




'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

SCREAMING_SNAKE_CASE_      =			(
    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
)

SCREAMING_SNAKE_CASE_      =			(
    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
)

SCREAMING_SNAKE_CASE_      =			[
    '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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					BigBirdPegasusForConditionalGeneration:
     """simple docstring"""




     __a       =   BigBirdPegasusConfig(**__SCREAMING_SNAKE_CASE		)
     __a       =   BigBirdPegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE		)
     __a       =   torch_model.state_dict()
     __a       =   {}

     # separating decoder weights
     __a       =   {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""		)}
     __a       =   {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       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   DECODER_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          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       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          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       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   REMAINING_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          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       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          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       =   mapping["""model.embed_positions.weight"""]
     __a       =   mapping.pop("""model.embed_positions.weight"""		)
     __a ,						__a       =   torch_model.load_state_dict(__SCREAMING_SNAKE_CASE				,  strict=__SCREAMING_SNAKE_CASE		)
     __a       =   [
         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        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   tf.train.list_variables(__SCREAMING_SNAKE_CASE		)
     __a       =   {}
     __a       =   ["""global_step"""]
     for name, shape in tqdm(__SCREAMING_SNAKE_CASE				,  desc="""converting tf checkpoint to dict"""		):
          __a       =   any(pat in name for pat in ignore_name		)
          if skip_key:
               continue
          __a       =   tf.train.load_variable(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          __a       =   array
     return tf_weights




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     __a       =   get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE		)
     __a       =   convert_bigbird_pegasus(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     torch_model.save_pretrained(__SCREAMING_SNAKE_CASE		)


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










'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
SCREAMING_SNAKE_CASE_      =			{'LayoutLMv2Config', 'LayoutLMv3Config'}


@is_pipeline_test
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  a_       :List[Any]												=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  a_       :List[Any]												=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    a_       :List[Any]												={config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    a_       :Tuple												={
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }

  @require_torch
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   pipeline(
           task="""text-classification"""				,							model="""hf-internal-testing/tiny-random-distilbert"""				,							framework="""pt"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

       __a       =   text_classifier("""This is great !"""				,							top_k=2						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]						)

       __a       =   text_classifier(["""This is great !""", """This is bad"""]				,							top_k=2						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
           ]				,							)

       __a       =   text_classifier("""This is great !"""				,							top_k=1						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

       # Legacy behavior
       __a       =   text_classifier("""This is great !"""				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

       __a       =   text_classifier("""This is great !"""				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]]						)

       __a       =   text_classifier(["""This is great !""", """Something else"""]				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
           ]				,							)

       __a       =   text_classifier(["""This is great !""", """Something else"""]				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[
               {"""label""": """LABEL_0""", """score""": 0.5_0_4},
               {"""label""": """LABEL_0""", """score""": 0.5_0_4},
           ]				,							)

  @require_torch
  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       import torch

       __a       =   pipeline(
           task="""text-classification"""				,							model="""hf-internal-testing/tiny-random-distilbert"""				,							framework="""pt"""				,							device=torch.device("""cpu"""						)				,							)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

  @require_tf
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   pipeline(
           task="""text-classification"""				,							model="""hf-internal-testing/tiny-random-distilbert"""				,							framework="""tf"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

  @slow
  @require_torch
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   pipeline("""text-classification"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""This is bad !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """NEGATIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""Birds are a type of animal"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 0.9_8_8}]						)

  @slow
  @require_tf
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   pipeline("""text-classification"""				,							framework="""tf"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""This is bad !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """NEGATIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""Birds are a type of animal"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 0.9_8_8}]						)

  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       __a       =   TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__				,							tokenizer=SCREAMING_SNAKE_CASE__						)
       return text_classifier, ["HuggingFace is in", "This is another test"]


  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       __a       =   text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       __a       =   """HuggingFace is in"""
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__						)

       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}]						)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)

       __a       =   ["""HuggingFace is in """, """Paris is in France"""]
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}, {"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}]				,							)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)
       self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values()						)

       # Forcing to get all results with `top_k=None`
       # This is NOT the legacy format
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__				,							top_k=SCREAMING_SNAKE_CASE__						)
       __a       =   len(model.config.idalabel.values()						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}] * N, [{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}] * N]				,							)

       __a       =   {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}				,							)
       self.assertTrue(outputs["""label"""] in model.config.idalabel.values()						)

       # This might be used a text pair, but tokenizer + pipe interaction
       # makes it hard to understand that it's not using the pair properly
       # https://github.com/huggingface/transformers/issues/17305
       # We disabled this usage instead as it was outputting wrong outputs.
       __a       =   [["""HuggingFace is in """, """Paris is in France"""]]
       with self.assertRaises(SCREAMING_SNAKE_CASE__						):
            text_classifier(SCREAMING_SNAKE_CASE__						)

       # This used to be valid for doing text pairs
       # We're keeping it working because of backward compatibility
       __a       =   text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]]						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}]				,							)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)






