




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase								=	get_tests_dir('fixtures/spiece.model')





@require_sentencepiece
@require_tokenizers
class 					__lowerCamelCase			(       UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = AlbertTokenizer
    snake_case__   = AlbertTokenizerFast
    snake_case__   = True
    snake_case__   = True
    snake_case__   = True


    def   a (			self	:	Union[str, Any]  ) ->			Optional[Any]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCAmelCase__            =					AlbertTokenizer(SCREAMING_SNAKE_CASE__  )
         tokenizer.save_pretrained(self.tmpdirname  )


    def   a (			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			int:
         lowerCAmelCase__            =					"this is a test"
         lowerCAmelCase__            =					"this is a test"
         return input_text, output_text


    def   a (			self	:	Union[str, Any]  ) ->			Tuple:
         lowerCAmelCase__            =					"<pad>"
         lowerCAmelCase__            =					0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__  )  ,      SCREAMING_SNAKE_CASE__  )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__  )  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ) ->			List[str]:
         lowerCAmelCase__            =					list(self.get_tokenizer().get_vocab().keys()  )

         self.assertEqual(vocab_keys[0]  ,      "<pad>"  )
         self.assertEqual(vocab_keys[1]  ,      "<unk>"  )
         self.assertEqual(vocab_keys[-1]  ,      "▁eloquent"  )
         self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      30_000  )


    def   a (			self	:	Optional[int]  ) ->			int:
         self.assertEqual(self.get_tokenizer().vocab_size  ,      30_000  )


    def   a (			self	:	Any  ) ->			Dict:
         if not self.test_rust_tokenizer:
              return

         lowerCAmelCase__            =					self.get_tokenizer()
         lowerCAmelCase__            =					self.get_rust_tokenizer()

         lowerCAmelCase__            =					"I was born in 92000, and this is falsé."

         lowerCAmelCase__            =					tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )
         self.assertListEqual(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					tokenizer.encode(SCREAMING_SNAKE_CASE__  ,      add_special_tokens=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					rust_tokenizer.encode(SCREAMING_SNAKE_CASE__  ,      add_special_tokens=SCREAMING_SNAKE_CASE__  )
         self.assertListEqual(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.get_rust_tokenizer()
         lowerCAmelCase__            =					tokenizer.encode(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					rust_tokenizer.encode(SCREAMING_SNAKE_CASE__  )
         self.assertListEqual(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Any  ) ->			Optional[int]:
         lowerCAmelCase__            =					AlbertTokenizer(SCREAMING_SNAKE_CASE__  ,      keep_accents=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					tokenizer.tokenize("This is a test"  )
         self.assertListEqual(SCREAMING_SNAKE_CASE__  ,      ["▁this", "▁is", "▁a", "▁test"]  )

         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )  ,      [48, 25, 21, 1_289]  )

         lowerCAmelCase__            =					tokenizer.tokenize("I was born in 92000, and this is falsé."  )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__  ,      ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]  )
         lowerCAmelCase__            =					tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )
         self.assertListEqual(SCREAMING_SNAKE_CASE__  ,      [31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]  )

         lowerCAmelCase__            =					tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__  ,      ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]  ,      )


    def   a (			self	:	Tuple  ) ->			int:
         lowerCAmelCase__            =					AlbertTokenizer(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					tokenizer.encode("sequence builders"  )
         lowerCAmelCase__            =					tokenizer.encode("multi-sequence build"  )

         lowerCAmelCase__            =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]


    @slow
    def   a (			self	:	Union[str, Any]  ) ->			Optional[Any]:
         # fmt: off
         lowerCAmelCase__            =					{"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=SCREAMING_SNAKE_CASE__  ,      model_name="albert-base-v2"  ,      revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"  ,      )



"""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,
  )




