





'''simple docstring'''

import unittest

from transformers import AutoTokenizer, NystromformerConfig, 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 (
          NystromformerForMaskedLM,
          NystromformerForMultipleChoice,
          NystromformerForQuestionAnswering,
          NystromformerForSequenceClassification,
          NystromformerForTokenClassification,
          NystromformerModel,
      )
      from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


class 					a_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A=13 ,		A=7 ,		A=True ,		A=True ,		A=True ,		A=True ,		A=99 ,		A=32 ,		A=5 ,		A=4 ,		A=37 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=16 ,		A=2 ,		A=0.02 ,		A=3 ,		A=4 ,		A=None ,		)		->   int:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			seq_length
        _SCREAMING_SNAKE_CASE									=			is_training
        _SCREAMING_SNAKE_CASE									=			use_input_mask
        _SCREAMING_SNAKE_CASE									=			use_token_type_ids
        _SCREAMING_SNAKE_CASE									=			use_labels
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			type_sequence_label_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			num_labels
        _SCREAMING_SNAKE_CASE									=			num_choices
        _SCREAMING_SNAKE_CASE									=			scope


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size       )

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

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

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

        _SCREAMING_SNAKE_CASE									=			self.get_config()

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


    def 			snake_case_(		self       )		->   Any:
        return NystromformerConfig(
            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 ,		)


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			NystromformerModel(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		token_type_ids=A       )
        _SCREAMING_SNAKE_CASE									=			model(A ,		token_type_ids=A       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			NystromformerForMaskedLM(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		token_type_ids=A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			NystromformerForQuestionAnswering(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		attention_mask=A ,		token_type_ids=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 ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			NystromformerForSequenceClassification(A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		token_type_ids=A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			NystromformerForTokenClassification(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		token_type_ids=A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.num_labels)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.num_choices
        _SCREAMING_SNAKE_CASE									=			NystromformerForMultipleChoice(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			input_ids.unsqueeze(1       ).expand(-1 ,		self.num_choices ,		-1       ).contiguous()
        _SCREAMING_SNAKE_CASE									=			token_type_ids.unsqueeze(1       ).expand(-1 ,		self.num_choices ,		-1       ).contiguous()
        _SCREAMING_SNAKE_CASE									=			input_mask.unsqueeze(1       ).expand(-1 ,		self.num_choices ,		-1       ).contiguous()
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		attention_mask=A ,		token_type_ids=A ,		labels=A ,		)
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_choices)       )





    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        (
            (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       
        )									=			config_and_inputs
        _SCREAMING_SNAKE_CASE									=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict




@require_torch
class 					a_     (				snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (
        (
            NystromformerModel,
            NystromformerForMaskedLM,
            NystromformerForMultipleChoice,
            NystromformerForQuestionAnswering,
            NystromformerForSequenceClassification,
            NystromformerForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    UpperCamelCase     = (
        {
            '''feature-extraction''': NystromformerModel,
            '''fill-mask''': NystromformerForMaskedLM,
            '''question-answering''': NystromformerForQuestionAnswering,
            '''text-classification''': NystromformerForSequenceClassification,
            '''token-classification''': NystromformerForTokenClassification,
            '''zero-shot''': NystromformerForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			NystromformerModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		hidden_size=37       )


    def 			snake_case_(		self       )		->   int:
        self.config_tester.run_common_tests()


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            _SCREAMING_SNAKE_CASE									=			type
            self.model_tester.create_and_check_model(*A       )


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*A       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*A       )


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*A       )


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*A       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*A       )





    @slow
    def 			snake_case_(		self       )		->   int:
        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE									=			NystromformerModel.from_pretrained(A       )
            self.assertIsNotNone(A       )




@require_torch
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    @slow
    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor([[0, 1, 2, 3, 4, 5]]       )

        with torch.no_grad():
            _SCREAMING_SNAKE_CASE									=			model(A       )[0]

        _SCREAMING_SNAKE_CASE									=			torch.Size((1, 6, 768)       )
        self.assertEqual(output.shape ,		A       )

        _SCREAMING_SNAKE_CASE									=			torch.tensor(
            [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]       )

        self.assertTrue(torch.allclose(output[:, :3, :3] ,		A ,		atol=1e-4       )       )





    @slow
    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			"""the [MASK] of Belgium is Brussels"""

        _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""       )
        _SCREAMING_SNAKE_CASE									=			NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""       )

        _SCREAMING_SNAKE_CASE									=			tokenizer(A ,		return_tensors="""pt"""       )

        with torch.no_grad():
            _SCREAMING_SNAKE_CASE									=			model(encoding.input_ids       ).logits

        _SCREAMING_SNAKE_CASE									=			token_logits[:, 2, :].argmax(-1       )[0]

        self.assertEqual(tokenizer.decode(A       ) ,		"""capital"""       )










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			classifier_dropout




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]       )



