
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowercase							   =				logging.getLogger(__name__)







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Dict      )     ->   Tuple:
							A						   =   False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      int  ,A_     :      Any  ,A_     :      Dict  ,A_     :      Union[str, Any]      )     ->   Tuple:
							if not self.initialized:
								A						   =   RagRetriever(
								    A_  ,question_encoder_tokenizer=A_  ,generator_tokenizer=A_  ,index=A_  ,init_retrieval=A_  ,)
								A						   =   True


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   str:
							self.retriever.index.init_index()




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      int      )     ->   List[Any]:
							A   ,      A						   =   self.retriever._main_retrieve(A_  ,A_      )
							return doc_ids, retrieved_doc_embeds







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      Union[str, Any]  ,A_     :      int  ,A_     :      Any  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      str=None      )     ->   List[str]:
							if index is not None and index.is_initialized() and len(A_      ) > 0:
								raise ValueError(
								    'When using Ray for distributed fine-tuning, '
								    'you\'ll need to provide the paths instead, '
								    'as the dataset and the index are loaded '
								    'separately. More info in examples/rag/use_own_knowledge_dataset.py '      )
							super().__init__(
							    A_  ,question_encoder_tokenizer=A_  ,generator_tokenizer=A_  ,index=A_  ,init_retrieval=A_  ,)
							A						   =   retrieval_workers
							if len(self.retrieval_workers      ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(A_  ,A_  ,A_  ,A_      )
								        for worker in self.retrieval_workers
								    ]      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							logger.info('initializing retrieval'      )

							if len(self.retrieval_workers      ) > 0:
								ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
							else:
								# Non-distributed training. Load index into this same process.
								self.index.init_index()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      int      )     ->   Optional[Any]:
							if len(self.retrieval_workers      ) > 0:
								# Select a random retrieval actor.
								A						   =   self.retrieval_workers[random.randint(0  ,len(self.retrieval_workers      ) - 1      )]
								A   ,      A						   =   ray.get(random_worker.retrieve.remote(A_  ,A_      )      )
							else:
								A   ,      A						   =   self._main_retrieve(A_  ,A_      )
							return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_      )


						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      Any  ,A_     :      List[str]  ,A_     :      Optional[int]=None  ,**A_     :      Any      )     ->   str:
							return super(A_  ,cls      ).get_tokenizers(A_  ,A_  ,**A_      )




						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      Any  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      int=None  ,**A_     :      List[str]      )     ->   Union[str, Any]:
							A						   =   kwargs.pop('config'  ,A_      ) or RagConfig.from_pretrained(A_  ,**A_      )
							A						   =   RagTokenizer.from_pretrained(A_  ,config=A_      )
							A						   =   rag_tokenizer.question_encoder
							A						   =   rag_tokenizer.generator
							if indexed_dataset is not None:
								A						   =   'custom'
								A						   =   CustomHFIndex(config.retrieval_vector_size  ,A_      )
							else:
								A						   =   cls._build_index(A_      )
							return cls(
							    A_  ,question_encoder_tokenizer=A_  ,generator_tokenizer=A_  ,retrieval_workers=A_  ,index=A_  ,)

"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy
		import tensorflow as tf

		from transformers.models.esm.modeling_tf_esm import (
		    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFEsmForMaskedLM,
		    TFEsmForSequenceClassification,
		    TFEsmForTokenClassification,
		    TFEsmModel,
		)







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      Optional[Any]  ,)     ->   Optional[int]:
							A						   =   parent
							A						   =   13
							A						   =   7
							A						   =   True
							A						   =   True
							A						   =   True
							A						   =   99
							A						   =   32
							A						   =   2
							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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Union[str, Any]:
							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
							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						   =   EsmConfig(
							    vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,pad_token_id=1  ,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  ,)

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Dict:
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   self.prepare_config_and_inputs()

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

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      int  ,A_     :      List[str]  ,A_     :      Optional[int]  ,A_     :      List[Any]  ,A_     :      Any  ,A_     :      Any      )     ->   Dict:
							A						   =   TFEsmModel(config=A_      )
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							A						   =   model(A_      )

							A						   =   [input_ids, input_mask]
							A						   =   model(A_      )

							A						   =   model(A_      )

							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      List[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,)     ->   Optional[int]:
							A						   =   True

							A						   =   TFEsmModel(config=A_      )
							A						   =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'encoder_hidden_states': encoder_hidden_states,
							    'encoder_attention_mask': encoder_attention_mask,
							}
							A						   =   model(A_      )

							A						   =   [input_ids, input_mask]
							A						   =   model(A_  ,encoder_hidden_states=A_      )

							# Also check the case where encoder outputs are not passed
							A						   =   model(A_  ,attention_mask=A_      )

							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[Any]      )     ->   Dict:
							A						   =   TFEsmForMaskedLM(config=A_      )
							A						   =   model([input_ids, input_mask]      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      List[Any]  ,A_     :      int  ,A_     :      Tuple  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							A						   =   self.num_labels
							A						   =   TFEsmForTokenClassification(config=A_      )
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							A						   =   model(A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    (
						    (
						        TFEsmModel,
						        TFEsmForMaskedLM,
						        TFEsmForSequenceClassification,
						        TFEsmForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCamelCase:    List[str]      =    (
						    {
						        '''feature-extraction''': TFEsmModel,
						        '''fill-mask''': TFEsmForMaskedLM,
						        '''text-classification''': TFEsmForSequenceClassification,
						        '''token-classification''': TFEsmForTokenClassification,
						        '''zero-shot''': TFEsmForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowerCamelCase:    Union[str, Any]      =    False
						_lowerCamelCase:    List[Any]      =    False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
							A						   =   TFEsmModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,hidden_size=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[int]:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[str]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*A_      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
							for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   TFEsmModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )


						@unittest.skip('Protein models do not support embedding resizing.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[int]:
							pass


						@unittest.skip('Protein models do not support embedding resizing.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Any:
							pass




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A						   =   model_class(A_      )
								assert isinstance(model.get_input_embeddings()  ,tf.keras.layers.Layer      )
								if model_class is TFEsmForMaskedLM:
									# Output embedding test differs from the main test because they're a matrix, not a layer
									A						   =   model.get_bias()
									assert isinstance(A_  ,A_      )
									for k, v in name.items():
										assert isinstance(A_  ,tf.Variable      )
								else:
									A						   =   model.get_output_embeddings()
									assert x is None
									A						   =   model.get_bias()
									assert name is None







@require_tf
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'      )

							A						   =   tf.constant([[0, 1, 2, 3, 4, 5]]      )
							A						   =   model(A_      )[0]
							A						   =   [1, 6, 33]
							self.assertEqual(list(output.numpy().shape      )  ,A_      )
							# compare the actual values for a slice.
							A						   =   tf.constant(
							    [
							        [
							            [8.92_15_18, -10.58_98_14, -6.4_67_13_07],
							            [-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
							            [-7.78_12_47, -13.95_15_57, -3.74_05_92],
							        ]
							    ]      )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1e-2      )      )




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   int:
							A						   =   TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'      )

							A						   =   tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
							A						   =   model(A_      )[0]
							# compare the actual values for a slice.
							A						   =   tf.constant(
							    [
							        [
							            [0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
							            [0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
							            [0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
							        ]
							    ]      )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1e-4      )      )