




'''simple docstring'''



import unittest

from transformers import 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         RemBertConfig,
         RemBertForCausalLM,
         RemBertForMaskedLM,
         RemBertForMultipleChoice,
         RemBertForQuestionAnswering,
         RemBertForSequenceClassification,
         RemBertForTokenClassification,
         RemBertModel,
     )
     from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=13      ,					__SCREAMING_SNAKE_CASE=7      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=99      ,					__SCREAMING_SNAKE_CASE=32      ,					__SCREAMING_SNAKE_CASE=18      ,					__SCREAMING_SNAKE_CASE=43      ,					__SCREAMING_SNAKE_CASE=5      ,					__SCREAMING_SNAKE_CASE=4      ,					__SCREAMING_SNAKE_CASE=37      ,					__SCREAMING_SNAKE_CASE="gelu"      ,					__SCREAMING_SNAKE_CASE=0.1      ,					__SCREAMING_SNAKE_CASE=0.1      ,					__SCREAMING_SNAKE_CASE=512      ,					__SCREAMING_SNAKE_CASE=16      ,					__SCREAMING_SNAKE_CASE=2      ,					__SCREAMING_SNAKE_CASE=0.02      ,					__SCREAMING_SNAKE_CASE=3      ,					__SCREAMING_SNAKE_CASE=4      ,					__SCREAMING_SNAKE_CASE=None      ,					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					parent
            __snake_case         =					batch_size
            __snake_case         =					seq_length
            __snake_case         =					is_training
            __snake_case         =					use_input_mask
            __snake_case         =					use_token_type_ids
            __snake_case         =					use_labels
            __snake_case         =					vocab_size
            __snake_case         =					hidden_size
            __snake_case         =					input_embedding_size
            __snake_case         =					output_embedding_size
            __snake_case         =					num_hidden_layers
            __snake_case         =					num_attention_heads
            __snake_case         =					intermediate_size
            __snake_case         =					hidden_act
            __snake_case         =					hidden_dropout_prob
            __snake_case         =					attention_probs_dropout_prob
            __snake_case         =					max_position_embeddings
            __snake_case         =					type_vocab_size
            __snake_case         =					type_sequence_label_size
            __snake_case         =					initializer_range
            __snake_case         =					num_labels
            __snake_case         =					num_choices
            __snake_case         =					scope


       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




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

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

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

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

            __snake_case         =					RemBertConfig(
                vocab_size=self.vocab_size      ,					hidden_size=self.hidden_size      ,					input_embedding_size=self.input_embedding_size      ,					output_embedding_size=self.output_embedding_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=__SCREAMING_SNAKE_CASE      ,					initializer_range=self.initializer_range      ,					)

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


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            (
                (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      
            )         =					self.prepare_config_and_inputs()

            __snake_case         =					True
            __snake_case         =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
            __snake_case         =					ids_tensor([self.batch_size, self.seq_length]      ,					vocab_size=2					)

            return (
                config,
                input_ids,
                token_type_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
                encoder_hidden_states,
                encoder_attention_mask,
            )


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					RemBertModel(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE					)
            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE					)
            __snake_case         =					model(__SCREAMING_SNAKE_CASE					)
            self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					True
            __snake_case         =					RemBertModel(__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(
                __SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,					encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,					)
            __snake_case         =					model(
                __SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,					)
            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE					)
            self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					Any:



            '''simple docstring'''




            __snake_case         =					RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					labels=__SCREAMING_SNAKE_CASE					)
            self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					RemBertForMaskedLM(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					labels=__SCREAMING_SNAKE_CASE					)
            self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					int:



            '''simple docstring'''




            __snake_case         =					True
            __snake_case         =					True
            __snake_case         =					RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            # first forward pass
            __snake_case         =					model(
                __SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,					encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,					use_cache=__SCREAMING_SNAKE_CASE      ,					)
            __snake_case         =					outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            __snake_case         =					ids_tensor((self.batch_size, 3)      ,					config.vocab_size					)
            __snake_case         =					ids_tensor((self.batch_size, 3)      ,					vocab_size=2					)

            # append to next input_ids and
            __snake_case         =					torch.cat([input_ids, next_tokens]      ,					dim=-1					)
            __snake_case         =					torch.cat([input_mask, next_mask]      ,					dim=-1					)

            __snake_case         =					model(
                __SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,					encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,					output_hidden_states=__SCREAMING_SNAKE_CASE      ,					)['''hidden_states'''][0]
            __snake_case         =					model(
                __SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,					encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,					past_key_values=__SCREAMING_SNAKE_CASE      ,					output_hidden_states=__SCREAMING_SNAKE_CASE      ,					)['''hidden_states'''][0]

