






import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
   import jax

   from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor



class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def __init__(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=100 ,     __SCREAMING_SNAKE_CASE      :     int=13 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=30 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=2 ,     __SCREAMING_SNAKE_CASE      :     Tuple=3 ,     __SCREAMING_SNAKE_CASE      :     List[str]=True ,     __SCREAMING_SNAKE_CASE      :     str=True ,     __SCREAMING_SNAKE_CASE      :     List[str]=32 ,     __SCREAMING_SNAKE_CASE      :     Any=5 ,     __SCREAMING_SNAKE_CASE      :     Dict=4 ,     __SCREAMING_SNAKE_CASE      :     Tuple=37 ,     __SCREAMING_SNAKE_CASE      :     Any="gelu" ,     __SCREAMING_SNAKE_CASE      :     Dict=0.1 ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.1 ,     __SCREAMING_SNAKE_CASE      :     List[str]=10 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=0.02 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=3 ,     )      ->							Optional[Any]:
           __UpperCAmelCase     				=parent
           __UpperCAmelCase     				=vocab_size
           __UpperCAmelCase     				=batch_size
           __UpperCAmelCase     				=image_size
           __UpperCAmelCase     				=patch_size
           __UpperCAmelCase     				=num_channels
           __UpperCAmelCase     				=is_training
           __UpperCAmelCase     				=use_labels
           __UpperCAmelCase     				=hidden_size
           __UpperCAmelCase     				=num_hidden_layers
           __UpperCAmelCase     				=num_attention_heads
           __UpperCAmelCase     				=intermediate_size
           __UpperCAmelCase     				=hidden_act
           __UpperCAmelCase     				=hidden_dropout_prob
           __UpperCAmelCase     				=attention_probs_dropout_prob
           __UpperCAmelCase     				=type_sequence_label_size
           __UpperCAmelCase     				=initializer_range

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           __UpperCAmelCase     				=(image_size // patch_size) ** 2
           __UpperCAmelCase     				=num_patches + 1



    def _a							(				self      :     Tuple      )      ->							Optional[Any]:
           __UpperCAmelCase     				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

           __UpperCAmelCase     				=BeitConfig(
               vocab_size=self.vocab_size ,     image_size=self.image_size ,     patch_size=self.patch_size ,     num_channels=self.num_channels ,     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 ,     is_decoder=__SCREAMING_SNAKE_CASE ,     initializer_range=self.initializer_range ,     )

           return config, pixel_values, labels



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     str      )      ->							List[Any]:
           __UpperCAmelCase     				=FlaxBeitModel(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )
           self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)      )



    def _a							(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     int      )      ->							List[Any]:
           __UpperCAmelCase     				=FlaxBeitForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )
           self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.seq_length - 1, self.vocab_size)      )



    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							List[str]:
           __UpperCAmelCase     				=self.type_sequence_label_size
           __UpperCAmelCase     				=FlaxBeitForImageClassification(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )
           self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           __UpperCAmelCase     				=1
           __UpperCAmelCase     				=FlaxBeitForImageClassification(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=self.prepare_config_and_inputs()
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=config_and_inputs
           __UpperCAmelCase     				={"""pixel_values""": pixel_values}
           return config, inputs_dict




@require_flax
class 				_A			(     UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[str]				= (
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )



    def _a							(				self      :     str      )      ->							None:
           __UpperCAmelCase     				=FlaxBeitModelTester(self      )
           __UpperCAmelCase     				=ConfigTester(self ,     config_class=__SCREAMING_SNAKE_CASE ,     has_text_modality=__SCREAMING_SNAKE_CASE ,     hidden_size=37      )



    def _a							(				self      :     int      )      ->							List[str]:
           self.config_tester.run_common_tests()



    def _a							(				self      :     int      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __UpperCAmelCase     				=model_class(__SCREAMING_SNAKE_CASE      )
                  __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] ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Union[str, Any]      )      ->							List[str]:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__      ):
                         __UpperCAmelCase     				=self._prepare_for_class(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=model_class(__SCREAMING_SNAKE_CASE      )

                         @jax.jit
                         def model_jitted(__SCREAMING_SNAKE_CASE      :     List[Any] ,     **__SCREAMING_SNAKE_CASE      :     List[Any]      ):
                                return model(pixel_values=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

                         with self.subTest("""JIT Enabled"""      ):
                                __UpperCAmelCase     				=model_jitted(**__SCREAMING_SNAKE_CASE      ).to_tuple()

                         with self.subTest("""JIT Disabled"""      ):
                                with jax.disable_jit():
                                       __UpperCAmelCase     				=model_jitted(**__SCREAMING_SNAKE_CASE      ).to_tuple()

                         self.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,     len(__SCREAMING_SNAKE_CASE      )      )
                         for jitted_output, output in zip(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ):
                                self.assertEqual(jitted_output.shape ,     output.shape      )



    def _a							(				self      :     Optional[Any]      )      ->							Optional[int]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Optional[int]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     str      )      ->							Dict:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE      )



    @slow
    def _a							(				self      :     Tuple      )      ->							Optional[int]:
           for model_class_name in self.all_model_classes:
                  __UpperCAmelCase     				=model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""      )
                  __UpperCAmelCase     				=model(np.ones((1, 3, 224, 224)      )      )
                  self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )







def       lowercase__	(					)	->				Any:







       """simple docstring"""






       __UpperCAmelCase     				=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       return image





@require_vision
@require_flax
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @cached_property
    def _a							(				self      :     Union[str, Any]      )      ->							List[str]:
           return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""      ) if is_vision_available() else None



    @slow
    def _a							(				self      :     Tuple      )      ->							Tuple:
           __UpperCAmelCase     				=FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""      )

           __UpperCAmelCase     				=self.default_image_processor
           __UpperCAmelCase     				=prepare_img()
           __UpperCAmelCase     				=image_processor(images=__SCREAMING_SNAKE_CASE ,     return_tensors="""np"""      ).pixel_values

           # prepare bool_masked_pos
           __UpperCAmelCase     				=np.ones((1, 196) ,     dtype=__SCREAMING_SNAKE_CASE      )

           # forward pass
           __UpperCAmelCase     				=model(pixel_values=__SCREAMING_SNAKE_CASE ,     bool_masked_pos=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=outputs.logits

           # verify the logits
           __UpperCAmelCase     				=(1, 196, 8192)
           self.assertEqual(logits.shape ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=np.array(
               [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      )

           self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=1e-2      )      )



    @slow
    def _a							(				self      :     Any      )      ->							Optional[Any]:
           __UpperCAmelCase     				=FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""      )

           __UpperCAmelCase     				=self.default_image_processor
           __UpperCAmelCase     				=prepare_img()
           __UpperCAmelCase     				=image_processor(images=__SCREAMING_SNAKE_CASE ,     return_tensors="""np"""      )

           # forward pass
           __UpperCAmelCase     				=model(**__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=outputs.logits

           # verify the logits
           __UpperCAmelCase     				=(1, 1000)
           self.assertEqual(logits.shape ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=np.array([-1.2_385, -1.0_987, -1.0_108]      )

           self.assertTrue(np.allclose(logits[0, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=1e-4      )      )

           __UpperCAmelCase     				=281
           self.assertEqual(logits.argmax(-1      ).item() ,     __SCREAMING_SNAKE_CASE      )



    @slow
    def _a							(				self      :     Dict      )      ->							Any:
           __UpperCAmelCase     				=FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""      )

           __UpperCAmelCase     				=self.default_image_processor
           __UpperCAmelCase     				=prepare_img()
           __UpperCAmelCase     				=image_processor(images=__SCREAMING_SNAKE_CASE ,     return_tensors="""np"""      )

           # forward pass
           __UpperCAmelCase     				=model(**__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=outputs.logits

           # verify the logits
           __UpperCAmelCase     				=(1, 21841)
           self.assertEqual(logits.shape ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=np.array([1.6_881, -0.2_787, 0.5_901]      )

           self.assertTrue(np.allclose(logits[0, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=1e-4      )      )

           __UpperCAmelCase     				=2396
           self.assertEqual(logits.argmax(-1      ).item() ,     __SCREAMING_SNAKE_CASE      )












import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A__      :     List[str]       =					(
    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__      :     List[str]       =					(
    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__      :     Dict       =					[
    '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     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for tf_name, hf_name in patterns:
								lowercase__  					=     k.replace(lowerCamelCase_						,  lowerCamelCase_							)
				return k






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     BigBirdPegasusConfig(**lowerCamelCase_							)
				lowercase__  					=     BigBirdPegasusForConditionalGeneration(lowerCamelCase_							)
				lowercase__  					=     torch_model.state_dict()
				lowercase__  					=     {}

				# separating decoder weights
				lowercase__  					=     {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''							)}
				lowercase__  					=     {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'''							):
								lowercase__  					=     [k.endswith(lowerCamelCase_							) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase_							):
												continue
								lowercase__  					=     DECODER_PATTERNS
								lowercase__  					=     rename_state_dict_key(lowerCamelCase_						,  lowerCamelCase_							)
								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''']							):
												lowercase__  					=     v.T
								lowercase__  					=     torch.from_numpy(lowerCamelCase_							)
								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'''							):
								lowercase__  					=     [k.endswith(lowerCamelCase_							) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase_							):
												continue
								lowercase__  					=     REMAINING_PATTERNS
								lowercase__  					=     rename_state_dict_key(lowerCamelCase_						,  lowerCamelCase_							)
								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''']							):
												lowercase__  					=     v.T
								lowercase__  					=     torch.from_numpy(lowerCamelCase_							)
								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}"""

				lowercase__  					=     mapping['''model.embed_positions.weight''']
				lowercase__  					=     mapping.pop('''model.embed_positions.weight'''							)
				lowercase__					,		lowercase__  					=     torch_model.load_state_dict(lowerCamelCase_						,  strict=lowerCamelCase_							)
				lowercase__  					=     [
				    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     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     tf.train.list_variables(lowerCamelCase_							)
				lowercase__  					=     {}
				lowercase__  					=     ['''global_step''']
				for name, shape in tqdm(lowerCamelCase_						,  desc='''converting tf checkpoint to dict'''							):
								lowercase__  					=     any(pat in name for pat in ignore_name							)
								if skip_key:
												continue
								lowercase__  					=     tf.train.load_variable(lowerCamelCase_						,  lowerCamelCase_							)
								lowercase__  					=     array
				return tf_weights






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_tf_weights_as_numpy(lowerCamelCase_							)
				lowercase__  					=     convert_bigbird_pegasus(lowerCamelCase_						,  lowerCamelCase_							)
				torch_model.save_pretrained(lowerCamelCase_							)


if __name__ == "__main__":
							A__      :     Optional[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__      :     Tuple       =					parser.parse_args()
							A__      :     int       =					{}
							convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




