


'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowercase      :		List[Any]			=      (
    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
)

__lowercase      :		Optional[Any]			=      (
    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
)

__lowercase      :		Union[str, Any]			=      [
    '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     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Tuple		, _SCREAMING_SNAKE_CASE :  Any  ):


					for tf_name, hf_name in patterns:
										__a     :			int          =    k.replace(_lowercase		, _lowercase  )
					return k






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Optional[Any]		, _SCREAMING_SNAKE_CASE :  str  ):
					__a     :			Union[str, Any]          =    BigBirdPegasusConfig(**_lowercase  )
					__a     :			Union[str, Any]          =    BigBirdPegasusForConditionalGeneration(_lowercase  )
					__a     :			Union[str, Any]          =    torch_model.state_dict()
					__a     :			Dict          =    {}

					# separating decoder weights
					__a     :			Any          =    {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'  )}
					__a     :			Optional[Any]          =    {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'  ):
										__a     :			Optional[Any]          =    [k.endswith(_lowercase  ) for ending in KEYS_TO_IGNORE]
										if any(_lowercase  ):
															continue
										__a     :			Optional[int]          =    DECODER_PATTERNS
										__a     :			Union[str, Any]          =    rename_state_dict_key(_lowercase		, _lowercase  )
										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']  ):
															__a     :			int          =    v.T
										__a     :			List[Any]          =    torch.from_numpy(_lowercase  )
										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'  ):
										__a     :			Optional[int]          =    [k.endswith(_lowercase  ) for ending in KEYS_TO_IGNORE]
										if any(_lowercase  ):
															continue
										__a     :			str          =    REMAINING_PATTERNS
										__a     :			Union[str, Any]          =    rename_state_dict_key(_lowercase		, _lowercase  )
										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']  ):
															__a     :			int          =    v.T
										__a     :			Tuple          =    torch.from_numpy(_lowercase  )
										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}"""

					__a     :			Tuple          =    mapping['''model.embed_positions.weight''']
					__a     :			str          =    mapping.pop('model.embed_positions.weight'  )
					__a     :			List[Any]          =    torch_model.load_state_dict(_lowercase		, strict=_lowercase  )
					__a     :			str          =    [
					    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     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Dict  ):
					__a     :			List[str]          =    tf.train.list_variables(_lowercase  )
					__a     :			List[Any]          =    {}
					__a     :			List[str]          =    ['''global_step''']
					for name, shape in tqdm(_lowercase		, desc='converting tf checkpoint to dict'  ):
										__a     :			Any          =    any(pat in name for pat in ignore_name  )
										if skip_key:
															continue
										__a     :			Tuple          =    tf.train.load_variable(_lowercase		, _lowercase  )
										__a     :			List[Any]          =    array
					return tf_weights






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  List[str]		, _SCREAMING_SNAKE_CASE :  Union[str, Any]		, _SCREAMING_SNAKE_CASE :  int  ):
					__a     :			Dict          =    get_tf_weights_as_numpy(_lowercase  )
					__a     :			Any          =    convert_bigbird_pegasus(_lowercase		, _lowercase  )
					torch_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
	__lowercase      :		Dict			=      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.')
	__lowercase      :		Union[str, Any]			=      parser.parse_args()
	__lowercase      :		Tuple			=      {}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)









import unittest

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


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )