
'''simple docstring'''

import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, 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 EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
    from transformers.models.esm.modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmEmbeddings,
        create_position_ids_from_input_ids,
    )


class  _UpperCAmelCase :

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=13							,		lowerCAmelCase_=7							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=33							,		lowerCAmelCase_=32							,		lowerCAmelCase_=5							,		lowerCAmelCase_=4							,		lowerCAmelCase_=37							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=16							,		lowerCAmelCase_=2							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=3							,		lowerCAmelCase_=4							,		lowerCAmelCase_=None							,		):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size )

       a_      :	Union[str, Any]      =							None
       if self.use_input_mask:
         a_      :	Any      =							random_attention_mask([self.batch_size, self.seq_length] )

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

       a_      :	Optional[Any]      =							self.get_config()

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return EsmConfig(
           vocab_size=self.vocab_size							,		hidden_size=self.hidden_size							,		pad_token_id=1							,		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							,		initializer_range=self.initializer_range							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							EsmModel(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	List[Any]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							model(lowerCAmelCase_ )
       a_      :	int      =							model(lowerCAmelCase_ )

       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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Dict      =							EsmForMaskedLM(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Optional[int]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.num_labels
       a_      :	Optional[int]      =							EsmForTokenClassification(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	List[str]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.num_labels) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.prepare_config_and_inputs()
       (
           (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , 
       )      :	List[str]      =							config_and_inputs
       a_      :	str      =							{"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					False

     a_    	=					(
         (
             EsmForMaskedLM,
             EsmModel,
             EsmForSequenceClassification,
             EsmForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     a_    	=					()
     a_    	=					(
         {
             "feature-extraction": EsmModel,
             "fill-mask": EsmForMaskedLM,
             "text-classification": EsmForSequenceClassification,
             "token-classification": EsmForTokenClassification,
             "zero-shot": EsmForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     a_    	=					True


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							EsmModelTester(self )
       a_      :	int      =							ConfigTester(self							,		config_class=lowerCAmelCase_							,		hidden_size=37 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.config_tester.run_common_tests()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         a_      :	str      =							type
         self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_ )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_      :	str      =							EsmModel.from_pretrained(lowerCAmelCase_ )
         self.assertIsNotNone(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.model_tester.prepare_config_and_inputs()[0]
       a_      :	str      =							EsmEmbeddings(config=lowerCAmelCase_ )

       a_      :	Any      =							torch.as_tensor([[12, 31, 13, model.padding_idx]] )
       a_      :	Tuple      =							torch.as_tensor(
           [
               [
                   0 + model.padding_idx + 1,
                   1 + model.padding_idx + 1,
                   2 + model.padding_idx + 1,
                   model.padding_idx,
               ]
           ] )
       a_      :	List[str]      =							create_position_ids_from_input_ids(lowerCAmelCase_							,		model.padding_idx )
       self.assertEqual(position_ids.shape							,		expected_positions.shape )
       self.assertTrue(torch.all(torch.eq(lowerCAmelCase_							,		lowerCAmelCase_ ) ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()[0]
       a_      :	Union[str, Any]      =							EsmEmbeddings(config=lowerCAmelCase_ )

       a_      :	int      =							torch.empty(2							,		4							,		30 )
       a_      :	List[Any]      =							[
           0 + embeddings.padding_idx + 1,
           1 + embeddings.padding_idx + 1,
           2 + embeddings.padding_idx + 1,
           3 + embeddings.padding_idx + 1,
       ]
       a_      :	List[Any]      =							torch.as_tensor([expected_single_positions, expected_single_positions] )
       a_      :	Any      =							embeddings.create_position_ids_from_inputs_embeds(lowerCAmelCase_ )
       self.assertEqual(position_ids.shape							,		expected_positions.shape )
       self.assertTrue(torch.all(torch.eq(lowerCAmelCase_							,		lowerCAmelCase_ ) ) )


     @unittest.skip("""Esm does not support embedding resizing""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @unittest.skip("""Esm does not support embedding resizing""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass






     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       with torch.no_grad():
         a_      :	str      =							EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
         model.eval()
         a_      :	Dict      =							torch.tensor([[0, 1, 2, 3, 4, 5]] )
         a_      :	int      =							model(lowerCAmelCase_ )[0]

         a_      :	Optional[Any]      =							33

         a_      :	Optional[int]      =							torch.Size((1, 6, vocab_size) )
         self.assertEqual(output.shape							,		lowerCAmelCase_ )

         a_      :	List[str]      =							torch.tensor(
             [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       with torch.no_grad():
         a_      :	List[str]      =							EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
         model.eval()

         a_      :	Union[str, Any]      =							torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
         a_      :	str      =							model(lowerCAmelCase_ )[0]
         # compare the actual values for a slice.
         a_      :	Any      =							torch.tensor(
             [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )




'''simple docstring'''

import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


__snake_case:  Dict								=			logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		*lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(*lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Tuple      =							eval_examples
       a_      :	Optional[Any]      =							post_process_function
       a_      :	List[str]      =							quant_trainer_args
       a_      :	List[str]      =							1_28  # default number of calibration samples


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if calib_dataset is None and self.calib_dataset is None:
         raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
       a_      :	str      =							calib_dataset if calib_dataset is not None else self.calib_dataset

       a_      :	Dict      =							self._remove_unused_columns(lowerCAmelCase_							,		description="""Calibration""" )

       return DataLoader(
           lowerCAmelCase_							,		batch_size=self.args.eval_batch_size							,		collate_fn=self.data_collator							,		drop_last=self.args.dataloader_drop_last							,		num_workers=self.args.dataloader_num_workers							,		pin_memory=self.args.dataloader_pin_memory							,		shuffle=lowerCAmelCase_							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.train_dataset if calib_dataset is None else calib_dataset
       a_      :	int      =							self.get_calib_dataloader(lowerCAmelCase_ )

       a_      :	Dict      =							self.model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args							,		calib=lowerCAmelCase_ )
       model.eval()
       quant_trainer.enable_calibration(lowerCAmelCase_ )

       logger.info("""***** Running calibration *****""" )
       logger.info(f'''  Num examples = {self.calib_num}''' )
       logger.info(f'''  Batch size = {calib_dataloader.batch_size}''' )

       for step, inputs in enumerate(lowerCAmelCase_ ):
         # Prediction step
         a_      , a_      , a_      :	Optional[int]      =							self.prediction_step(lowerCAmelCase_							,		lowerCAmelCase_							,		prediction_loss_only=lowerCAmelCase_ )
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
           break

       quant_trainer.finish_calibration(lowerCAmelCase_							,		self.quant_trainer_args )
       a_      :	Optional[int]      =							model


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "eval" ):

       '''simple docstring'''

       a_      :	str      =							self.eval_dataset if eval_dataset is None else eval_dataset
       a_      :	List[str]      =							self.get_eval_dataloader(lowerCAmelCase_ )
       a_      :	str      =							self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	List[str]      =							self.compute_metrics
       a_      :	Tuple      =							None
       a_      :	Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	List[str]      =							eval_loop(
             lowerCAmelCase_							,		description="""Evaluation"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is not None and self.compute_metrics is not None:
         a_      :	Optional[Any]      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions )
         a_      :	Tuple      =							self.compute_metrics(lowerCAmelCase_ )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys() ):
           if not key.startswith(f'''{metric_key_prefix}_''' ):
             a_      :	Optional[int]      =							metrics.pop(lowerCAmelCase_ )

         self.log(lowerCAmelCase_ )
       else:
         a_      :	Any      =							{}

       if self.args.tpu_metrics_debug or self.args.debug:
         # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
         xm.master_print(met.metrics_report() )

       a_      :	Optional[Any]      =							self.callback_handler.on_evaluate(self.args							,		self.state							,		self.control							,		lowerCAmelCase_ )
       return metrics


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "test" ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.get_test_dataloader(lowerCAmelCase_ )

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	Any      =							self.compute_metrics
       a_      :	Dict      =							None
       a_      :	Dict      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	Dict      =							eval_loop(
             lowerCAmelCase_							,		description="""Prediction"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is None or self.compute_metrics is None:
         return output

       a_      :	Tuple      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions							,		"""predict""" )
       a_      :	List[Any]      =							self.compute_metrics(lowerCAmelCase_ )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys() ):
         if not key.startswith(f'''{metric_key_prefix}_''' ):
           a_      :	Tuple      =							metrics.pop(lowerCAmelCase_ )

       return PredictionOutput(predictions=predictions.predictions							,		label_ids=predictions.label_ids							,		metrics=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_="./" ):

       '''simple docstring'''

       a_      :	Any      =							self.eval_dataset
       a_      :	Tuple      =							self.get_eval_dataloader(lowerCAmelCase_ )

       a_      :	List[str]      =							next(iter(lowerCAmelCase_ ) )

       # saving device - to make it consistent
       a_      :	Dict      =							torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

       # convert to tuple
       a_      :	Optional[int]      =							tuple(v.to(lowerCAmelCase_ ) for k, v in batch.items() )

       logger.info("""Converting model to be onnx compatible""" )
       from pytorch_quantization.nn import TensorQuantizer

       a_      :	List[str]      =							True

       a_      :	str      =							self.model.to(lowerCAmelCase_ )

       model.eval()
       model.float()

       a_      :	Optional[int]      =							model.module if hasattr(lowerCAmelCase_							,		"""module""" ) else model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args )

       a_      :	Union[str, Any]      =							os.path.join(lowerCAmelCase_							,		"""model.onnx""" )
       logger.info(f'''exporting model to {output_model_file}''' )

       a_      :	Dict      =							{0: """batch_size""", 1: """seq_len"""}

       torch.onnx.export(
           lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		export_params=lowerCAmelCase_							,		opset_version=13							,		do_constant_folding=lowerCAmelCase_							,		input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]							,		output_names=["""output_start_logits""", """output_end_logits"""]							,		dynamic_axes={
               """input_ids""": axes,
               """attention_mask""": axes,
               """token_type_ids""": axes,
               """output_start_logits""": axes,
               """output_end_logits""": axes,
           }							,		verbose=lowerCAmelCase_							,		)
       logger.info("""onnx export finished""" )


