
'''simple docstring'''



import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OpenAIGPTConfig,
						    OpenAIGPTDoubleHeadsModel,
						    OpenAIGPTForSequenceClassification,
						    OpenAIGPTLMHeadModel,
						    OpenAIGPTModel,
						)




class     UpperCAmelCase_	:






						'''simple docstring'''






						def __init__(   self       ,  _lowercase       ,  _lowercase=13       ,  _lowercase=7       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase=99       ,  _lowercase=32       ,  _lowercase=5       ,  _lowercase=4       ,  _lowercase=37       ,  _lowercase="gelu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=512       ,  _lowercase=16       ,  _lowercase=2       ,  _lowercase=0.02       ,  _lowercase=3       ,  _lowercase=4       ,  _lowercase=None       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					parent
												_lowerCAmelCase    		=					batch_size
												_lowerCAmelCase    		=					seq_length
												_lowerCAmelCase    		=					is_training
												_lowerCAmelCase    		=					use_token_type_ids
												_lowerCAmelCase    		=					use_labels
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					hidden_act
												_lowerCAmelCase    		=					hidden_dropout_prob
												_lowerCAmelCase    		=					attention_probs_dropout_prob
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					type_vocab_size
												_lowerCAmelCase    		=					type_sequence_label_size
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					num_labels
												_lowerCAmelCase    		=					num_choices
												_lowerCAmelCase    		=					scope
												_lowerCAmelCase    		=					self.vocab_size - 1




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size )

												_lowerCAmelCase    		=					None
												if self.use_token_type_ids:
																		_lowerCAmelCase    		=					ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size )

												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					None
												if self.use_labels:
																		_lowerCAmelCase    		=					ids_tensor([self.batch_size]       ,  self.type_sequence_label_size )
																		_lowerCAmelCase    		=					ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels )
																		_lowerCAmelCase    		=					ids_tensor([self.batch_size]       ,  self.num_choices )

												_lowerCAmelCase    		=					OpenAIGPTConfig(
												    vocab_size=self.vocab_size       ,  n_embd=self.hidden_size       ,  n_layer=self.num_hidden_layers       ,  n_head=self.num_attention_heads       ,  n_positions=self.max_position_embeddings       ,  pad_token_id=self.pad_token_id       ,  )

												_lowerCAmelCase    		=					ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,  2 )

												return (
												    config,
												    input_ids,
												    head_mask,
												    token_type_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OpenAIGPTModel(config=_lowercase )
												model.to(_lowercase )
												model.eval()

												_lowerCAmelCase    		=					model(_lowercase       ,  token_type_ids=_lowercase       ,  head_mask=_lowercase )
												_lowerCAmelCase    		=					model(_lowercase       ,  token_type_ids=_lowercase )
												_lowerCAmelCase    		=					model(_lowercase )

												self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size) )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OpenAIGPTLMHeadModel(_lowercase )
												model.to(_lowercase )
												model.eval()

												_lowerCAmelCase    		=					model(_lowercase       ,  token_type_ids=_lowercase       ,  labels=_lowercase )
												self.parent.assertEqual(result.loss.shape       ,  () )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size) )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OpenAIGPTDoubleHeadsModel(_lowercase )
												model.to(_lowercase )
												model.eval()

												_lowerCAmelCase    		=					model(_lowercase       ,  token_type_ids=_lowercase       ,  labels=_lowercase )
												self.parent.assertEqual(result.loss.shape       ,  () )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size) )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.num_labels
												_lowerCAmelCase    		=					OpenAIGPTForSequenceClassification(_lowercase )
												model.to(_lowercase )
												model.eval()

												_lowerCAmelCase    		=					ids_tensor([self.batch_size]       ,  self.type_sequence_label_size )
												_lowerCAmelCase    		=					model(_lowercase       ,  token_type_ids=_lowercase       ,  labels=_lowercase )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels) )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.prepare_config_and_inputs()
												(
												    (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       
												)    		=					config_and_inputs
												_lowerCAmelCase    		=					{
												    """input_ids""": input_ids,
												    """token_type_ids""": token_type_ids,
												    """head_mask""": head_mask,
												}

												return config, inputs_dict






@require_torch
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Any										=  (
						    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
						    if is_torch_available()
						    else ()
						)
						_lowercase						: Optional[Any]										=  (
						    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
						_lowercase						: Any										=  (
						    {
						        '''feature-extraction''': OpenAIGPTModel,
						        '''text-classification''': OpenAIGPTForSequenceClassification,
						        '''text-generation''': OpenAIGPTLMHeadModel,
						        '''zero-shot''': OpenAIGPTForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																		# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																		# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
																		# tiny config could not be created.
																		return True

												return False




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=False ):

												"""simple docstring"""



