
"""simple docstring"""






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__UpperCAmelCase       =  (
    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
)

__UpperCAmelCase       =  (
    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
)

__UpperCAmelCase       =  [
    """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 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  Any		)  ->				List[str]:


			'''simple docstring'''





			for tf_name, hf_name in patterns:
						lowerCAmelCase_    :Optional[int]			     =  k.replace(UpperCAmelCase_      ,     UpperCAmelCase_		)
			return k






def 						_snake_case       (	lowercase__		:  dict      ,     lowercase__		:  dict		)  ->				BigBirdPegasusForConditionalGeneration:


			'''simple docstring'''





			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_    :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 						_snake_case       (	lowercase__		:  Union[str, Any]		)  ->				Dict:


			'''simple docstring'''





			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 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  str      ,     lowercase__		:  dict		)  ->				Union[str, Any]:


			'''simple docstring'''





			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__":
				__UpperCAmelCase       =  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.')
				__UpperCAmelCase       =  parser.parse_args()
				__UpperCAmelCase       =  {}
				convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)










'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         if dst_width < 0 or dst_height < 0:
              raise ValueError('Destination width/height should be > 0'	)

         __lowerCamelCase   :	Dict								=		img
         __lowerCamelCase   :	Any								=		img.shape[1]
         __lowerCamelCase   :	Optional[int]								=		img.shape[0]
         __lowerCamelCase   :	Dict								=		dst_width
         __lowerCamelCase   :	str								=		dst_height

         __lowerCamelCase   :	Dict								=		self.src_w / self.dst_w
         __lowerCamelCase   :	List[Any]								=		self.src_h / self.dst_h

         __lowerCamelCase   :	Optional[int]								=		(
             np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta	) * 2_55
         )







    def       lowercase_				(							self	)				->     List[Any]:
         for i in range(self.dst_h	):
              for j in range(self.dst_w	):
                   __lowerCamelCase   :	Union[str, Any]								=		self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_x * x	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_y * y	)


if __name__ == "__main__":
      A__						,					A__		:					Optional[Any]												=  800, 600
      A__		:					List[str]												=  imread("""image_data/lena.jpg""", 1)
      A__		:					List[Any]												=  NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
      )
      waitKey(0)
      destroyAllWindows()






