


'''simple docstring'''



import unittest

from transformers import LiltConfig, 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 (
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
      )
      from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=13			,     SCREAMING_SNAKE_CASE_=7			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=99			,     SCREAMING_SNAKE_CASE_=24			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=6			,     SCREAMING_SNAKE_CASE_=37			,     SCREAMING_SNAKE_CASE_="gelu"			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=5_12			,     SCREAMING_SNAKE_CASE_=16			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=0.0_2			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=10_00			,     )				->     Union[str, Any]:
         __lowerCamelCase   :	List[Any]								=		parent
         __lowerCamelCase   :	str								=		batch_size
         __lowerCamelCase   :	Dict								=		seq_length
         __lowerCamelCase   :	int								=		is_training
         __lowerCamelCase   :	List[str]								=		use_input_mask
         __lowerCamelCase   :	Dict								=		use_token_type_ids
         __lowerCamelCase   :	Optional[int]								=		use_labels
         __lowerCamelCase   :	Optional[int]								=		vocab_size
         __lowerCamelCase   :	Any								=		hidden_size
         __lowerCamelCase   :	Union[str, Any]								=		num_hidden_layers
         __lowerCamelCase   :	Union[str, Any]								=		num_attention_heads
         __lowerCamelCase   :	Any								=		intermediate_size
         __lowerCamelCase   :	str								=		hidden_act
         __lowerCamelCase   :	int								=		hidden_dropout_prob
         __lowerCamelCase   :	List[Any]								=		attention_probs_dropout_prob
         __lowerCamelCase   :	str								=		max_position_embeddings
         __lowerCamelCase   :	str								=		type_vocab_size
         __lowerCamelCase   :	Any								=		type_sequence_label_size
         __lowerCamelCase   :	Optional[int]								=		initializer_range
         __lowerCamelCase   :	Tuple								=		num_labels
         __lowerCamelCase   :	List[str]								=		scope
         __lowerCamelCase   :	int								=		range_bbox







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	str								=		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size	)

         __lowerCamelCase   :	List[Any]								=		ids_tensor([self.batch_size, self.seq_length, 4]			,     self.range_bbox	)
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]	):
              for j in range(bbox.shape[1]	):
                   if bbox[i, j, 3] < bbox[i, j, 1]:
                        __lowerCamelCase   :	str								=		bbox[i, j, 3]
                        __lowerCamelCase   :	int								=		bbox[i, j, 1]
                        __lowerCamelCase   :	Optional[int]								=		t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                        __lowerCamelCase   :	Tuple								=		bbox[i, j, 2]
                        __lowerCamelCase   :	List[str]								=		bbox[i, j, 0]
                        __lowerCamelCase   :	Any								=		t

         __lowerCamelCase   :	int								=		None
         if self.use_input_mask:
              __lowerCamelCase   :	List[str]								=		ids_tensor([self.batch_size, self.seq_length]			,     vocab_size=2	)

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

         __lowerCamelCase   :	int								=		None
         __lowerCamelCase   :	Any								=		None
         if self.use_labels:
              __lowerCamelCase   :	Optional[int]								=		ids_tensor([self.batch_size]			,     self.type_sequence_label_size	)
              __lowerCamelCase   :	int								=		ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels	)

         __lowerCamelCase   :	List[Any]								=		self.get_config()

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







    def       lowercase_				(							self	)				->     List[str]:
         return LiltConfig(
             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			,     )







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     )				->     int:
         __lowerCamelCase   :	Optional[int]								=		LiltModel(config=SCREAMING_SNAKE_CASE_	)
         model.to(SCREAMING_SNAKE_CASE_	)
         model.eval()
         __lowerCamelCase   :	Optional[Any]								=		model(SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		model(SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_	)

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







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     )				->     Optional[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		self.num_labels
         __lowerCamelCase   :	int								=		LiltForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
         model.to(SCREAMING_SNAKE_CASE_	)
         model.eval()
         __lowerCamelCase   :	Dict								=		model(
             SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_			,     labels=SCREAMING_SNAKE_CASE_	)
         self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     )				->     Dict:
         __lowerCamelCase   :	Optional[Any]								=		LiltForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
         model.to(SCREAMING_SNAKE_CASE_	)
         model.eval()
         __lowerCamelCase   :	Optional[Any]								=		model(
             SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_			,     start_positions=SCREAMING_SNAKE_CASE_			,     end_positions=SCREAMING_SNAKE_CASE_			,     )
         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       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	Union[str, Any]								=		self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       
         )   :	int								=		config_and_inputs
         __lowerCamelCase   :	Optional[Any]								=		{
             'input_ids': input_ids,
             'bbox': bbox,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict







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





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			(
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase  :					List[str]							     =			(
        {
            'feature-extraction': LiltModel,
            'question-answering': LiltForQuestionAnswering,
            'text-classification': LiltForSequenceClassification,
            'token-classification': LiltForTokenClassification,
            'zero-shot': LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase  :					Union[str, Any]							     =			False
    lowerCamelCase  :					str							     =			False







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         return True







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	List[Any]								=		LiltModelTester(self	)
         __lowerCamelCase   :	Union[str, Any]								=		ConfigTester(self			,     config_class=SCREAMING_SNAKE_CASE_			,     hidden_size=37	)







    def       lowercase_				(							self	)				->     Any:
         self.config_tester.run_common_tests()







    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	List[Any]								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	Any								=		self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              __lowerCamelCase   :	int								=		type
              self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	str								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	Optional[Any]								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)







    @slow
    def       lowercase_				(							self	)				->     List[str]:
         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase   :	Dict								=		LiltModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







@require_torch
@slow
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	str								=		LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'	).to(SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.tensor([[1, 2]]			,     device=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]			,     device=SCREAMING_SNAKE_CASE_	)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase   :	int								=		model(input_ids=SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		torch.Size([1, 2, 7_68]	)
         __lowerCamelCase   :	Optional[Any]								=		torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]			,     device=SCREAMING_SNAKE_CASE_			,     )

         self.assertTrue(outputs.last_hidden_state.shape			,     SCREAMING_SNAKE_CASE_	)
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)










'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A__		:					str												=  [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
A__		:					Dict												=  [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

A__		:					int												=  (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	str								=		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'							)}

     for k, v in tqdm(decoder_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict:
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		torch.from_numpy(UpperCAmelCase_							)
          assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     for k, v in tqdm(remaining_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Optional[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	List[str]								=		v.T
          __lowerCamelCase   :	List[str]								=		torch.from_numpy(UpperCAmelCase_							)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     __lowerCamelCase   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		[
         k
         for k in missing
         if k
         not in [
             'final_logits_bias',
             'model.encoder.embed_tokens.weight',
             'model.decoder.embed_tokens.weight',
             'lm_head.weight',
         ]
     ]
     assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], F'no matches found for the following tf keys {extra}'
     return torch_model



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Any												=  argparse.ArgumentParser()
      parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
      parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
      A__		:					List[str]												=  parser.parse_args()
      A__		:					Optional[int]												=  {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






