


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
    )



class 	__lowerCamelCase			(_a						):







       def __init__(			self: Optional[int],A_: Dict,A_: List[str]=13,A_: List[str]=7,A_: Dict=True,A_: Any=True,A_: Optional[Any]=False,A_: Any=True,A_: List[Any]=99,A_: Optional[Any]=32,A_: Union[str, Any]=5,A_: str=4,A_: Optional[int]=64,A_: Tuple="gelu",A_: Optional[int]=0.1,A_: str=0.1,A_: Union[str, Any]=512,A_: Optional[Any]=16,A_: Any=2,A_: Dict=0.0_2,A_: Dict=3,A_: List[Any]=4,A_: Optional[Any]=None,A_: Tuple=2,A_: Optional[int]=2,A_: int=2,A_: List[Any]=2,A_: Optional[Any]=4,A_: Any=1,):






            '''simple docstring'''




            __UpperCamelCase =			parent
            __UpperCamelCase =			batch_size
            __UpperCamelCase =			seq_length
            __UpperCamelCase =			is_training
            __UpperCamelCase =			use_input_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_labels
            __UpperCamelCase =			num_choices
            __UpperCamelCase =			scope
            __UpperCamelCase =			q_groups
            __UpperCamelCase =			k_groups
            __UpperCamelCase =			v_groups
            __UpperCamelCase =			post_attention_groups
            __UpperCamelCase =			intermediate_groups
            __UpperCamelCase =			output_groups







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			ids_tensor([self.batch_size, self.seq_length],self.vocab_size					)

            __UpperCamelCase =			None
            if self.use_input_mask:
                 __UpperCamelCase =			random_attention_mask([self.batch_size, self.seq_length]					)

            __UpperCamelCase =			None
            __UpperCamelCase =			None
            __UpperCamelCase =			None
            if self.use_labels:
                 __UpperCamelCase =			ids_tensor([self.batch_size],self.type_sequence_label_size					)
                 __UpperCamelCase =			ids_tensor([self.batch_size, self.seq_length],self.num_labels					)
                 __UpperCamelCase =			ids_tensor([self.batch_size],self.num_choices					)

            __UpperCamelCase =			self.get_config()

            return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            return SqueezeBertConfig(
                embedding_size=self.hidden_size,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,attention_probs_dropout_prob=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,initializer_range=self.initializer_range,q_groups=self.q_groups,k_groups=self.k_groups,v_groups=self.v_groups,post_attention_groups=self.post_attention_groups,intermediate_groups=self.intermediate_groups,output_groups=self.output_groups,)







       def 		snake_case_	(			self: List[str],A_: str,A_: str,A_: str,A_: List[Any],A_: Any,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertModel(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,A_					)
            __UpperCamelCase =			model(A_					)
            self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)					)







       def 		snake_case_	(			self: int,A_: Optional[Any],A_: List[Any],A_: List[str],A_: List[str],A_: Optional[Any],A_: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertForMaskedLM(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)					)







       def 		snake_case_	(			self: str,A_: str,A_: Union[str, Any],A_: Tuple,A_: Any,A_: List[str],A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertForQuestionAnswering(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(
                A_,attention_mask=A_,start_positions=A_,end_positions=A_					)
            self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length)					)
            self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length)					)







       def 		snake_case_	(			self: Optional[int],A_: Optional[int],A_: List[str],A_: int,A_: Optional[int],A_: Dict,A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			self.num_labels
            __UpperCamelCase =			SqueezeBertForSequenceClassification(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)					)







       def 		snake_case_	(			self: str,A_: Any,A_: str,A_: Optional[Any],A_: Optional[Any],A_: Optional[Any],A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.num_labels
            __UpperCamelCase =			SqueezeBertForTokenClassification(config=A_					)
            model.to(A_					)
            model.eval()

            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)					)







       def 		snake_case_	(			self: Union[str, Any],A_: Tuple,A_: str,A_: Any,A_: Union[str, Any],A_: Dict,A_: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.num_choices
            __UpperCamelCase =			SqueezeBertForMultipleChoice(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			input_ids.unsqueeze(1					).expand(-1,self.num_choices,-1					).contiguous()
            __UpperCamelCase =			input_mask.unsqueeze(1					).expand(-1,self.num_choices,-1					).contiguous()
            __UpperCamelCase =			model(
                A_,attention_mask=A_,labels=A_,)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.prepare_config_and_inputs()
            ((__UpperCamelCase),    (__UpperCamelCase),    (__UpperCamelCase),    (__UpperCamelCase),    (__UpperCamelCase),    (__UpperCamelCase)) =			config_and_inputs
            __UpperCamelCase =			{'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict



