





'''simple docstring'''


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel

class    lowerCAmelCase_		:




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




	def 							_snake_case   (	self		)    ->							int:
					_lowerCAmelCase				=      ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size		)

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

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

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

					_lowerCAmelCase				=      self.get_config()

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




	def 							_snake_case   (	self		)    ->							Any:
					return OpenLlamaConfig(
					    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			,     use_stable_embedding=a_			,     )




	def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[Any]:
					_lowerCAmelCase				=      OpenLlamaModel(config=a_		)
					model.to(a_		)
					model.eval()
					_lowerCAmelCase				=      model(a_			,     attention_mask=a_		)
					_lowerCAmelCase				=      model(a_		)
					self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)




	def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     )    ->							List[str]:
					_lowerCAmelCase				=      True
					_lowerCAmelCase				=      OpenLlamaModel(a_		)
					model.to(a_		)
					model.eval()
					_lowerCAmelCase				=      model(
					    a_			,     attention_mask=a_			,     encoder_hidden_states=a_			,     encoder_attention_mask=a_			,     )
					_lowerCAmelCase				=      model(
					    a_			,     attention_mask=a_			,     encoder_hidden_states=a_			,     )
					_lowerCAmelCase				=      model(a_			,     attention_mask=a_		)
					self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)




	def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     )    ->							str:
					_lowerCAmelCase				=      OpenLlamaForCausalLM(config=a_		)
					model.to(a_		)
					model.eval()
					_lowerCAmelCase				=      model(a_			,     attention_mask=a_			,     labels=a_		)
					self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)		)




	def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     )    ->							int:
					_lowerCAmelCase				=      True
					_lowerCAmelCase				=      True
					_lowerCAmelCase				=      OpenLlamaForCausalLM(config=a_		)
					model.to(a_		)
					model.eval()

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

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

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

					_lowerCAmelCase				=      model(
					    a_			,     attention_mask=a_			,     encoder_hidden_states=a_			,     encoder_attention_mask=a_			,     output_hidden_states=a_			,     )["hidden_states"][0]
					_lowerCAmelCase				=      model(
					    a_			,     attention_mask=a_			,     encoder_hidden_states=a_			,     encoder_attention_mask=a_			,     past_key_values=a_			,     output_hidden_states=a_			,     )["hidden_states"][0]

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





	def 							_snake_case   (	self		)    ->							Optional[int]:
					_lowerCAmelCase				=      self.prepare_config_and_inputs()
					(
					    _lowerCAmelCase
					)				=      config_and_inputs
					_lowerCAmelCase				=      {"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict







@require_torch
class    lowerCAmelCase_		(		__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase						,unittest.TestCase		):
	__lowerCamelCase						:							Union[str, Any]         =					(
	    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
	)
	__lowerCamelCase						:							Dict         =					(OpenLlamaForCausalLM,) if is_torch_available() else ()
	__lowerCamelCase						:							Tuple         =					(
	    {
	        '''feature-extraction''': OpenLlamaModel,
	        '''text-classification''': OpenLlamaForSequenceClassification,
	        '''text-generation''': OpenLlamaForCausalLM,
	        '''zero-shot''': OpenLlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	__lowerCamelCase						:							Dict         =					False
	__lowerCamelCase						:							str         =					False




	def 							_snake_case   (	self		)    ->							List[str]:
					_lowerCAmelCase				=      OpenLlamaModelTester(self		)
					_lowerCAmelCase				=      ConfigTester(self			,     config_class=a_			,     hidden_size=37		)




	def 							_snake_case   (	self		)    ->							Tuple:
					self.config_tester.run_common_tests()




	def 							_snake_case   (	self		)    ->							Optional[Any]:
					_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_		)




	def 							_snake_case   (	self		)    ->							Dict:
					_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									_lowerCAmelCase				=      type
									self.model_tester.create_and_check_model(*a_		)




	def 							_snake_case   (	self		)    ->							Optional[int]:
					_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
					_lowerCAmelCase				=      3
					_lowerCAmelCase				=      input_dict["input_ids"]
					_lowerCAmelCase				=      input_ids.ne(1		).to(a_		)
					_lowerCAmelCase				=      ids_tensor([self.model_tester.batch_size]			,     self.model_tester.type_sequence_label_size		)
					_lowerCAmelCase				=      OpenLlamaForSequenceClassification(a_		)
					model.to(a_		)
					model.eval()
					_lowerCAmelCase				=      model(a_			,     attention_mask=a_			,     labels=a_		)
					self.assertEqual(result.logits.shape			,     (self.model_tester.batch_size, self.model_tester.num_labels)		)




	def 							_snake_case   (	self		)    ->							List[Any]:
					_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
					_lowerCAmelCase				=      3
					_lowerCAmelCase				=      "single_label_classification"
					_lowerCAmelCase				=      input_dict["input_ids"]
					_lowerCAmelCase				=      input_ids.ne(1		).to(a_		)
					_lowerCAmelCase				=      ids_tensor([self.model_tester.batch_size]			,     self.model_tester.type_sequence_label_size		)
					_lowerCAmelCase				=      OpenLlamaForSequenceClassification(a_		)
					model.to(a_		)
					model.eval()
					_lowerCAmelCase				=      model(a_			,     attention_mask=a_			,     labels=a_		)
					self.assertEqual(result.logits.shape			,     (self.model_tester.batch_size, self.model_tester.num_labels)		)




	def 							_snake_case   (	self		)    ->							Optional[Any]:
					_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
					_lowerCAmelCase				=      3
					_lowerCAmelCase				=      "multi_label_classification"
					_lowerCAmelCase				=      input_dict["input_ids"]
					_lowerCAmelCase				=      input_ids.ne(1		).to(a_		)
					_lowerCAmelCase				=      ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]			,     self.model_tester.type_sequence_label_size		).to(torch.float		)
					_lowerCAmelCase				=      OpenLlamaForSequenceClassification(a_		)
					model.to(a_		)
					model.eval()
					_lowerCAmelCase				=      model(a_			,     attention_mask=a_			,     labels=a_		)
					self.assertEqual(result.logits.shape			,     (self.model_tester.batch_size, self.model_tester.num_labels)		)




	@unittest.skip("Open-Llama buffers include complex numbers, which breaks this test"		)
	def 							_snake_case   (	self		)    ->							Optional[int]:
					pass





	@parameterized.expand([("linear",), ("dynamic",)]		)
	def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Dict:
					_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
					_lowerCAmelCase				=      ids_tensor([1, 10]			,     config.vocab_size		)
					_lowerCAmelCase				=      ids_tensor([1, int(config.max_position_embeddings * 1.5		)]			,     config.vocab_size		)

					set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
					_lowerCAmelCase				=      OpenLlamaModel(a_		)
					original_model.to(a_		)
					original_model.eval()
					_lowerCAmelCase				=      original_model(a_		).last_hidden_state
					_lowerCAmelCase				=      original_model(a_		).last_hidden_state

					set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
					_lowerCAmelCase				=      {"type": scaling_type, "factor": 10.0}
					_lowerCAmelCase				=      OpenLlamaModel(a_		)
					scaled_model.to(a_		)
					scaled_model.eval()
					_lowerCAmelCase				=      scaled_model(a_		).last_hidden_state
					_lowerCAmelCase				=      scaled_model(a_		).last_hidden_state

					# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
					# maximum sequence length, so the outputs for the short input should match.
					if scaling_type == "dynamic":
									self.assertTrue(torch.allclose(a_			,     a_			,     atol=1E-5		)		)
					else:
									self.assertFalse(torch.allclose(a_			,     a_			,     atol=1E-5		)		)

					# The output should be different for long inputs
					self.assertFalse(torch.allclose(a_			,     a_			,     atol=1E-5		)		)









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


