





'''simple docstring'''
from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
    import tensorflow as tf

    from transformers import TFCvtForImageClassification, TFCvtModel
    from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  self.config_class(**self.inputs_dict	)
        self.parent.assertTrue(hasattr(__A							,	'embed_dim'	)	)
        self.parent.assertTrue(hasattr(__A							,	'num_heads'	)	)






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							str							,	__A       :							Optional[int]							,	__A       :							Dict=1_3							,	__A       :							Optional[Any]=6_4							,	__A       :							Tuple=3							,	__A       :							str=[1_6, 4_8, 9_6]							,	__A       :							List[Any]=[1, 3, 6]							,	__A       :							Tuple=[1, 2, 1_0]							,	__A       :							str=[7, 3, 3]							,	__A       :							Optional[int]=[4, 2, 2]							,	__A       :							Any=[2, 1, 1]							,	__A       :							List[str]=[2, 2, 2]							,	__A       :							List[Any]=[False, False, True]							,	__A       :							Dict=[0.0, 0.0, 0.0]							,	__A       :							Dict=0.02							,	__A       :							int=1e-12							,	__A       :							int=True							,	__A       :							List[Any]=True							,	__A       :							List[Any]=2							,	):
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  patch_sizes
        __UpperCamelCase       =  patch_stride
        __UpperCamelCase       =  patch_padding
        __UpperCamelCase       =  is_training
        __UpperCamelCase       =  use_labels
        __UpperCamelCase       =  num_labels
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  embed_dim
        __UpperCamelCase       =  num_heads
        __UpperCamelCase       =  stride_kv
        __UpperCamelCase       =  depth
        __UpperCamelCase       =  cls_token
        __UpperCamelCase       =  attention_drop_rate
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  layer_norm_eps






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

        __UpperCamelCase       =  None
        if self.use_labels:
               # create a random int32 tensor of given shape
               __UpperCamelCase       =  ids_tensor([self.batch_size]							,	self.num_labels	)

        __UpperCamelCase       =  self.get_config()
        return config, pixel_values, labels






 def        _lowerCamelCase					(       self       :							str	):
        return CvtConfig(
            image_size=self.image_size							,	num_labels=self.num_labels							,	num_channels=self.num_channels							,	embed_dim=self.embed_dim							,	num_heads=self.num_heads							,	patch_sizes=self.patch_sizes							,	patch_padding=self.patch_padding							,	patch_stride=self.patch_stride							,	stride_kv=self.stride_kv							,	depth=self.depth							,	cls_token=self.cls_token							,	attention_drop_rate=self.attention_drop_rate							,	initializer_range=self.initializer_range							,	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[str]							,	__A       :							List[str]							,	__A       :							Optional[Any]	):
        __UpperCamelCase       =  TFCvtModel(config=__A	)
        __UpperCamelCase       =  model(__A							,	training=__A	)
        __UpperCamelCase       =  (self.image_size, self.image_size)
        __UpperCamelCase       ,						__UpperCamelCase       =  image_size[0], image_size[1]
        for i in range(len(self.depth	)	):
               __UpperCamelCase       =  floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
               __UpperCamelCase       =  floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
        self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.embed_dim[-1], height, width)	)






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							Any							,	__A       :							Optional[int]							,	__A       :							Optional[Any]	):
        __UpperCamelCase       =  self.num_labels
        __UpperCamelCase       =  TFCvtForImageClassification(__A	)
        __UpperCamelCase       =  model(__A							,	labels=__A							,	training=__A	)
        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self.prepare_config_and_inputs()
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  config_and_inputs
        __UpperCamelCase       =  {'pixel_values': pixel_values}
        return config, inputs_dict






@require_tf
class      snake_case     (    __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
 SCREAMING_SNAKE_CASE_	:					int					=(
     {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
     if is_tf_available()
     else {}
 )
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=False
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=False
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=False
 SCREAMING_SNAKE_CASE_	:					Any					=False
 SCREAMING_SNAKE_CASE_	:					int					=False