@require_torch
class 	__lowerCamelCase			(_a				,      _a				,      unittest.TestCase						):
       _lowercase											=     (
           (
               SqueezeBertModel,
               SqueezeBertForMaskedLM,
               SqueezeBertForMultipleChoice,
               SqueezeBertForQuestionAnswering,
               SqueezeBertForSequenceClassification,
               SqueezeBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       _lowercase											=     (
           {
               """feature-extraction""": SqueezeBertModel,
               """fill-mask""": SqueezeBertForMaskedLM,
               """question-answering""": SqueezeBertForQuestionAnswering,
               """text-classification""": SqueezeBertForSequenceClassification,
               """token-classification""": SqueezeBertForTokenClassification,
               """zero-shot""": SqueezeBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       _lowercase											=     False
       _lowercase											=     True
       _lowercase											=     False







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertModelTester(self					)
            __UpperCamelCase =			ConfigTester(self,config_class=A_,dim=37					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_model(*A_					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_masked_lm(*A_					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_question_answering(*A_					)







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A_					)







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_token_classification(*A_					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A_					)







       @slow
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __UpperCamelCase =			SqueezeBertModel.from_pretrained(A_					)
                 self.assertIsNotNone(A_					)



@require_sentencepiece
@require_tokenizers
@require_torch
class 	__lowerCamelCase			(unittest.TestCase						):







       @slow
       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'					)

            __UpperCamelCase =			torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]					)
            __UpperCamelCase =			model(A_					)[0]
            __UpperCamelCase =			torch.Size((1, 3)					)
            self.assertEqual(output.shape,A_					)
            __UpperCamelCase =			torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]					)
            self.assertTrue(torch.allclose(A_,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
a   	=    {
    "/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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







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

      __SCREAMING_SNAKE_CASE   				=							R"""(encoder|decoder)\/"""

      if re.match(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							re.match(__UpperCAmelCase				,						__UpperCAmelCase   ).groups()
         if groups[0] == "encoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/1/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/pre_mlp_layer_norm/"""				,						R"""/1/layer_norm/"""				,						__UpperCAmelCase   )

         elif groups[0] == "decoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/2/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/pre_mlp_layer_norm/"""				,						R"""/2/layer_norm/"""				,						__UpperCAmelCase   )

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

      print(f"""{key} -> {new_key}"""   )
      __SCREAMING_SNAKE_CASE   				=							s_dict.pop(__UpperCAmelCase   )

   if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      __SCREAMING_SNAKE_CASE   				=							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:
      __SCREAMING_SNAKE_CASE   				=							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:
         __SCREAMING_SNAKE_CASE   				=							s_dict[key].shape[0]
         __SCREAMING_SNAKE_CASE   				=							s_dict[key]
         for idx in range(__UpperCAmelCase   ):
            __SCREAMING_SNAKE_CASE   				=							expert_weihts[idx]
            print(f"""{key} -> {key.replace('expert/'				,						'nested fstring'   )}"""   )

         s_dict.pop(__UpperCAmelCase   )

   return s_dict


a   	=    {
    "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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   import regex as re

   with open(__UpperCAmelCase				,						"""r"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							f.read()

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

   __SCREAMING_SNAKE_CASE   				=							re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,						__UpperCAmelCase   )[0]
   __SCREAMING_SNAKE_CASE   				=							str(activation[1]   )

   __SCREAMING_SNAKE_CASE   				=							num_experts
   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig(**__UpperCAmelCase   )
   return config

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase="./"				,						__UpperCAmelCase=8   )    ->       str:

   '''simple docstring'''







   print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
   __SCREAMING_SNAKE_CASE   				=							checkpoints.load_tax_checkpoint(__UpperCAmelCase   )

   if gin_file is not None:
      __SCREAMING_SNAKE_CASE   				=							convert_gin_to_config(__UpperCAmelCase				,						__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							flax_params["""target"""]
   __SCREAMING_SNAKE_CASE   				=							flatten_dict(__UpperCAmelCase				,						sep="""/"""   )
   __SCREAMING_SNAKE_CASE   				=							rename_keys(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							unflatten_dict(__UpperCAmelCase				,						sep="""/"""   )

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

   print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
   pt_model.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    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")
    a   	=    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,
    )





