






'''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  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      for tf_name, hf_name in patterns:
            SCREAMING_SNAKE_CASE__     :				str									=k.replace(UpperCamelCase__,	UpperCamelCase__							)
      return k





def  _a(  UpperCamelCase__				:						dict,	UpperCamelCase__				:						dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=BigBirdPegasusConfig(**UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									=BigBirdPegasusForConditionalGeneration(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=torch_model.state_dict()
      SCREAMING_SNAKE_CASE__     :				Dict									={}

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

      SCREAMING_SNAKE_CASE__     :				Dict									=mapping['''model.embed_positions.weight''']
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=mapping.pop('''model.embed_positions.weight'''							)
      SCREAMING_SNAKE_CASE__     :				int									=torch_model.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				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  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str,	UpperCamelCase__				:						dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=get_tf_weights_as_numpy(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				List[str]									=convert_bigbird_pegasus(UpperCamelCase__,	UpperCamelCase__							)
      torch_model.save_pretrained(UpperCamelCase__							)


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

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:






  class      __SCREAMING_SNAKE_CASE      :




   @staticmethod
   def      __magic_name__	(      *__lowercase	:							int					,      **__lowercase	:							Optional[Any]      )	->						Optional[Any]:
         pass






@is_pipeline_test
@require_vision
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @require_torch
 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
       # python and torch versions.
       self.assertIn(
           nested_simplify(__lowercase      )					,      [
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				int									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      framework='''tf'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @slow
 @require_torch
 def      __magic_name__	(      self	:							List[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Any									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )




 @slow
 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				str									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      framework='''tf'''      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )