




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     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Dict					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Any=13					,      __lowerCamelCase				:     int=7					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     str=True					,      __lowerCamelCase				:     Union[str, Any]=True					,      __lowerCamelCase				:     str=True					,      __lowerCamelCase				:     Dict=99					,      __lowerCamelCase				:     Union[str, Any]=32					,      __lowerCamelCase				:     Tuple=5					,      __lowerCamelCase				:     Optional[Any]=4					,      __lowerCamelCase				:     Union[str, Any]=37					,      __lowerCamelCase				:     List[Any]="gelu"					,      __lowerCamelCase				:     List[str]=0.1					,      __lowerCamelCase				:     Optional[Any]=0.1					,      __lowerCamelCase				:     Dict=5_12					,      __lowerCamelCase				:     List[Any]=16					,      __lowerCamelCase				:     List[Any]=2					,      __lowerCamelCase				:     List[Any]=0.02					,      __lowerCamelCase				:     List[str]=3					,      __lowerCamelCase				:     Any=4					,      __lowerCamelCase				:     Optional[Any]=None					,      )					->     Any:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_input_mask
							a    =			use_token_type_ids
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			type_sequence_label_size
							a    =			initializer_range
							a    =			num_labels
							a    =			num_choices
							a    =			scope





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[str]:
							a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

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

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

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

							a    =			self.get_config()

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





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							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=__lowerCamelCase					,      initializer_range=self.initializer_range					,      )





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     int							)					->     int:
							a    =			NystromformerModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase							)
							a    =			model(__lowerCamelCase					,      token_type_ids=__lowerCamelCase							)
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Any							)					->     Dict:
							a    =			NystromformerForMaskedLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict							)					->     int:
							a    =			NystromformerForQuestionAnswering(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(
							    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      start_positions=__lowerCamelCase					,      end_positions=__lowerCamelCase					,      )
							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 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     str							)					->     Dict:
							a    =			self.num_labels
							a    =			NystromformerForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Tuple							)					->     Any:
							a    =			self.num_labels
							a    =			NystromformerForTokenClassification(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     List[Any]							)					->     int:
							a    =			self.num_choices
							a    =			NystromformerForMultipleChoice(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			input_ids.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
							a    =			token_type_ids.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
							a    =			input_mask.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
							a    =			model(
							    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      labels=__lowerCamelCase					,      )
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Any:
							a    =			self.prepare_config_and_inputs()
							(
							    (
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		
							)    =			config_and_inputs
							a    =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_torch
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					(
				    (
				        NystromformerModel,
				        NystromformerForMaskedLM,
				        NystromformerForMultipleChoice,
				        NystromformerForQuestionAnswering,
				        NystromformerForSequenceClassification,
				        NystromformerForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(
				    {
				        """feature-extraction""": NystromformerModel,
				        """fill-mask""": NystromformerForMaskedLM,
				        """question-answering""": NystromformerForQuestionAnswering,
				        """text-classification""": NystromformerForSequenceClassification,
				        """token-classification""": NystromformerForTokenClassification,
				        """zero-shot""": NystromformerForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					False
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False





				def 							__UpperCAmelCase							(      self				:     int							)					->     Dict:
							a    =			NystromformerModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase					,      hidden_size=37							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Dict:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							a    =			self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										a    =			type
										self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Tuple:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[int]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     List[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     str							)					->     Tuple:
							for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a    =			NystromformerModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




@require_torch
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



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

							with torch.no_grad():
										a    =			model(__lowerCamelCase							)[0]

							a    =			torch.Size((1, 6, 7_68)							)
							self.assertEqual(output.shape					,      __lowerCamelCase							)

							a    =			torch.tensor(
							    [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]							)

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





				@slow
				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict:
							a    =			"the [MASK] of Belgium is Brussels"

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

							a    =			tokenizer(__lowerCamelCase					,      return_tensors="pt"							)

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

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

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












from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={"""help""": """The output directory where the model will be written."""}    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The encoder model checkpoint for weights initialization."""
				            """Don't set if you want to train an encoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The decoder model checkpoint for weights initialization."""
				            """Don't set if you want to train a decoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			HfArgumentParser((ModelArguments,)   )
			((a)    ,		)    =			parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

			# Use explicit specified encoder config
			if model_args.encoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.encoder_config_name   )
			# Use pretrained encoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

			# Use explicit specified decoder config
			if model_args.decoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.decoder_config_name   )
			# Use pretrained decoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

			# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
			a    =			True
			a    =			True

			a    =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
			    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,  decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,  encoder_config=A,  decoder_config=A,  )

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			a    =			decoder_config.decoder_start_token_id
			a    =			decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a    =			decoder_config.bos_token_id
			if pad_token_id is None:
						a    =			decoder_config.eos_token_id

			# This is necessary to make Flax's generate() work
			a    =			decoder_config.eos_token_id
			a    =			decoder_start_token_id
			a    =			pad_token_id

			a    =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

			a    =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path   )
			a    =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id   )

			model.save_pretrained(model_args.output_dir   )
			image_processor.save_pretrained(model_args.output_dir   )
			tokenizer.save_pretrained(model_args.output_dir   )


if __name__ == "__main__":
		main()






