

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 			a					(		unittest.TestCase			):






						"""simple docstring"""




						def __init__(    self  ,   A_  ,   A_=13  ,   A_=7  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=99  ,   A_=32  ,   A_=5  ,   A_=4  ,   A_=37  ,   A_="gelu"  ,   A_=0.1  ,   A_=0.1  ,   A_=512  ,   A_=16  ,   A_=2  ,   A_=0.02  ,   A_=4  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      parent
										_UpperCAmelCase    :						Any							=      batch_size
										_UpperCAmelCase    :						List[Any]							=      seq_length
										_UpperCAmelCase    :						List[Any]							=      is_training
										_UpperCAmelCase    :						Dict							=      use_attention_mask
										_UpperCAmelCase    :						Union[str, Any]							=      use_token_type_ids
										_UpperCAmelCase    :						List[Any]							=      use_labels
										_UpperCAmelCase    :						List[str]							=      vocab_size
										_UpperCAmelCase    :						Dict							=      hidden_size
										_UpperCAmelCase    :						int							=      num_hidden_layers
										_UpperCAmelCase    :						Union[str, Any]							=      num_attention_heads
										_UpperCAmelCase    :						Optional[int]							=      intermediate_size
										_UpperCAmelCase    :						Dict							=      hidden_act
										_UpperCAmelCase    :						Optional[int]							=      hidden_dropout_prob
										_UpperCAmelCase    :						int							=      attention_probs_dropout_prob
										_UpperCAmelCase    :						List[str]							=      max_position_embeddings
										_UpperCAmelCase    :						Any							=      type_vocab_size
										_UpperCAmelCase    :						Union[str, Any]							=      type_sequence_label_size
										_UpperCAmelCase    :						Optional[Any]							=      initializer_range
										_UpperCAmelCase    :						List[str]							=      num_choices


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

										_UpperCAmelCase    :						Union[str, Any]							=      None
										if self.use_attention_mask:
														_UpperCAmelCase    :						int							=      random_attention_mask([self.batch_size, self.seq_length]		)

										_UpperCAmelCase    :						Dict							=      None
										if self.use_token_type_ids:
														_UpperCAmelCase    :						List[Any]							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.type_vocab_size		)

										_UpperCAmelCase    :						Optional[int]							=      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=A_  ,   initializer_range=self.initializer_range  ,   )

										return config, input_ids, token_type_ids, attention_mask


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.prepare_config_and_inputs()
										_UpperCAmelCase    :						str							=      config_and_inputs
										_UpperCAmelCase    :						Any							=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.prepare_config_and_inputs()
										_UpperCAmelCase    :						Dict							=      config_and_inputs

										_UpperCAmelCase    :						Dict							=      True
										_UpperCAmelCase    :						Tuple							=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
										_UpperCAmelCase    :						Optional[Any]							=      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 			a					(		UpperCAmelCase							,	unittest.TestCase			):






						"""simple docstring"""




						_lowercase        =     True

						_lowercase        =     (
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      FlaxRobertaPreLayerNormModelTester(self		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
														_UpperCAmelCase    :						Any							=      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,   from_pt=A_		)
														_UpperCAmelCase    :						List[str]							=      model(np.ones((1, 1)		)		)
														self.assertIsNotNone(A_		)







@require_flax
class 			a					(		unittest.TestCase			):






						"""simple docstring"""




						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,   from_pt=A_		)

										_UpperCAmelCase    :						str							=      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,   dtype=jnp.intaa		)
										_UpperCAmelCase    :						str							=      model(A_		)[0]
										_UpperCAmelCase    :						Tuple							=      [1, 11, 50265]
										self.assertEqual(list(output.shape		)  ,   A_		)
										# compare the actual values for a slice.
										_UpperCAmelCase    :						List[Any]							=      np.array(
										    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]  ,   dtype=np.floataa		)
										self.assertTrue(np.allclose(output[:, :3, :3]  ,   A_  ,   atol=1e-4		)		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,   from_pt=A_		)

										_UpperCAmelCase    :						List[Any]							=      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,   dtype=jnp.intaa		)
										_UpperCAmelCase    :						Optional[Any]							=      model(A_		)[0]
										# compare the actual values for a slice.
										_UpperCAmelCase    :						str							=      np.array(
										    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]  ,   dtype=np.floataa		)
										self.assertTrue(np.allclose(output[:, :3, :3]  ,   A_  ,   atol=1e-4		)		)





import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = 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'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


