





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()
snake_case_           =     logging.get_logger(__name__)






def lowerCamelCase__       (				snake_case_      :    List[Any]			, snake_case_      :    Optional[Any]=False			, snake_case_      :    Optional[int]=False			) ->							int:
   __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__       (				snake_case_      :    int			, snake_case_      :    Optional[Any]			, snake_case_      :    Tuple=False			, snake_case_      :    List[str]=False			) ->							List[Any]:






   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__       (				snake_case_      :    Tuple			, snake_case_      :    int			, snake_case_      :    List[str]			) ->							Tuple:
   __snake_case							=  dct.pop(snake_case_			)
   __snake_case							=  val






def lowerCamelCase__       (				) ->							Any:
   __snake_case							=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   __snake_case							=  Image.open(requests.get(snake_case_			, stream=snake_case_			).raw			)
   return im






@torch.no_grad()
def lowerCamelCase__       (				snake_case_      :    List[str]			, snake_case_      :    str			, snake_case_      :    Any=False			) ->							Optional[Any]:
   __snake_case							=  False if '''rvlcdip''' in checkpoint_url else True
   __snake_case							=  BeitConfig(use_absolute_position_embeddings=snake_case_			, use_mask_token=snake_case_			)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      __snake_case							=  1024
      __snake_case							=  4096
      __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(snake_case_			, snake_case_			, repo_type='''dataset'''			)			, '''r'''			)			)
      __snake_case							=  {int(snake_case_			): 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(snake_case_			, map_location='''cpu'''			)['''model''']

   __snake_case							=  create_rename_keys(snake_case_			, has_lm_head=snake_case_			)
   for src, dest in rename_keys:
      rename_key(snake_case_			, snake_case_			, snake_case_			)
   read_in_q_k_v(snake_case_			, snake_case_			, has_lm_head=snake_case_			)

   # load HuggingFace model
   __snake_case							=  BeitForMaskedImageModeling(snake_case_			) if has_lm_head else BeitForImageClassification(snake_case_			)
   model.eval()
   model.load_state_dict(snake_case_			)

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

   __snake_case							=  image_processor(images=snake_case_			, return_tensors='''pt'''			)
   __snake_case							=  encoding['''pixel_values''']

   __snake_case							=  model(snake_case_			)
   __snake_case							=  outputs.logits

   # verify logits
   __snake_case							=  [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
   assert logits.shape == torch.Size(snake_case_			), "Shape of logits not as expected"

   Path(snake_case_			).mkdir(exist_ok=snake_case_			)
   print(f"""Saving model to {pytorch_dump_folder_path}"""			)
   model.save_pretrained(snake_case_			)
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
   image_processor.save_pretrained(snake_case_			)

   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(snake_case_			, snake_case_			)			, organization='''nielsr'''			, commit_message='''Add image processor'''			, use_temp_dir=snake_case_			, )
      model.push_to_hub(
          repo_path_or_name=Path(snake_case_			, snake_case_			)			, organization='''nielsr'''			, commit_message='''Add model'''			, use_temp_dir=snake_case_			, )


if __name__ == "__main__":
    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',
    )
    snake_case_           =     parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)
class     lowerCamelCase_					:







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=1_3							,       __lowerCAmelCase=7							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=9_9							,       __lowerCAmelCase=3_2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=4							,       __lowerCAmelCase=3_7							,       __lowerCAmelCase="gelu"							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=5_1_2							,       __lowerCAmelCase=1_6							,       __lowerCAmelCase=2							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=3							,       __lowerCAmelCase=4							,       __lowerCAmelCase=None							,       ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   parent
								__magic_name__		:List[Any]   =   1_3
								__magic_name__		:Union[str, Any]   =   7
								__magic_name__		:Optional[Any]   =   True
								__magic_name__		:Tuple   =   True
								__magic_name__		:List[str]   =   True
								__magic_name__		:List[Any]   =   True
								__magic_name__		:int   =   9_9
								__magic_name__		:Any   =   3_2
								__magic_name__		:Union[str, Any]   =   2
								__magic_name__		:List[str]   =   4
								__magic_name__		:List[Any]   =   3_7
								__magic_name__		:Tuple   =   '''gelu'''
								__magic_name__		:Any   =   0.1
								__magic_name__		:str   =   0.1
								__magic_name__		:List[str]   =   5_1_2
								__magic_name__		:int   =   1_6
								__magic_name__		:Any   =   2
								__magic_name__		:List[Any]   =   0.02
								__magic_name__		:Optional[Any]   =   3
								__magic_name__		:Tuple   =   4
								__magic_name__		:Optional[Any]   =   None