												_lowerCAmelCase    		=					super()._prepare_for_class(_lowercase       ,  _lowercase       ,  return_labels=_lowercase )

												if return_labels:
																		if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																								_lowerCAmelCase    		=					torch.zeros(
																								    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)       ,  dtype=torch.long       ,  device=_lowercase       ,  )
																								_lowerCAmelCase    		=					inputs_dict["""labels"""]
																								_lowerCAmelCase    		=					inputs_dict["""labels"""]
																								_lowerCAmelCase    		=					torch.zeros(
																								    (self.model_tester.batch_size, self.model_tester.num_choices)       ,  dtype=torch.long       ,  device=_lowercase       ,  )
																								_lowerCAmelCase    		=					torch.zeros(
																								    self.model_tester.batch_size       ,  dtype=torch.long       ,  device=_lowercase )
												return inputs_dict




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OpenAIGPTModelTester(self )
												_lowerCAmelCase    		=					ConfigTester(self       ,  config_class=_lowercase       ,  n_embd=37 )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_model(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_double_lm_head_model(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase )




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase    		=					OpenAIGPTModel.from_pretrained(_lowercase )
																		self.assertIsNotNone(_lowercase )






@require_torch
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
												model.to(_lowercase )
												_lowerCAmelCase    		=					torch.tensor([[481, 4_735, 544]]       ,  dtype=torch.long       ,  device=_lowercase )  # the president is
												_lowerCAmelCase    		=					[
												    481,
												    4_735,
												    544,
												    246,
												    963,
												    870,
												    762,
												    239,
												    244,
												    40_477,
												    244,
												    249,
												    719,
												    881,
												    487,
												    544,
												    240,
												    244,
												    603,
												    481,
												]  # the president is a very good man. " \n " i\'m sure he is, " said the

												_lowerCAmelCase    		=					model.generate(_lowercase       ,  do_sample=_lowercase )
												self.assertListEqual(output_ids[0].tolist()       ,  _lowercase )







'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Tuple=False      ,    __lowerCAmelCase       :							Any=False		)		->  Optional[Any]:
   snake_case 						=   """backbone.""" if is_semantic else """"""

   snake_case 						=   []
   for i in range(config.num_hidden_layers		):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')		)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           (F'''{prefix}cls_token''', """beit.embeddings.cls_token"""),
           (F'''{prefix}patch_embed.proj.weight''', """beit.embeddings.patch_embeddings.projection.weight"""),
           (F'''{prefix}patch_embed.proj.bias''', """beit.embeddings.patch_embeddings.projection.bias"""),
           (F'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
       ]		)

   if has_lm_head:
      # mask token + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", """beit.embeddings.mask_token"""),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
          ]		)
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
              ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]		)

   return rename_keys





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str=False      ,    __lowerCAmelCase       :							List[Any]=False		)		->  Optional[int]:
   for i in range(config.num_hidden_layers		):
      snake_case 						=   """backbone.""" if is_semantic else """"""
      # queries, keys and values
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

      snake_case 						=   in_proj_weight[
          : config.hidden_size, :
      ]
      snake_case 						=   q_bias
      snake_case 						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case 						=   in_proj_weight[
          -config.hidden_size :, :
      ]
      snake_case 						=   v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

      snake_case 						=   gamma_a
      snake_case 						=   gamma_a





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Tuple		)		->  Any:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Dict:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Any=False		)		->  str:
   snake_case 						=   False if """rvlcdip""" in checkpoint_url else True
   snake_case 						=   BeitConfig(use_absolute_position_embeddings=__lowerCAmelCase      ,    use_mask_token=__lowerCAmelCase		)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      snake_case 						=   10_24
      snake_case 						=   40_96
      snake_case 						=   24
      snake_case 						=   16

   # labels
   if "rvlcdip" in checkpoint_url:
      snake_case 						=   16
      snake_case 						=   """huggingface/label-files"""
      snake_case 						=   """rvlcdip-id2label.json"""
      snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
      snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case 						=   idalabel
      snake_case 						=   {v: k for k, v in idalabel.items()}

   # load state_dict of original model, remove and rename some keys
   snake_case 						=   torch.hub.load_state_dict_from_url(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]

   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)

   # load HuggingFace model
   snake_case 						=   BeitForMaskedImageModeling(__lowerCAmelCase		) if has_lm_head else BeitForImageClassification(__lowerCAmelCase		)
   model.eval()
   model.load_state_dict(__lowerCAmelCase		)

   # Check outputs on an image
   snake_case 						=   BeitImageProcessor(
       size=config.image_size      ,    resample=PILImageResampling.BILINEAR      ,    do_center_crop=__lowerCAmelCase		)
   snake_case 						=   prepare_img()

   snake_case 						=   image_processor(images=__lowerCAmelCase      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]

   snake_case 						=   model(__lowerCAmelCase		)
   snake_case 						=   outputs.logits

   # verify logits
   snake_case 						=   [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(__lowerCAmelCase		), "Shape of logits not as expected"

   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   print(F'''Saving model to {pytorch_dump_folder_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
   image_processor.save_pretrained(__lowerCAmelCase		)

   if push_to_hub:
      if has_lm_head:
         snake_case 						=   """dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         snake_case 						=   """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
      image_processor.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add image processor"""      ,    use_temp_dir=__lowerCAmelCase      ,    )
      model.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add model"""      ,    use_temp_dir=__lowerCAmelCase      ,    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_url",
     default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
     type=str,
     help="URL to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

