



'''simple docstring'''






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A    	=   (
    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    	=   (
    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    	=   [
    '''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        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  )    -> Dict:







   """simple docstring"""
   for tf_name, hf_name in patterns:
      __snake_case				:      List[Any]     							= k.replace(A  ,		A  )
   return k




def        _SCREAMING_SNAKE_CASE  (				A  :       dict  ,		A  :       dict  )    -> BigBirdPegasusForConditionalGeneration:







   """simple docstring"""
   __snake_case				:      Tuple     							= BigBirdPegasusConfig(**A  )
   __snake_case				:      Dict     							= BigBirdPegasusForConditionalGeneration(A  )
   __snake_case				:      int     							= torch_model.state_dict()
   __snake_case				:      str     							= {}

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

   __snake_case				:      Optional[int]     							= mapping['model.embed_positions.weight']
   __snake_case				:      Tuple     							= mapping.pop('model.embed_positions.weight'  )
   __snake_case				:      Optional[int]     							= torch_model.load_state_dict(A  ,		strict=A  )
   __snake_case				:      Optional[int]     							= [
       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        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= tf.train.list_variables(A  )
   __snake_case				:      Optional[int]     							= {}
   __snake_case				:      int     							= ['global_step']
   for name, shape in tqdm(A  ,		desc='converting tf checkpoint to dict'  ):
      __snake_case				:      str     							= any(pat in name for pat in ignore_name  )
      if skip_key:
         continue
      __snake_case				:      List[str]     							= tf.train.load_variable(A  ,		A  )
      __snake_case				:      Union[str, Any]     							= array
   return tf_weights




def        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       str  ,		A  :       dict  )    -> int:







   """simple docstring"""
   __snake_case				:      List[Any]     							= get_tf_weights_as_numpy(A  )
   __snake_case				:      List[Any]     							= convert_bigbird_pegasus(A  ,		A  )
   torch_model.save_pretrained(A  )


if __name__ == "__main__":
      __A    	=   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    	=   parser.parse_args()
      __A    	=   {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







'''simple docstring'''






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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor




class a_    (    unittest.TestCase							):







   def __init__(self					,   __a					,   __a=7					,   __a=3					,   __a=1_8					,   __a=3_0					,   __a=4_0_0					,   __a=True					,   __a=None					,   __a=True					,   __a=None					,   __a=True					,   __a=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,   __a=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,   __a=True					,   )			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Tuple     							= size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
      __snake_case				:      Any     							= crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      __snake_case				:      Optional[int]     							= parent
      __snake_case				:      Dict     							= batch_size
      __snake_case				:      str     							= num_channels
      __snake_case				:      Optional[Any]     							= image_size
      __snake_case				:      Optional[int]     							= min_resolution
      __snake_case				:      Tuple     							= max_resolution
      __snake_case				:      Optional[int]     							= do_resize
      __snake_case				:      Optional[int]     							= size
      __snake_case				:      Union[str, Any]     							= do_center_crop
      __snake_case				:      List[Any]     							= crop_size
      __snake_case				:      int     							= do_normalize
      __snake_case				:      Optional[Any]     							= image_mean
      __snake_case				:      str     							= image_std
      __snake_case				:      Optional[Any]     							= do_convert_rgb







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=False					,   __a=False					,   __a=False)			->      List[str]:
      """simple docstring"""






      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         __snake_case				:      Optional[int]     							= []
         for i in range(self.batch_size):
            image_inputs.append(
                np.random.randint(
                    2_5_5					,   size=(self.num_channels, self.max_resolution, self.max_resolution)					,   dtype=np.uinta))
      else:
         __snake_case				:      Dict     							= []
         for i in range(self.batch_size):
            __snake_case							,__snake_case				:      Optional[Any]     							= np.random.choice(np.arange(self.min_resolution					,   self.max_resolution)					,   2)
            image_inputs.append(np.random.randint(2_5_5					,   size=(self.num_channels, width, height)					,   dtype=np.uinta))

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         __snake_case				:      int     							= [Image.fromarray(np.moveaxis(__a					,   0					,   -1)) for x in image_inputs]

      if torchify:
         __snake_case				:      List[Any]     							= [torch.from_numpy(__a) for x in image_inputs]

      return image_inputs




@require_torch
@require_vision
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= ChineseCLIPImageProcessingTester(self					,   do_center_crop=__a)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      int     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size					,   {'height': 2_2_4, 'width': 2_2_4})
      self.assertEqual(image_processor.crop_size					,   {'height': 1_8, 'width': 1_8})

      __snake_case				:      List[str]     							= self.image_processing_class.from_dict(self.image_processor_dict					,   size=4_2					,   crop_size=8_4)
      self.assertEqual(image_processor.size					,   {'shortest_edge': 4_2})
      self.assertEqual(image_processor.crop_size					,   {'height': 8_4, 'width': 8_4})







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Optional[Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

      # Test not batched input
      __snake_case				:      int     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      List[Any]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      __snake_case				:      Optional[int]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   numpify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   np.ndarray)

      # Test not batched input
      __snake_case				:      List[Any]     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      int     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      __snake_case				:      Tuple     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   torchify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   torch.Tensor)

      # Test not batched input
      __snake_case				:      Any     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      Union[str, Any]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )




@require_torch
@require_vision
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= ChineseCLIPImageProcessingTester(self					,   num_channels=4					,   do_center_crop=__a)
      __snake_case				:      List[Any]     							= 3







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Union[str, Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

      # Test not batched input
      __snake_case				:      Optional[int]     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      Optional[int]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )