


'''simple docstring'''
import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import 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 (
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetModel,
  )




class 			lowerCamelCase_   :






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


          '''simple docstring'''


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






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size )

          UpperCAmelCase__			:		str													=				None
          if self.use_input_mask:
              UpperCAmelCase__			:		Dict													=				random_attention_mask([self.batch_size, self.seq_length] )

          UpperCAmelCase__			:		List[Any]													=				None
          UpperCAmelCase__			:		int													=				None
          UpperCAmelCase__			:		Dict													=				None
          if self.use_labels:
              UpperCAmelCase__			:		Optional[Any]													=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size )
              UpperCAmelCase__			:		Optional[Any]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels )
              UpperCAmelCase__			:		Tuple													=				ids_tensor([self.batch_size]						,						self.num_choices )

          UpperCAmelCase__			:		Tuple													=				self.get_config()
          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          return MPNetConfig(
              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						,						initializer_range=self.initializer_range						,						)






      def lowercase_							(					self				:    Optional[int]						,						_A				:    int						,						_A				:    List[str]						,						_A				:    Dict						,						_A				:    str						,						_A				:    Union[str, Any]						,						_A				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				MPNetModel(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Union[str, Any]													=				model(_A						,						_A )
          UpperCAmelCase__			:		List[str]													=				model(_A )
          self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape						,						(self.batch_size, self.hidden_size) )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    Union[str, Any]						,						_A				:    int						,						_A				:    Dict						,						_A				:    Any						,						_A				:    str						,						_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				MPNetForQuestionAnswering(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Optional[int]													=				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 lowercase_							(					self				:    Dict						,						_A				:    Dict						,						_A				:    List[str]						,						_A				:    Union[str, Any]						,						_A				:    Union[str, Any]						,						_A				:    List[Any]						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.num_labels
          UpperCAmelCase__			:		Dict													=				MPNetForSequenceClassification(_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		List[Any]													=				model(_A						,						attention_mask=_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels) )






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    Any						,						_A				:    Optional[int]						,						_A				:    str						,						_A				:    Optional[int]						,						_A				:    str						,						_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.num_choices
          UpperCAmelCase__			:		List[str]													=				MPNetForMultipleChoice(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Dict													=				input_ids.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		str													=				input_mask.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		Dict													=				model(
              _A						,						attention_mask=_A						,						labels=_A						,						)
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices) )






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Any						,						_A				:    List[Any]						,						_A				:    int						,						_A				:    List[str]						,						_A				:    int						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.num_labels
          UpperCAmelCase__			:		int													=				MPNetForTokenClassification(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		int													=				model(_A						,						attention_mask=_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels) )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.prepare_config_and_inputs()
          ((UpperCAmelCase__)						,						(UpperCAmelCase__)						,						(UpperCAmelCase__)						,						(UpperCAmelCase__)						,						(UpperCAmelCase__)						,						(UpperCAmelCase__))			:		List[Any]													=				config_and_inputs
          UpperCAmelCase__			:		List[Any]													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




@require_torch
class 			lowerCamelCase_   (	__a     ,       __a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     (
          (
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetModel,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__		     =     (
          {
              'feature-extraction': MPNetModel,
              'fill-mask': MPNetForMaskedLM,
              'question-answering': MPNetForQuestionAnswering,
              'text-classification': MPNetForSequenceClassification,
              'token-classification': MPNetForTokenClassification,
              'zero-shot': MPNetForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     True






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				MPNetModelTester(self )
          UpperCAmelCase__			:		Optional[Any]													=				ConfigTester(self						,						config_class=_A						,						hidden_size=37 )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_model(*_A )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_sequence_classification(*_A )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_multiple_choice(*_A )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_token_classification(*_A )






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_question_answering(*_A )




@require_torch
class 			lowerCamelCase_   (	unittest.TestCase      ):






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


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
          UpperCAmelCase__			:		Union[str, Any]													=				torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
          UpperCAmelCase__			:		List[str]													=				model(_A )[0]
          UpperCAmelCase__			:		int													=				torch.Size((1, 11, 768) )
          self.assertEqual(output.shape						,						_A )
          UpperCAmelCase__			:		Optional[Any]													=				torch.tensor(
              [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]] )
          # compare the actual values for a slice.
          self.assertTrue(torch.allclose(output[:, :3, :3]						,						_A						,						atol=1e-4 ) )




'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key						)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        UpperCAmelCase__			:		Optional[int]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        UpperCAmelCase__			:		List[Any]													=				pt_tensor.transpose(2  ,					3  ,					1  ,					0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    UpperCAmelCase__			:		Tuple													=				flax_model.init_weights(PRNGKey(lowerCAmelCase__						)						)

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
