

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      lowerCamelCase_      (	unittest.TestCase	):




	def __init__(     self						:     int					,      __A						:     int					,      __A						:     Optional[Any]=13					,      __A						:     Any=7					,      __A						:     Any=True					,      __A						:     List[Any]=True					,      __A						:     str=True					,      __A						:     int=True					,      __A						:     Optional[Any]=99					,      __A						:     Dict=32					,      __A						:     int=5					,      __A						:     Optional[int]=4					,      __A						:     Dict=37					,      __A						:     List[str]="gelu"					,      __A						:     str=0.1					,      __A						:     int=0.1					,      __A						:     Optional[int]=512					,      __A						:     Optional[int]=16					,      __A						:     List[str]=2					,      __A						:     List[Any]=0.0_2					,      __A						:     int=4					,      ):
		__A :			Tuple		    =			parent
		__A :			Union[str, Any]		    =			batch_size
		__A :			int		    =			seq_length
		__A :			Any		    =			is_training
		__A :			Optional[int]		    =			use_attention_mask
		__A :			List[Any]		    =			use_token_type_ids
		__A :			Optional[int]		    =			use_labels
		__A :			Optional[Any]		    =			vocab_size
		__A :			Tuple		    =			hidden_size
		__A :			int		    =			num_hidden_layers
		__A :			List[Any]		    =			num_attention_heads
		__A :			Optional[Any]		    =			intermediate_size
		__A :			Optional[int]		    =			hidden_act
		__A :			Any		    =			hidden_dropout_prob
		__A :			List[Any]		    =			attention_probs_dropout_prob
		__A :			Any		    =			max_position_embeddings
		__A :			Tuple		    =			type_vocab_size
		__A :			List[str]		    =			type_sequence_label_size
		__A :			List[str]		    =			initializer_range
		__A :			List[Any]		    =			num_choices




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			List[Any]		    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

		__A :			Optional[Any]		    =			None
		if self.use_attention_mask:
			__A :			Optional[int]		    =			random_attention_mask([self.batch_size, self.seq_length]							)

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

		__A :			Dict		    =			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        lowerCAmelCase_       (     self						:     int							):
		__A :			Optional[int]		    =			self.prepare_config_and_inputs()
		__A    ,				__A    ,				__A    ,				__A :			Tuple		    =			config_and_inputs
		__A :			str		    =			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
		return config, inputs_dict




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			Tuple		    =			self.prepare_config_and_inputs()
		__A    ,				__A    ,				__A    ,				__A :			List[Any]		    =			config_and_inputs

		__A :			Optional[int]		    =			True
		__A :			int		    =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
		__A :			List[str]		    =			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      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   str     					=				True

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




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			List[Any]		    =			FlaxRobertaPreLayerNormModelTester(self							)




	@slow
	def        lowerCAmelCase_       (     self						:     Any							):
		for model_class_name in self.all_model_classes:
			__A :			List[Any]		    =			model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""					,      from_pt=__A							)
			__A :			Dict		    =			model(np.ones((1, 1)							)							)
			self.assertIsNotNone(__A							)






@require_flax
class      lowerCamelCase_      (	unittest.TestCase	):




	@slow
	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Dict		    =			FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""					,      from_pt=__A							)

		__A :			str		    =			np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,      dtype=jnp.intaa							)
		__A :			List[Any]		    =			model(__A							)[0]
		__A :			str		    =			[1, 11, 5_0265]
		self.assertEqual(list(output.shape							)					,      __A							)
		# compare the actual values for a slice.
		__A :			int		    =			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]					,      __A					,      atol=1e-4							)							)




	@slow
	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Union[str, Any]		    =			FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""					,      from_pt=__A							)

		__A :			Dict		    =			np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,      dtype=jnp.intaa							)
		__A :			Optional[int]		    =			model(__A							)[0]
		# compare the actual values for a slice.
		__A :			Optional[int]		    =			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]					,      __A					,      atol=1e-4							)							)








"""simple docstring"""


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCAmelCase													={
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		list(s_dict.keys()				)
      for key in keys:
            A				  =		r""".*/layers_(\d+)"""
            A				  =		key
            if re.match(_a     ,   _a				):
                  A				  =		re.sub(r"""layers_(\d+)"""     ,   r"""block/\1/layer"""     ,   _a				)

            A				  =		r"""(encoder|decoder)\/"""

            if re.match(_a     ,   _a				):
                  A				  =		re.match(_a     ,   _a				).groups()
                  if groups[0] == "encoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/1/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/1/layer_norm/"""     ,   _a				)

                  elif groups[0] == "decoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/2/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/2/layer_norm/"""     ,   _a				)

        # 2. Convert other classic mappings
            for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                  if old_key in new_key:
                        A				  =		new_key.replace(_a     ,   _a				)

            print(f'{key} -> {new_key}'				)
            A				  =		s_dict.pop(_a				)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T
      if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T

      # 3. Take extra care of the EXPERTS layer
      for key in list(s_dict.keys()				):
            if "expert" in key:
                  A				  =		s_dict[key].shape[0]
                  A				  =		s_dict[key]
                  for idx in range(_a				):
                        A				  =		expert_weihts[idx]
                        print(f'{key} -> {key.replace("expert/"     ,   "nested fstring"				)}'				)

                  s_dict.pop(_a				)

      return s_dict


UpperCAmelCase													={
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}



def   _A					(	_a	:						Dict     ,   _a	:						int				):



      """simple docstring"""






      import regex as re

      with open(_a     ,   """r"""				) as f:
            A				  =		f.read()

      A				  =		re.findall(r"""(.*) = ([0-9.]*)"""     ,   _a				)
      A				  =		{}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  A				  =		float(_a				) if """.""" in value else int(_a				)

      A				  =		re.findall(r"""(.*activations) = \(\'(.*)\',\)"""     ,   _a				)[0]
      A				  =		str(activation[1]				)

      A				  =		num_experts
      A				  =		SwitchTransformersConfig(**_a				)
      return config



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Dict     ,   _a	:						Optional[Any]=None     ,   _a	:						Dict="./"     ,   _a	:						Dict=8				):



      """simple docstring"""






      print(f'Loading flax weights from : {flax_checkpoint_path}'				)
      A				  =		checkpoints.load_tax_checkpoint(_a				)

      if gin_file is not None:
            A				  =		convert_gin_to_config(_a     ,   _a				)
      else:
            A				  =		SwitchTransformersConfig.from_pretrained(_a				)

      A				  =		SwitchTransformersForConditionalGeneration(_a				)

      A				  =		flax_params["""target"""]
      A				  =		flatten_dict(_a     ,   sep="""/"""				)
      A				  =		rename_keys(_a				)
      A				  =		unflatten_dict(_a     ,   sep="""/"""				)

      # Load the flax params in the PT model
      load_flax_weights_in_pytorch_model(_a     ,   _a				)

      print(f'Save PyTorch model to {pytorch_dump_path}'				)
      pt_model.save_pretrained(_a				)


if __name__ == "__main__":
  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
          " model architecture. If not provided, a `gin_file` has to be provided."
      ),
  )
  parser.add_argument(
      "--gin_file",
      default=None,
      type=str,
      required=False,
      help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
  )
  parser.add_argument(
      "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
  )
  parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
  UpperCAmelCase													=parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )