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size  )

								__magic_name__		:str   =   None
								if self.use_input_mask:
												__magic_name__		:Optional[int]   =   random_attention_mask([self.batch_size, self.seq_length]  )

								__magic_name__		:str   =   None
								if self.use_token_type_ids:
												__magic_name__		:List[Any]   =   ids_tensor([self.batch_size, self.seq_length]							,       self.type_vocab_size  )

								__magic_name__		:Union[str, Any]   =   None
								__magic_name__		:Tuple   =   None
								__magic_name__		:str   =   None
								if self.use_labels:
												__magic_name__		:List[Any]   =   ids_tensor([self.batch_size]							,       self.type_sequence_label_size  )
												__magic_name__		:List[Any]   =   ids_tensor([self.batch_size, self.seq_length]							,       self.num_labels  )
												__magic_name__		:List[Any]   =   ids_tensor([self.batch_size]							,       self.num_choices  )

								__magic_name__		:str   =   RoFormerConfig(
								    vocab_size=self.vocab_size							,       hidden_size=self.hidden_size							,       num_hidden_layers=self.num_hidden_layers							,       num_attention_heads=self.num_attention_heads							,       intermediate_size=self.intermediate_size							,       hidden_act=self.hidden_act							,       hidden_dropout_prob=self.hidden_dropout_prob							,       attention_probs_dropout_prob=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       type_vocab_size=self.type_vocab_size							,       initializer_range=self.initializer_range							,       return_dict=__lowerCAmelCase							,       )

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:int   =   TFRoFormerModel(config=__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

								__magic_name__		:List[str]   =   [input_ids, input_mask]
								__magic_name__		:Any   =   model(__lowerCAmelCase  )

								__magic_name__		:List[str]   =   model(__lowerCAmelCase  )

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







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   True
								__magic_name__		:List[str]   =   TFRoFormerForCausalLM(config=__lowerCAmelCase  )
								__magic_name__		:str   =   {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								__magic_name__		:Optional[Any]   =   model(__lowerCAmelCase  )['''logits''']
								self.parent.assertListEqual(
								    list(prediction_scores.numpy().shape  )							,       [self.batch_size, self.seq_length, self.vocab_size]  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   TFRoFormerForMaskedLM(config=__lowerCAmelCase  )
								__magic_name__		:Any   =   {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								__magic_name__		:Dict   =   model(__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.num_labels
								__magic_name__		:str   =   TFRoFormerForSequenceClassification(config=__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}

								__magic_name__		:str   =   model(__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.num_choices
								__magic_name__		:Tuple   =   TFRoFormerForMultipleChoice(config=__lowerCAmelCase  )
								__magic_name__		:int   =   tf.tile(tf.expand_dims(__lowerCAmelCase							,       1  )							,       (1, self.num_choices, 1)  )
								__magic_name__		:Optional[Any]   =   tf.tile(tf.expand_dims(__lowerCAmelCase							,       1  )							,       (1, self.num_choices, 1)  )
								__magic_name__		:Union[str, Any]   =   tf.tile(tf.expand_dims(__lowerCAmelCase							,       1  )							,       (1, self.num_choices, 1)  )
								__magic_name__		:str   =   {
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								    '''token_type_ids''': multiple_choice_token_type_ids,
								}
								__magic_name__		:Tuple   =   model(__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_choices)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self.num_labels
								__magic_name__		:Any   =   TFRoFormerForTokenClassification(config=__lowerCAmelCase  )
								__magic_name__		:str   =   {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								__magic_name__		:Dict   =   model(__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   TFRoFormerForQuestionAnswering(config=__lowerCAmelCase  )
								__magic_name__		:List[str]   =   {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}

								__magic_name__		:Union[str, Any]   =   model(__lowerCAmelCase  )
								self.parent.assertEqual(result.start_logits.shape							,       (self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape							,       (self.batch_size, self.seq_length)  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.prepare_config_and_inputs()
								(
								    (
								    __magic_name__
								)  ,					(
								    __magic_name__
								)  ,					(
								    __magic_name__
								)  ,					(
								    __magic_name__
								)  ,					(
								    __magic_name__
								)  ,					(
								    __magic_name__
								)  ,					(
								    __magic_name__
								)  ,					
								)		:Union[str, Any]   =   config_and_inputs
								__magic_name__		:Optional[Any]   =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict







