

"""simple docstring"""

import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import DonutSwinModel
       from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


class 			_snake_case     :







      def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=1_3     ,							UpperCAmelCase_=3_2     ,							UpperCAmelCase_=2     ,							UpperCAmelCase_=3     ,							UpperCAmelCase_=1_6     ,							UpperCAmelCase_=[1, 2, 1]     ,							UpperCAmelCase_=[2, 2, 4]     ,							UpperCAmelCase_=2     ,							UpperCAmelCase_=2.0     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=0.0     ,							UpperCAmelCase_=0.0     ,							UpperCAmelCase_=0.1     ,							UpperCAmelCase_="gelu"     ,							UpperCAmelCase_=False     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=0.0_2     ,							UpperCAmelCase_=1e-5     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=1_0     ,							UpperCAmelCase_=8     ,							)			->						int:
           __lowercase												=			parent
           __lowercase												=			batch_size
           __lowercase												=			image_size
           __lowercase												=			patch_size
           __lowercase												=			num_channels
           __lowercase												=			embed_dim
           __lowercase												=			depths
           __lowercase												=			num_heads
           __lowercase												=			window_size
           __lowercase												=			mlp_ratio
           __lowercase												=			qkv_bias
           __lowercase												=			hidden_dropout_prob
           __lowercase												=			attention_probs_dropout_prob
           __lowercase												=			drop_path_rate
           __lowercase												=			hidden_act
           __lowercase												=			use_absolute_embeddings
           __lowercase												=			patch_norm
           __lowercase												=			layer_norm_eps
           __lowercase												=			initializer_range
           __lowercase												=			is_training
           __lowercase												=			scope
           __lowercase												=			use_labels
           __lowercase												=			type_sequence_label_size
           __lowercase												=			encoder_stride







      def        _UpperCamelCase    (self							)			->						List[str]:
           __lowercase												=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

           __lowercase												=			None
           if self.use_labels:
                __lowercase												=			ids_tensor([self.batch_size]     ,							self.type_sequence_label_size							)

           __lowercase												=			self.get_config()

           return config, pixel_values, labels







      def        _UpperCamelCase    (self							)			->						str:
           return DonutSwinConfig(
               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     ,							)







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Union[str, Any]:
           __lowercase												=			DonutSwinModel(config=UpperCAmelCase_							)
           model.to(UpperCAmelCase_							)
           model.eval()
           __lowercase												=			model(UpperCAmelCase_							)

           __lowercase												=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
           __lowercase												=			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        _UpperCamelCase    (self							)			->						Any:
           __lowercase												=			self.prepare_config_and_inputs()
           (
               (
               __lowercase 
           )						, (
               __lowercase 
           )						, (
               __lowercase 
           )						,  
           )												=			config_and_inputs
           __lowercase												=			{'''pixel_values''': pixel_values}
           return config, inputs_dict



@require_torch
class 			_snake_case     (						UpperCamelCase						,			UpperCamelCase						,			unittest.TestCase       ):
      __snake_case 			=     (DonutSwinModel,) if is_torch_available() else ()
      __snake_case 			=     {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
      __snake_case 			=     True

      __snake_case 			=     False
      __snake_case 			=     False
      __snake_case 			=     False







      def        _UpperCamelCase    (self							)			->						Dict:
           __lowercase												=			DonutSwinModelTester(self							)
           __lowercase												=			ConfigTester(self     ,							config_class=UpperCAmelCase_     ,							embed_dim=3_7							)







      def        _UpperCamelCase    (self							)			->						int:
           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        _UpperCamelCase    (self							)			->						Any:
           return







      def        _UpperCamelCase    (self							)			->						Optional[Any]:
           __lowercase												=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_							)







      def        _UpperCamelCase    (self							)			->						str:
           # DonutSwin does not use inputs_embeds
           pass







      def        _UpperCamelCase    (self							)			->						str:
           __lowercase						, __lowercase												=			self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                __lowercase												=			model_class(UpperCAmelCase_							)
                self.assertIsInstance(model.get_input_embeddings()     ,							(nn.Module)							)
                __lowercase												=			model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(UpperCAmelCase_     ,							nn.Linear							)							)







      def        _UpperCamelCase    (self							)			->						List[str]:
           __lowercase						, __lowercase												=			self.model_tester.prepare_config_and_inputs_for_common()