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  TFCvtModelTester(self	)
        __UpperCamelCase       =  TFCvtConfigTester(self							,	config_class=__A							,	has_text_modality=__A							,	hidden_size=3_7	)






 def        _lowerCamelCase					(       self       :							Any	):
        self.config_tester.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()






 @unittest.skip(reason='Cvt does not output attentions'	)
 def        _lowerCamelCase					(       self       :							Tuple	):
        pass






 @unittest.skip(reason='Cvt does not use inputs_embeds'	)
 def        _lowerCamelCase					(       self       :							List[str]	):
        pass






 @unittest.skip(reason='Cvt does not support input and output embeddings'	)
 def        _lowerCamelCase					(       self       :							int	):
        pass






 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('GPU'	)	) == 0							,	reason='TF does not support backprop for grouped convolutions on CPU.'							,	)
 def        _lowerCamelCase					(       self       :							Dict	):
        super().test_dataset_conversion()






 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('GPU'	)	) == 0							,	reason='TF does not support backprop for grouped convolutions on CPU.'							,	)
 @slow
 def        _lowerCamelCase					(       self       :							List[Any]	):
        super().test_keras_fit()






 @unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'	)
 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  tf.keras.mixed_precision.Policy('mixed_float16'	)
        tf.keras.mixed_precision.set_global_policy(__A	)
        super().test_keras_fit()
        tf.keras.mixed_precision.set_global_policy('float32'	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

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

               __UpperCamelCase       =  ['pixel_values']
               self.assertListEqual(arg_names[:1]							,	__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        def check_hidden_states_output(__A       :							List[Any]							,	__A       :							List[Any]							,	__A       :							Dict	):
               __UpperCamelCase       =  model_class(__A	)

               __UpperCamelCase       =  model(**self._prepare_for_class(__A							,	__A	)	)
               __UpperCamelCase       =  outputs.hidden_states

               __UpperCamelCase       =  len(self.model_tester.depth	)
               self.assertEqual(len(__A	)							,	__A	)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]	)							,	[
                       self.model_tester.embed_dim[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]							,	)

        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __UpperCamelCase       =  True
               check_hidden_states_output(__A							,	__A							,	__A	)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __UpperCamelCase       =  True

               check_hidden_states_output(__A							,	__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A	)






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A	)






 @slow
 def        _lowerCamelCase					(       self       :							Dict	):
        for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase       =  TFCvtModel.from_pretrained(__A	)
               self.assertIsNotNone(__A	)






def 				lowercase__   (   ) ->      str:



       """simple docstring"""






       __UpperCamelCase       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image


@require_tf
@require_vision
class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 @cached_property
 def        _lowerCamelCase					(       self       :							Dict	):
        return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)






 @slow
 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

        __UpperCamelCase       =  self.default_image_processor
        __UpperCamelCase       =  prepare_img()
        __UpperCamelCase       =  image_processor(images=__A							,	return_tensors='tf'	)

        # forward pass
        __UpperCamelCase       =  model(**__A	)

        # verify the logits
        __UpperCamelCase       =  tf.TensorShape((1, 1_0_0_0)	)
        self.assertEqual(outputs.logits.shape							,	__A	)

        __UpperCamelCase       =  tf.constant([0.9285, 0.9015, -0.3150]	)
        self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()							,	__A							,	atol=1e-4	)	)







'''simple docstring'''
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Any     ) ->      Optional[Any]:



       """simple docstring"""






       with open(__lowercase     ) as metadata_file:
              __UpperCamelCase       =  json.load(__lowercase     )
       __UpperCamelCase       =  LukeConfig(use_entity_aware_attention=__lowercase				,  **metadata['model_config']     )

