
"""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__						(					lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					List[Any] ) ->      str:






    """simple docstring"""




    for tf_name, hf_name in patterns:
        UpperCAmelCase									=					k.replace(lowerCAmelCase ,				lowerCAmelCase )
    return k




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






    """simple docstring"""




    UpperCAmelCase									=					BigBirdPegasusConfig(**lowerCAmelCase )
    UpperCAmelCase									=					BigBirdPegasusForConditionalGeneration(lowerCAmelCase )
    UpperCAmelCase									=					torch_model.state_dict()
    UpperCAmelCase									=					{}

    # separating decoder weights
    UpperCAmelCase									=					{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
    UpperCAmelCase									=					{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									=					[k.endswith(lowerCAmelCase ) for ending in KEYS_TO_IGNORE]
        if any(lowerCAmelCase ):
            continue
        UpperCAmelCase									=					DECODER_PATTERNS
        UpperCAmelCase									=					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									=					v.T
        UpperCAmelCase									=					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									=					[k.endswith(lowerCAmelCase ) for ending in KEYS_TO_IGNORE]
        if any(lowerCAmelCase ):
            continue
        UpperCAmelCase									=					REMAINING_PATTERNS
        UpperCAmelCase									=					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									=					v.T
        UpperCAmelCase									=					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									=					mapping['model.embed_positions.weight']
    UpperCAmelCase									=					mapping.pop('model.embed_positions.weight' )
    UpperCAmelCase			,							UpperCAmelCase									=					torch_model.load_state_dict(lowerCAmelCase ,				strict=lowerCAmelCase )
    UpperCAmelCase									=					[
        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__						(					lowerCAmelCase				:					List[str] ) ->      Dict:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					dict ) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					get_tf_weights_as_numpy(lowerCAmelCase )
    UpperCAmelCase									=					convert_bigbird_pegasus(lowerCAmelCase ,				lowerCAmelCase )
    torch_model.save_pretrained(lowerCAmelCase )


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

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=9_9   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=5_1_2   ,       lowercase_=1_6   ,       lowercase_=2   ,       lowercase_=0.0_2   ,       lowercase_=4   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					seq_length
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_attention_mask
          UpperCAmelCase									=					use_token_type_ids
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					vocab_size
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					intermediate_size
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					hidden_dropout_prob
          UpperCAmelCase									=					attention_probs_dropout_prob
          UpperCAmelCase									=					max_position_embeddings
          UpperCAmelCase									=					type_vocab_size
          UpperCAmelCase									=					type_sequence_label_size
          UpperCAmelCase									=					initializer_range
          UpperCAmelCase									=					num_choices




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size							)

          UpperCAmelCase									=					None
          if self.use_attention_mask:
              UpperCAmelCase									=					random_attention_mask([self.batch_size, self.seq_length]							)

          UpperCAmelCase									=					None
          if self.use_token_type_ids:
              UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size							)

          UpperCAmelCase									=					RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       is_decoder=lowercase_   ,       initializer_range=self.initializer_range   ,       )

          return config, input_ids, token_type_ids, attention_mask




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs

          UpperCAmelCase									=					True
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       vocab_size=2							)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	True

      __SCREAMING_SNAKE_CASE   :							int							      =	(
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModelTester(self							)




      @slow
      def     a_      (							self							)						->					Dict:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase									=					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)
              UpperCAmelCase									=					model(np.ones((1, 1)							)							)
              self.assertIsNotNone(lowercase_							)

@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @slow
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          UpperCAmelCase									=					[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape							)   ,       lowercase_							)
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)




      @slow
      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)



