                 lowerCamelCase__					     = self.model_tester.num_encoder_blocks
                 self.assertEqual(len(A__	)		,     A__	)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:]	)		,     [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]		,     )

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = True
                 check_hidden_states_output(A__		,     A__		,     A__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase__					     = True

                 check_hidden_states_output(A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      Any	) ->				Tuple:
           if not self.model_tester.is_training:
                 return

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__					     = True

           for model_class in self.all_model_classes:
                 if model_class in get_values(A__	):
                       continue
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.train()
                 lowerCamelCase__					     = self._prepare_for_class(A__		,     A__		,     return_labels=A__	)
                 lowerCamelCase__					     = model(**A__	).loss
                 loss.backward()




     def        __snake_case      (			self     :      Tuple	) ->				Any:
           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCamelCase__					     = ['pixel_values']
                 self.assertListEqual(arg_names[:1]		,     A__	)




     @slow
     def        __snake_case      (			self     :      Dict	) ->				int:
           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__					     = PoolFormerModel.from_pretrained(A__	)
                 self.assertIsNotNone(A__	)

def 	lowerCAmelCase_	(     )    ->	Dict:


      '''simple docstring'''




      lowerCamelCase__					     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image






@require_torch
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @slow
     def        __snake_case      (			self     :      Any	) ->				Dict:
           lowerCamelCase__					     = PoolFormerImageProcessor()
           lowerCamelCase__					     = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'	).to(A__	)

           lowerCamelCase__					     = image_processor(images=prepare_img()		,     return_tensors='pt'	).to(A__	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__					     = model(**A__	)

           # verify the logits
           lowerCamelCase__					     = torch.Size((1, 1_0_0_0)	)
           self.assertEqual(outputs.logits.shape		,     A__	)

           lowerCamelCase__					     = torch.tensor([-0.6113, 0.1685, -0.0492]	).to(A__	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     A__		,     atol=1E-4	)	)