       # Load in the weights from the checkpoint_path
       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )

       # Load the entity vocab file
       __UpperCamelCase       =  load_entity_vocab(__lowercase     )

       __UpperCamelCase       =  RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

       # Add special tokens to the token vocabulary for downstream tasks
       __UpperCamelCase       =  AddedToken('<ent>'				,  lstrip=__lowercase				,  rstrip=__lowercase     )
       __UpperCamelCase       =  AddedToken('<ent2>'				,  lstrip=__lowercase				,  rstrip=__lowercase     )
       tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]}     )
       config.vocab_size += 2

       print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''     )
       tokenizer.save_pretrained(__lowercase     )
       with open(os.path.join(__lowercase				,  LukeTokenizer.vocab_files_names['entity_vocab_file']     )				,  'w'     ) as f:
              json.dump(__lowercase				,  __lowercase     )

       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase     )

       # Initialize the embeddings of the special tokens
       __UpperCamelCase       =  state_dict['embeddings.word_embeddings.weight']
       __UpperCamelCase       =  word_emb[tokenizer.convert_tokens_to_ids(['@']     )[0]].unsqueeze(0     )
       __UpperCamelCase       =  word_emb[tokenizer.convert_tokens_to_ids(['#']     )[0]].unsqueeze(0     )
       __UpperCamelCase       =  torch.cat([word_emb, ent_emb, enta_emb]     )

       # Initialize the query layers of the entity-aware self-attention mechanism
       for layer_index in range(config.num_hidden_layers     ):
              for matrix_name in ["query.weight", "query.bias"]:
                     __UpperCamelCase       =  F'''encoder.layer.{layer_index}.attention.self.'''
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       __UpperCamelCase       =  state_dict['entity_embeddings.entity_embeddings.weight']
       __UpperCamelCase       =  entity_emb[entity_vocab['[MASK]']]

       __UpperCamelCase       =  LukeModel(config=__lowercase     ).eval()

       __UpperCamelCase       ,						__UpperCamelCase       =  model.load_state_dict(__lowercase				,  strict=__lowercase     )
       if not (len(__lowercase     ) == 1 and missing_keys[0] == "embeddings.position_ids"):
              raise ValueError(F'''Missing keys {', '.join(__lowercase     )}. Expected only missing embeddings.position_ids'''     )
       if not (all(key.startswith('entity_predictions'     ) or key.startswith('lm_head'     ) for key in unexpected_keys     )):
              raise ValueError(
                  'Unexpected keys'
                  F''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'     ) or key.startswith('lm_head'     ))]     )}'''     )

       # Check outputs
       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase				,  task='entity_classification'     )

       __UpperCamelCase       =  (
           'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
           ' new world number one avoid a humiliating second- round exit at Wimbledon .'
       )
       __UpperCamelCase       =  (39, 42)
       __UpperCamelCase       =  tokenizer(__lowercase				,  entity_spans=[span]				,  add_prefix_space=__lowercase				,  return_tensors='pt'     )

       __UpperCamelCase       =  model(**__lowercase     )

       # Verify word hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 42, 1024)     )
              __UpperCamelCase       =  torch.tensor(
                  [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 42, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]     )

       if not (outputs.last_hidden_state.shape == expected_shape):
              raise ValueError(
                  F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''     )
       if not torch.allclose(outputs.last_hidden_state[0, :3, :3]				,  __lowercase				,  atol=1e-4     ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 1, 1024)     )
              __UpperCamelCase       =  torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 1, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]     )

       if not (outputs.entity_last_hidden_state.shape != expected_shape):
              raise ValueError(
                  F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
                  F''' {expected_shape}'''     )
       if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]				,  __lowercase				,  atol=1e-4     ):
              raise ValueError

       # Finally, save our PyTorch model and tokenizer
       print('Saving PyTorch model to {}'.format(__lowercase     )     )
       model.save_pretrained(__lowercase     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  {}
       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              for index, line in enumerate(__lowercase     ):
                     __UpperCamelCase       ,						__UpperCamelCase       =  line.rstrip().split('\t'     )
                     __UpperCamelCase       =  index

       return entity_vocab


if __name__ == "__main__":
    a__							:							Any =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
    parser.add_argument(
        '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
    )
    parser.add_argument(
        '''--entity_vocab_path''',
        default=None,
        type=str,
        help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
    )
    parser.add_argument(
        '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
    )
    a__							:							str =parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )
