


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]





def 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	if k.startswith("""encoder"""    ):
		A             =					k.replace(""".attn"""				,       """.self_attn"""    )
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """final_layer_norm"""    )
	elif k.startswith("""decoder"""    ):
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """encoder_attn_layer_norm"""    )
		A             =					k.replace("""norm3"""				,       """final_layer_norm"""    )
	return k





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		A             =					rename_state_dict_key(UpperCamelCase__    )
		if new_k not in valid_keys:
			failures.append([k, new_k]    )
		else:
			A             =					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase__    )
	m.model.load_state_dict(UpperCamelCase__				,       strict=UpperCamelCase__    )
	m.half()
	m.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
				_lowercase							:   str						=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
				parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
				parser.add_argument(
				    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







import unittest

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax

				from transformers.models.big_bird.modeling_flax_big_bird import (
				    FlaxBigBirdForCausalLM,
				    FlaxBigBirdForMaskedLM,
				    FlaxBigBirdForMultipleChoice,
				    FlaxBigBirdForPreTraining,
				    FlaxBigBirdForQuestionAnswering,
				    FlaxBigBirdForSequenceClassification,
				    FlaxBigBirdForTokenClassification,
				    FlaxBigBirdModel,
				)






class    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__=2					, a__=56					, a__=True					, a__=True					, a__=True					, a__=True					, a__=99					, a__=32					, a__=2					, a__=2					, a__=7					, a__="gelu_new"					, a__=0.1					, a__=0.1					, a__=512					, a__=16					, a__=2					, a__=0.02					, a__=4					, a__="block_sparse"					, a__=True					, a__=False					, a__=2					, a__=3					, )						->					List[Any]:
					A             =					parent
					A             =					batch_size
					A             =					seq_length
					A             =					is_training
					A             =					use_attention_mask
					A             =					use_token_type_ids
					A             =					use_labels
					A             =					vocab_size
					A             =					hidden_size
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					intermediate_size
					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					max_position_embeddings
					A             =					type_vocab_size
					A             =					type_sequence_label_size
					A             =					initializer_range
					A             =					num_choices

					A             =					rescale_embeddings
					A             =					attention_type
					A             =					use_bias
					A             =					block_size
					A             =					num_random_blocks

				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size )

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

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

					A             =					BigBirdConfig(
					    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=a__					, initializer_range=self.initializer_range					, attention_type=self.attention_type					, block_size=self.block_size					, num_random_blocks=self.num_random_blocks					, use_bias=self.use_bias					, rescale_embeddings=self.rescale_embeddings					, )

					return config, input_ids, token_type_ids, attention_mask

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					self.prepare_config_and_inputs()
					A       ,      A       ,      A       ,      A             =					config_and_inputs
					A             =					{
					    """input_ids""": input_ids,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": attention_mask,
					}
					return config, inputs_dict






@require_flax
class    _UpperCamelCase     (       __snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			(
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				lowerCAmelCase					=			False
				lowerCAmelCase					=			False

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					FlaxBigBirdModelTester(self )

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					Tuple:
					super().test_from_pretrained_save_pretrained()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					Dict:
					super().test_from_pretrained_with_no_automatic_init()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					str:
					super().test_no_automatic_init()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					super().test_hidden_states_output()

				@slow
				def   _UpperCAmelCase       (    self )						->					str:
					for model_class_name in self.all_model_classes:
						A             =					model_class_name.from_pretrained("""google/bigbird-roberta-base""" )
						self.assertIsNotNone(a__ )

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					if self.test_attn_probs:
						super().test_attention_outputs()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A       ,      A             =					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__ ):
							A             =					self._prepare_for_class(a__					, a__ )
							A             =					model_class(a__ )

							@jax.jit
							def model_jitted(a__					, a__=None					, **a__ ):
								return model(input_ids=a__					, attention_mask=a__					, **a__ )

							with self.subTest("""JIT Enabled""" ):
								A             =					model_jitted(**a__ ).to_tuple()

							with self.subTest("""JIT Disabled""" ):
								with jax.disable_jit():
									A             =					model_jitted(**a__ ).to_tuple()

							self.assertEqual(len(a__ )					, len(a__ ) )
							for jitted_output, output in zip(a__					, a__ ):
								self.assertEqual(jitted_output.shape					, output.shape )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__=1e-5					, a__="outputs"					, a__=None )						->					Tuple:
					# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
					# an effort was done to return `attention_probs` (yet to be verified).
					if name.startswith("""outputs.attentions""" ):
						return
					else:
						super().check_pt_flax_outputs(a__					, a__					, a__					, a__					, a__					, a__ )



