

"""simple docstring"""

import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_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 SwinaSRForImageSuperResolution, SwinaSRModel
							from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor


class       _snake_case  :







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=1_3  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=1  ,				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_=False  ,				UpperCAmelCase_=2  ,				)      ->    Any:
											__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        =      upscale

											# here we set some attributes to make tests pass
											__lowercase        =      len(UpperCAmelCase_				)
											__lowercase        =      embed_dim
											__lowercase        =      (image_size // patch_size) ** 2







						def 							_UpperCamelCase     (self				)      ->    int:
											__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				)      ->    Dict:
											return SwinaSRConfig(
											    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  ,				upscale=self.upscale  ,				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      SwinaSRModel(config=UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()
											__lowercase        =      model(UpperCAmelCase_				)

											self.parent.assertEqual(
											    result.last_hidden_state.shape  ,				(self.batch_size, self.embed_dim, self.image_size, self.image_size)				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Optional[int]:
											__lowercase        =      SwinaSRForImageSuperResolution(UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()
											__lowercase        =      model(UpperCAmelCase_				)

											__lowercase        =      self.image_size * self.upscale
											self.parent.assertEqual(
											    result.reconstruction.shape  ,				(self.batch_size, self.num_channels, expected_image_size, expected_image_size)				)







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__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			      =  (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
						__snake_case			      =  {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False







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







						def 							_UpperCamelCase     (self				)      ->    Dict:
											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				)      ->    Tuple:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_super_resolution(*UpperCAmelCase_				)







						@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''				)
						def 							_UpperCamelCase     (self				)      ->    int:
											pass







						@unittest.skip(reason='''Swin2SR does not use inputs_embeds'''				)
						def 							_UpperCamelCase     (self				)      ->    Tuple:
											pass







						@unittest.skip(reason='''Swin2SR does not support training yet'''				)
						def 							_UpperCamelCase     (self				)      ->    Dict:
											pass







						@unittest.skip(reason='''Swin2SR does not support training yet'''				)
						def 							_UpperCamelCase     (self				)      ->    List[str]:
											pass







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__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				)      ->    Optional[int]:
											__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_				)







						@slow
						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowercase        =      SwinaSRModel.from_pretrained(UpperCAmelCase_				)
																self.assertIsNotNone(UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											__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 "logit_scale" in name:
																										continue
																					if 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'''  ,				)







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__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_				)				)

																self.assertEqual(out_len + 1  ,				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]  ,				)



@require_vision
@require_torch
@slow
class       _snake_case  (    unittest.TestCase							):







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      SwinaSRImageProcessor()
											__lowercase        =      SwinaSRForImageSuperResolution.from_pretrained('''caidas/swin2SR-classical-sr-x2-64'''				).to(UpperCAmelCase_				)

											__lowercase        =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
											__lowercase        =      processor(images=UpperCAmelCase_  ,				return_tensors='''pt'''				).to(UpperCAmelCase_				)

											# forward pass
											with torch.no_grad():
																__lowercase        =      model(**UpperCAmelCase_				)

											# verify the logits
											__lowercase        =      torch.Size([1, 3, 9_7_6, 1_2_9_6]				)
											self.assertEqual(outputs.reconstruction.shape  ,				UpperCAmelCase_				)
											__lowercase        =      torch.tensor(
											    [[0.5_4_5_8, 0.5_5_4_6, 0.5_6_3_8], [0.5_5_2_6, 0.5_5_6_5, 0.5_6_5_1], [0.5_3_9_6, 0.5_4_2_6, 0.5_6_2_1]]				).to(UpperCAmelCase_				)
											self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,				UpperCAmelCase_  ,				atol=1e-4				)				)





"""simple docstring"""

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			int			,						a__:			Dict=0      )					->      Any:




					"""simple docstring"""



					if name is None:
										__lowercase        =      None
					else:
										__lowercase        =      '''.''' * max(0			,						spaces - 2      ) + '''# {:''' + str(50 - spaces      ) + '''s}'''
										__lowercase        =      fmt.format(a__      )

					# Print and recurse (if needed).
					if isinstance(a__			,						a__      ):
										if msg is not None:
															print(a__      )
										for k in val.keys():
															recursive_print(a__			,						val[k]			,						spaces + 2      )
					elif isinstance(a__			,						torch.Tensor      ):
										print(a__			,						''':'''			,						val.size()      )
					else:
										print(a__			,						''':'''			,						a__      )





def 					__UpperCamelCase (     a__:			Optional[int]			,						a__:			Tuple			,						a__:			List[Any]			,						a__:			List[str]      )					->      str:




					"""simple docstring"""



					__lowercase        =      param.size()
					# other versions store [num_heads * num_splits * hidden_size, :]
					__lowercase        =      (num_heads, num_splits, hidden_size) + input_shape[1:]
					__lowercase        =      param.view(*a__      )
					__lowercase        =      param.transpose(0			,						1      ).contiguous()
					__lowercase        =      param.view(*a__      )
					return param





def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Optional[int]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      config.n_positions
					__lowercase        =      config.n_layer
					__lowercase        =      config.vocab_size
					__lowercase        =      config.n_head
					__lowercase        =      config.n_embd // config.n_head

					__lowercase        =      sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
					__lowercase        =      {
					    '''transformer.wte.weight''': word_embeddings,
					    '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
					    '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
					    '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
					}

					__lowercase        =      '''model.language_model.encoder.layers.'''
					for i in range(a__      ):
										__lowercase        =      torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool      )      )
										__lowercase        =      causal_mask.view(1			,						1			,						a__			,						a__      )
										__lowercase        =      causal_mask
										__lowercase        =      torch.tensor(-1e4			,						dtype=torch.bfloataa      )

										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala.transpose(0			,						1      ).contiguous()

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
										    0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
										    0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

					# For LM head, transformers' wants the matrix to weight embeddings.
					__lowercase        =      word_embeddings

					return sd_hf





def 					__UpperCamelCase (     a__:			Any			,						a__:			List[Any]      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      6_40_00
					__lowercase        =      config_megatron['''encoder_seq_length''']
					__lowercase        =      config_megatron['''hidden_size''']
					__lowercase        =      config_megatron['''num_layers''']
					__lowercase        =      config_megatron['''num_attention_heads''']
					__lowercase        =      config_megatron['''ffn_hidden_size''']
					__lowercase        =      '''gelu'''
					__lowercase        =      0.1
					__lowercase        =      0.1
					__lowercase        =      0.1
					__lowercase        =      config_megatron['''layernorm_epsilon''']  # 1e-5
					__lowercase        =      config_megatron['''init_method_std''']  # 0.02
					__lowercase        =      config_megatron['''apply_query_key_layer_scaling''']  # True
					__lowercase        =      True
					__lowercase        =      True

					# This identifies the 6.7B (7B) model which uses a different tokenizer
					if config_megatron["hidden_size"] == 40_96:
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      0  # <unk>
					else:
										__lowercase        =      2  # <s>
										__lowercase        =      3  # <|endoftext|>
										__lowercase        =      0  # <pad>

					return config_hf





def 					__UpperCamelCase (     a__:			Dict      )					->      Union[str, Any]:




					"""simple docstring"""



					print(a__      )

					__lowercase        =      args.checkpoint_path
					__lowercase        =      args.save_path
					if isfile(a__      ):
										raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )

					# Load the model.
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )

					# Load the config.
					__lowercase        =      checkpoint['''hyper_parameters''']['''cfg''']
					__lowercase        =      GPTaConfig()
					__lowercase        =      copy_config(config_hf=a__			,						config_megatron=a__      )
					__lowercase        =      ['''GPT2LMHeadModel''']

					__lowercase        =      checkpoint['''state_dict''']

					# Convert.
					print('''Converting'''      )
					__lowercase        =      convert_megatron_checkpoint(a__			,						a__      )

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(a__			,						a__      )

					__lowercase        =      '''GPTSw3Tokenizer'''

					# Store the config to file.
					print('''Saving config'''      )
					config_hf.save_pretrained(a__      )

					# Store the state_dict to file.
					__lowercase        =      os.path.join(a__			,						'''pytorch_model.bin'''      )
					print(f'''Saving checkpoint to "{output_checkpoint_file}"'''      )
					torch.save(a__			,						a__      )


if __name__ == "__main__":
							a__			:						Union[str, Any]							=			argparse.ArgumentParser()
							parser.add_argument(
							    """--checkpoint_path""",
							    type=str,
							    required=True,
							    help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
							)
							parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
							parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
							a__			:						Dict							=			parser.parse_args()
							main(_args)