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

                __lowercase												=			['''pixel_values''']
                self.assertListEqual(arg_names[:1]     ,							UpperCAmelCase_							)







      def        _UpperCamelCase    (self							)			->						Tuple:
           __lowercase						, __lowercase												=			self.model_tester.prepare_config_and_inputs_for_common()
           __lowercase												=			True

           for model_class in self.all_model_classes:
                __lowercase												=			True
                __lowercase												=			False
                __lowercase												=			True
                __lowercase												=			model_class(UpperCAmelCase_							)
                model.to(UpperCAmelCase_							)
                model.eval()
                with torch.no_grad():
                     __lowercase												=			model(**self._prepare_for_class(UpperCAmelCase_     ,							UpperCAmelCase_							)							)
                __lowercase												=			outputs.attentions
                __lowercase												=			len(self.model_tester.depths							)
                self.assertEqual(len(UpperCAmelCase_							)     ,							UpperCAmelCase_							)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                __lowercase												=			True
                __lowercase												=			config.window_size**2
                __lowercase												=			model_class(UpperCAmelCase_							)
                model.to(UpperCAmelCase_							)
                model.eval()
                with torch.no_grad():
                     __lowercase												=			model(**self._prepare_for_class(UpperCAmelCase_     ,							UpperCAmelCase_							)							)
                __lowercase												=			outputs.attentions
                self.assertEqual(len(UpperCAmelCase_							)     ,							UpperCAmelCase_							)

                self.assertListEqual(
                    list(attentions[0].shape[-3:]							)     ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,							)
                __lowercase												=			len(UpperCAmelCase_							)

                # Check attention is always last and order is fine
                __lowercase												=			True
                __lowercase												=			True
                __lowercase												=			model_class(UpperCAmelCase_							)
                model.to(UpperCAmelCase_							)
                model.eval()
                with torch.no_grad():
                     __lowercase												=			model(**self._prepare_for_class(UpperCAmelCase_     ,							UpperCAmelCase_							)							)

                if hasattr(self.model_tester     ,							'''num_hidden_states_types'''							):
                     __lowercase												=			self.model_tester.num_hidden_states_types
                else:
                     # also another +1 for reshaped_hidden_states
                     __lowercase												=			2
                self.assertEqual(out_len + added_hidden_states     ,							len(UpperCAmelCase_							)							)

                __lowercase												=			outputs.attentions

                self.assertEqual(len(UpperCAmelCase_							)     ,							UpperCAmelCase_							)

                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]							)     ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,							)







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Dict:
           __lowercase												=			model_class(UpperCAmelCase_							)
           model.to(UpperCAmelCase_							)
           model.eval()

           with torch.no_grad():
                __lowercase												=			model(**self._prepare_for_class(UpperCAmelCase_     ,							UpperCAmelCase_							)							)

           __lowercase												=			outputs.hidden_states

           __lowercase												=			getattr(
               self.model_tester     ,							'''expected_num_hidden_layers'''     ,							len(self.model_tester.depths							) + 1							)
           self.assertEqual(len(UpperCAmelCase_							)     ,							UpperCAmelCase_							)

           # DonutSwin has a different seq_length
           __lowercase												=			(
               config.patch_size
               if isinstance(config.patch_size     ,							collections.abc.Iterable							)
               else (config.patch_size, config.patch_size)
           )

           __lowercase												=			(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]     ,							)

           __lowercase												=			outputs.reshaped_hidden_states
           self.assertEqual(len(UpperCAmelCase_							)     ,							UpperCAmelCase_							)

           __lowercase						, __lowercase						, __lowercase						, __lowercase												=			reshaped_hidden_states[0].shape
           __lowercase												=			(
               reshaped_hidden_states[0].view(UpperCAmelCase_     ,							UpperCAmelCase_     ,							height * width							).permute(0     ,							2     ,							1							)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]							)     ,							[num_patches, self.model_tester.embed_dim]     ,							)







      def        _UpperCamelCase    (self							)			->						Dict:
           __lowercase						, __lowercase												=			self.model_tester.prepare_config_and_inputs_for_common()

           __lowercase												=			(
               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:
                __lowercase												=			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"]
                __lowercase												=			True

                self.check_hidden_states_output(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)







      def        _UpperCamelCase    (self							)			->						int:
           __lowercase						, __lowercase												=			self.model_tester.prepare_config_and_inputs_for_common()
           __lowercase												=			3

           __lowercase												=			(
               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)
           )
           __lowercase												=			(
               config.patch_size
               if isinstance(config.patch_size     ,							collections.abc.Iterable							)
               else (config.patch_size, config.patch_size)
           )

           __lowercase												=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __lowercase												=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
                __lowercase												=			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"]
                __lowercase												=			True
                self.check_hidden_states_output(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							(padded_height, padded_width)							)







      @slow
      def        _UpperCamelCase    (self							)			->						Optional[int]:
           for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowercase												=			DonutSwinModel.from_pretrained(UpperCAmelCase_							)
                self.assertIsNotNone(UpperCAmelCase_							)







      def        _UpperCamelCase    (self							)			->						Optional[Any]:
           __lowercase						, __lowercase												=			self.model_tester.prepare_config_and_inputs_for_common()

           __lowercase												=			_config_zero_init(UpperCAmelCase_							)
           for model_class in self.all_model_classes:
                __lowercase												=			model_class(config=UpperCAmelCase_							)
                for name, param in model.named_parameters():
                     if "embeddings" not in name and param.requires_grad:
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()     ,							[0.0, 1.0]     ,							msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,							)





"""simple docstring"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