            # select random slice
            __snake_case         =					ids_tensor((1,)      ,					output_from_past.shape[-1]					).item()
            __snake_case         =					output_from_no_past[:, -3:, random_slice_idx].detach()
            __snake_case         =					output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					atol=1E-3					)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					RemBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(
                __SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					start_positions=__SCREAMING_SNAKE_CASE      ,					end_positions=__SCREAMING_SNAKE_CASE      ,					)
            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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					self.num_labels
            __snake_case         =					RemBertForSequenceClassification(__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					labels=__SCREAMING_SNAKE_CASE					)
            self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					self.num_labels
            __snake_case         =					RemBertForTokenClassification(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					labels=__SCREAMING_SNAKE_CASE					)
            self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					self.num_choices
            __snake_case         =					RemBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					input_ids.unsqueeze(1					).expand(-1      ,					self.num_choices      ,					-1					).contiguous()
            __snake_case         =					token_type_ids.unsqueeze(1					).expand(-1      ,					self.num_choices      ,					-1					).contiguous()
            __snake_case         =					input_mask.unsqueeze(1					).expand(-1      ,					self.num_choices      ,					-1					).contiguous()
            __snake_case         =					model(
                __SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					labels=__SCREAMING_SNAKE_CASE      ,					)
            self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_choices)					)




       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




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





@require_torch
class       lowerCAmelCase				(	__lowerCAmelCase			,				__lowerCAmelCase			,				unittest.TestCase):
       __lowercase					: List[str]	    =     (
           (
               RemBertModel,
               RemBertForMaskedLM,
               RemBertForCausalLM,
               RemBertForMultipleChoice,
               RemBertForQuestionAnswering,
               RemBertForSequenceClassification,
               RemBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       __lowercase					: str	    =     (RemBertForCausalLM,) if is_torch_available() else ()
       __lowercase					: int	    =     (
           {
               '''feature-extraction''': RemBertModel,
               '''fill-mask''': RemBertForMaskedLM,
               '''question-answering''': RemBertForQuestionAnswering,
               '''text-classification''': RemBertForSequenceClassification,
               '''text-generation''': RemBertForCausalLM,
               '''token-classification''': RemBertForTokenClassification,
               '''zero-shot''': RemBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case         =					RemBertModelTester(self					)
            __snake_case         =					ConfigTester(self      ,					config_class=__SCREAMING_SNAKE_CASE      ,					hidden_size=37					)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            self.config_tester.run_common_tests()


       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                 __snake_case         =					type
                 self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            (
                (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      
            )         =					self.model_tester.prepare_config_and_inputs_for_decoder()

            __snake_case         =					None

            self.model_tester.create_and_check_model_as_decoder(
                __SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					)




       @slow
       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case         =					RemBertModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)





@require_torch
class       lowerCAmelCase				(	unittest.TestCase):


       @slow
       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case         =					RemBertModel.from_pretrained('''google/rembert'''					)
            __snake_case         =					torch.tensor([[312, 5_6498, 313, 2125, 313]]					)
            __snake_case         =					torch.tensor([[0, 0, 0, 1, 1]]					)
            with torch.no_grad():
                 __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					output_hidden_states=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					1152

            __snake_case         =					torch.Size((1, 5, hidden_size)					)
            self.assertEqual(output['''last_hidden_state'''].shape      ,					__SCREAMING_SNAKE_CASE					)

            __snake_case         =					torch.tensor(
                [
                    [
                        [0.0_754, -0.2_022, 0.1_904],
                        [-0.3_354, -0.3_692, -0.4_791],
                        [-0.2_314, -0.6_729, -0.0_749],
                        [-0.0_396, -0.3_105, -0.4_234],
                        [-0.1_571, -0.0_525, 0.5_353],
                    ]
                ]					)

            # Running on the original tf implementation gives slightly different results here.
            # Not clear why this variations is present
            # TODO: Find reason for discrepancy
            # expected_original_implementation = [[
            #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
            #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
            #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
            #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
            #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
            # ]]

            self.assertTrue(torch.allclose(output['''last_hidden_state'''][:, :, :3]      ,					__SCREAMING_SNAKE_CASE      ,					atol=1E-4					)					)












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

     __snake_case         =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_encoder_rel_embedding

     # Assigning
     __snake_case         =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __snake_case         =					tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

     # Decoder Normalization
     __snake_case         =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __snake_case         =					txa_decoder_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_decoder_rel_embedding

     # Token Embeddings
     __snake_case         =					tax_model['''target''']['''token_embedder''']['''embedding''']
     __snake_case         =					txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          __snake_case         =					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(_lowerCamelCase		)
     print('''T5X Model was sucessfully converted!'''		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[str]					      =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     UpperCAmelCase_    :					List[Any]					      =						parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






