



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowerCamelCase	:					Tuple			  =					(
    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
)

__lowerCamelCase	:					Union[str, 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
)

__lowerCamelCase	:					Optional[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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 for tf_name, hf_name in patterns:
  UpperCamelCase     : List[str]	     =       k.replace(_lowerCAmelCase							,       _lowerCAmelCase					)
 return k



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			BigBirdPegasusForConditionalGeneration:
 UpperCamelCase     : List[str]	     =       BigBirdPegasusConfig(**_lowerCAmelCase					)
 UpperCamelCase     : Dict	     =       BigBirdPegasusForConditionalGeneration(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       torch_model.state_dict()
 UpperCamelCase     : Optional[Any]	     =       {}

 # separating decoder weights
 UpperCamelCase     : Optional[Any]	     =       {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"					)}
 UpperCamelCase     : Dict	     =       {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"					):
  UpperCamelCase     : List[str]	     =       [k.endswith(_lowerCAmelCase					) for ending in KEYS_TO_IGNORE]
  if any(_lowerCAmelCase					):
   continue
  UpperCamelCase     : Optional[Any]	     =       DECODER_PATTERNS
  UpperCamelCase     : Union[str, Any]	     =       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"]					):
   UpperCamelCase     : List[str]	     =       v.T
  UpperCamelCase     : Dict	     =       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"					):
  UpperCamelCase     : Union[str, Any]	     =       [k.endswith(_lowerCAmelCase					) for ending in KEYS_TO_IGNORE]
  if any(_lowerCAmelCase					):
   continue
  UpperCamelCase     : Tuple	     =       REMAINING_PATTERNS
  UpperCamelCase     : Any	     =       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"]					):
   UpperCamelCase     : str	     =       v.T
  UpperCamelCase     : List[str]	     =       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}"""

 UpperCamelCase     : Dict	     =       mapping["model.embed_positions.weight"]
 UpperCamelCase     : Any	     =       mapping.pop("model.embed_positions.weight"					)
 UpperCamelCase     : List[Any]	     =       torch_model.load_state_dict(_lowerCAmelCase							,       strict=_lowerCAmelCase					)
 UpperCamelCase     : List[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 		A_    (					_lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : Union[str, Any]	     =       tf.train.list_variables(_lowerCAmelCase					)
 UpperCamelCase     : Union[str, Any]	     =       {}
 UpperCamelCase     : Union[str, Any]	     =       ["global_step"]
 for name, shape in tqdm(_lowerCAmelCase							,       desc="converting tf checkpoint to dict"					):
  UpperCamelCase     : int	     =       any(pat in name for pat in ignore_name					)
  if skip_key:
   continue
  UpperCamelCase     : Optional[int]	     =       tf.train.load_variable(_lowerCAmelCase							,       _lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       array
 return tf_weights



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			str:
 UpperCamelCase     : Union[str, Any]	     =       get_tf_weights_as_numpy(_lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       convert_bigbird_pegasus(_lowerCAmelCase							,       _lowerCAmelCase					)
 torch_model.save_pretrained(_lowerCAmelCase					)


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







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class  A__       (	unittest.TestCase      ):





      def __init__(			self	,							A_	,							A_=13	,							A_=3	,							A_=224	,							A_=30	,							A_=400	,							A_=True	,							A_=None	,							A_=True	,							A_=[0.5, 0.5, 0.5]	,							A_=[0.5, 0.5, 0.5]	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase     : Tuple	     =       parent
       UpperCamelCase     : Tuple	     =       batch_size
       UpperCamelCase     : Tuple	     =       num_channels
       UpperCamelCase     : str	     =       image_size
       UpperCamelCase     : Optional[int]	     =       min_resolution
       UpperCamelCase     : List[Any]	     =       max_resolution
       UpperCamelCase     : Union[str, Any]	     =       do_resize
       UpperCamelCase     : str	     =       size
       UpperCamelCase     : List[str]	     =       do_normalize
       UpperCamelCase     : Any	     =       image_mean
       UpperCamelCase     : int	     =       image_std





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       ViTImageProcessor if is_vision_available() else None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       EfficientFormerImageProcessorTester(self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.image_proc_tester.prepare_image_processor_dict()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(A_	,							"image_mean"       )       )
       self.assertTrue(hasattr(A_	,							"image_std"       )       )
       self.assertTrue(hasattr(A_	,							"do_normalize"       )       )
       self.assertTrue(hasattr(A_	,							"do_resize"       )       )
       self.assertTrue(hasattr(A_	,							"size"       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       UpperCamelCase     : Union[str, Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							Image.Image       )

       # Test not batched input
       UpperCamelCase     : Optional[Any]	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Tuple	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       UpperCamelCase     : List[Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							numpify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							np.ndarray       )

       # Test not batched input
       UpperCamelCase     : Dict	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Optional[Any]	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       UpperCamelCase     : Any	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							torchify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							torch.Tensor       )

       # Test not batched input
       UpperCamelCase     : Any	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Any	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)
