
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 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=13						,    __UpperCamelCase=32						,    __UpperCamelCase=2						,    __UpperCamelCase=3						,    __UpperCamelCase=16						,    __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=0.02						,    __UpperCamelCase=1E-5						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    __UpperCamelCase=True						,    __UpperCamelCase=10						,    __UpperCamelCase=8						,    ):
      A_								=					parent
      A_								=					batch_size
      A_								=					image_size
      A_								=					patch_size
      A_								=					num_channels
      A_								=					embed_dim
      A_								=					depths
      A_								=					num_heads
      A_								=					window_size
      A_								=					mlp_ratio
      A_								=					qkv_bias
      A_								=					hidden_dropout_prob
      A_								=					attention_probs_dropout_prob
      A_								=					drop_path_rate
      A_								=					hidden_act
      A_								=					use_absolute_embeddings
      A_								=					patch_norm
      A_								=					layer_norm_eps
      A_								=					initializer_range
      A_								=					is_training
      A_								=					scope
      A_								=					use_labels
      A_								=					type_sequence_label_size
      A_								=					encoder_stride

  def 					lowercase_						(					self					):
      A_								=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

      A_								=					self.get_config()

      return config, pixel_values, labels

  def 					lowercase_						(					self					):
      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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					DonutSwinModel(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(__UpperCamelCase					)

      A_								=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
      A_								=					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 					lowercase_						(					self					):
      A_								=					self.prepare_config_and_inputs()
      (
          (
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	
      )								=					config_and_inputs
      A_								=					{"pixel_values": pixel_values}
      return config, inputs_dict



@require_torch
class 							lowerCamelCase       (		__snake_case			,  __snake_case			,  unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		(DonutSwinModel,) if is_torch_available() else ()
  lowerCAmelCase_								=		{"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
  lowerCAmelCase_								=		True

  lowerCAmelCase_								=		False
  lowerCAmelCase_								=		False
  lowerCAmelCase_								=		False

  def 					lowercase_						(					self					):
      A_								=					DonutSwinModelTester(self					)
      A_								=					ConfigTester(self						,    config_class=__UpperCamelCase						,    embed_dim=37					)

  def 					lowercase_						(					self					):
      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 					lowercase_						(					self					):
      return

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      # DonutSwin does not use inputs_embeds
      pass

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()

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

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()

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

          A_								=					["pixel_values"]
          self.assertListEqual(arg_names[:1]						,    __UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
      A_								=					True

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

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

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

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

          A_								=					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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					model_class(__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()

      with torch.no_grad():
          A_								=					model(**self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase					)					)

      A_								=					outputs.hidden_states

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

      A_								=					(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]						,    )

      A_								=					outputs.reshaped_hidden_states
      self.assertEqual(len(__UpperCamelCase					)						,    __UpperCamelCase					)

      A_	,	A_	,	A_	,	A_								=					reshaped_hidden_states[0].shape
      A_								=					(
          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 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()

      A_								=					(
          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:
          A_								=					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"]
          A_								=					True

          self.check_hidden_states_output(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
      A_								=					3

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

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

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

  @slow
  def 					lowercase_						(					self					):
      for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_								=					DonutSwinModel.from_pretrained(__UpperCamelCase					)
          self.assertIsNotNone(__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()

      A_								=					_config_zero_init(__UpperCamelCase					)
      for model_class in self.all_model_classes:
          A_								=					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'						,    )








import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def        lowerCAmelCase       (				snake_case__   :      List[str]				,					snake_case__   :      List[str]				,					snake_case__   :      Optional[int]=0 )->			Union[str, Any]:
    # Format the message.
    if name is None:
        A_								=					None
    else:
        A_								=					"." * max(0				,					spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
        A_								=					fmt.format(snake_case__ )

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





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      List[Any]				,					snake_case__   :      str				,					snake_case__   :      str )->			Union[str, Any]:
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    A_								=					param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A_								=					(num_heads, num_splits, hidden_size) + input_shape[1:]
    A_								=					param.view(*snake_case__ )
    A_								=					param.transpose(0				,					1 ).contiguous()
    A_								=					param.view(*snake_case__ )
    return param





def        lowerCAmelCase       (				snake_case__   :      Dict				,					snake_case__   :      str )->			Optional[int]:
    A_								=					config.n_positions
    A_								=					config.n_layer
    A_								=					config.vocab_size
    A_								=					config.n_head
    A_								=					config.n_embd // config.n_head

    A_								=					sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
    A_								=					{
        "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"],
    }

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

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

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

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

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

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

    return sd_hf





def        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      List[Any] )->			Dict:
    A_								=					64000
    A_								=					config_megatron["encoder_seq_length"]
    A_								=					config_megatron["hidden_size"]
    A_								=					config_megatron["num_layers"]
    A_								=					config_megatron["num_attention_heads"]
    A_								=					config_megatron["ffn_hidden_size"]
    A_								=					"gelu"
    A_								=					0.1
    A_								=					0.1
    A_								=					0.1
    A_								=					config_megatron["layernorm_epsilon"]  # 1e-5
    A_								=					config_megatron["init_method_std"]  # 0.02
    A_								=					config_megatron["apply_query_key_layer_scaling"]  # True
    A_								=					True
    A_								=					True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4096:
        A_								=					1  # <|endoftext|>
        A_								=					1  # <|endoftext|>
        A_								=					0  # <unk>
    else:
        A_								=					2  # <s>
        A_								=					3  # <|endoftext|>
        A_								=					0  # <pad>

    return config_hf





def        lowerCAmelCase       (				snake_case__   :      Tuple )->			Dict:
    print(snake_case__ )

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

    # Load the model.
    A_								=					torch.load(snake_case__				,					map_location="cpu" )

    # Load the config.
    A_								=					checkpoint["hyper_parameters"]["cfg"]
    A_								=					GPTaConfig()
    A_								=					copy_config(config_hf=snake_case__				,					config_megatron=snake_case__ )
    A_								=					["GPT2LMHeadModel"]

    A_								=					checkpoint["state_dict"]

    # Convert.
    print("Converting" )
    A_								=					convert_megatron_checkpoint(snake_case__				,					snake_case__ )

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

    A_								=					"GPTSw3Tokenizer"

    # Store the config to file.
    print("Saving config" )
    config_hf.save_pretrained(snake_case__ )

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


if __name__ == "__main__":
     __magic_name__  :  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')
     __magic_name__  :  str     			=   parser.parse_args()
     main(_args)