@require_tf
class     lowerCamelCase_					(   lowerCamelCase      ,					lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	(
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				a__       							=	(
				    {
				        '''feature-extraction''': TFRoFormerModel,
				        '''fill-mask''': TFRoFormerForMaskedLM,
				        '''question-answering''': TFRoFormerForQuestionAnswering,
				        '''text-classification''': TFRoFormerForSequenceClassification,
				        '''text-generation''': TFRoFormerForCausalLM,
				        '''token-classification''': TFRoFormerForTokenClassification,
				        '''zero-shot''': TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				a__       							=	False
				a__       							=	False







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								if pipeline_test_casse_name == "TextGenerationPipelineTests":
												return True

								return False







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   TFRoFormerModelTester(self  )
								__magic_name__		:List[str]   =   ConfigTester(self							,       config_class=__lowerCAmelCase							,       hidden_size=3_7  )







				def       A						(    self  ):
								"""simple docstring"""
								self.config_tester.run_common_tests()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''  )
								self.assertIsNotNone(__lowerCAmelCase  )







@require_tf
class     lowerCamelCase_					(   unittest.TestCase			):







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
								__magic_name__		:Dict   =   tf.constant([[0, 1, 2, 3, 4, 5]]  )
								__magic_name__		:Optional[Any]   =   model(__lowerCAmelCase  )[0]

								# TODO Replace vocab size
								__magic_name__		:int   =   5_0_0_0_0

								__magic_name__		:Tuple   =   [1, 6, vocab_size]
								self.assertEqual(output.shape							,       __lowerCAmelCase  )

								print(output[:, :3, :3]  )

								# TODO Replace values below with what was printed above.
								__magic_name__		:Any   =   tf.constant(
								    [
								        [
								            [-0.12053341, -1.0264901, 0.29221946],
								            [-1.5133783, 0.197433, 0.15190607],
								            [-5.0135403, -3.900256, -0.84038764],
								        ]
								    ]  )
								tf.debugging.assert_near(output[:, :3, :3]							,       __lowerCAmelCase							,       atol=1E-4  )







@require_tf
class     lowerCamelCase_					(   unittest.TestCase			):
				a__       							=	1e-4







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   tf.constant([[4, 1_0]]  )
								__magic_name__		:Optional[int]   =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,       embedding_dim=6  )

								__magic_name__		:Optional[Any]   =   emba(input_ids.shape  )
								__magic_name__		:List[str]   =   tf.constant(
								    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]  )

								tf.debugging.assert_near(__lowerCAmelCase							,       __lowerCAmelCase							,       atol=self.tolerance  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   tf.constant(
								    [
								        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
								        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
								        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
								    ]  )
								__magic_name__		:Union[str, Any]   =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2							,       embedding_dim=5_1_2  )
								emba([2, 1_6, 5_1_2]  )
								__magic_name__		:Optional[int]   =   emba.weight[:3, :5]

								tf.debugging.assert_near(__lowerCAmelCase							,       __lowerCAmelCase							,       atol=self.tolerance  )







@require_tf
class     lowerCamelCase_					(   unittest.TestCase			):
				a__       							=	1e-4







				def       A						(    self  ):
								"""simple docstring"""
								# 2,12,16,64
								__magic_name__		:int   =   tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4							,       dtype=tf.floataa  )							,       shape=(2, 1_2, 1_6, 6_4)  ) / 1_0_0

								__magic_name__		:str   =   -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4							,       dtype=tf.floataa  )							,       shape=(2, 1_2, 1_6, 6_4)  ) / 1_0_0

								__magic_name__		:int   =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2							,       embedding_dim=6_4  )
								__magic_name__		:List[str]   =   embed_positions([2, 1_6, 7_6_8]  )[None, None, :, :]

								__magic_name__  ,					__magic_name__		:Union[str, Any]   =   TFRoFormerSelfAttention.apply_rotary_position_embeddings(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )

								__magic_name__		:Tuple   =   tf.constant(
								    [
								        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
								        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
								        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
								        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
								        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
								        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
								    ]  )
								__magic_name__		:List[str]   =   tf.constant(
								    [
								        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
								        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
								        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
								        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
								        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
								        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
								    ]  )

								tf.debugging.assert_near(query_layer[0, 0, :6, :8]							,       __lowerCAmelCase							,       atol=self.tolerance  )
								tf.debugging.assert_near(key_layer[0, 0, :6, :8]							,       __lowerCAmelCase							,       atol=self.tolerance  )






