
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        lowercase      :



		def __init__(				self       ,   A_       ,   A_=13       ,   A_=7       ,   A_=True       ,   A_=True       ,   A_=False       ,   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       ,   )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  seq_length
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_input_mask
				UpperCamelCase						=  use_token_type_ids
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  type_sequence_label_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  num_labels
				UpperCamelCase						=  num_choices
				UpperCamelCase						=  scope



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size )

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

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

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

				UpperCamelCase						=  self.get_config()

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



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  OpenLlamaModel(config=A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  model(A_       ,   attention_mask=A_ )
				UpperCamelCase						=  model(A_ )
				self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  True
				UpperCamelCase						=  OpenLlamaModel(A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  model(
				    A_       ,   attention_mask=A_       ,   encoder_hidden_states=A_       ,   encoder_attention_mask=A_       ,   )
				UpperCamelCase						=  model(
				    A_       ,   attention_mask=A_       ,   encoder_hidden_states=A_       ,   )
				UpperCamelCase						=  model(A_       ,   attention_mask=A_ )
				self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  OpenLlamaForCausalLM(config=A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  model(A_       ,   attention_mask=A_       ,   labels=A_ )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  True
				UpperCamelCase						=  True
				UpperCamelCase						=  OpenLlamaForCausalLM(config=A_ )
				model.to(A_ )
				model.eval()

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

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

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

				UpperCamelCase						=  model(
				    A_       ,   attention_mask=A_       ,   encoder_hidden_states=A_       ,   encoder_attention_mask=A_       ,   output_hidden_states=A_       ,   )['hidden_states'][0]
				UpperCamelCase						=  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
				UpperCamelCase						=  ids_tensor((1,)       ,   output_from_past.shape[-1] ).item()
				UpperCamelCase						=  output_from_no_past[:, -3:, random_slice_idx].detach()
				UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						
				)						=  config_and_inputs
				UpperCamelCase						=  {'input_ids': input_ids, 'attention_mask': input_mask}
				return config, inputs_dict






@require_torch
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[Any]			  =       (
		    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
		)
		__lowercase : str			  =       (OpenLlamaForCausalLM,) if is_torch_available() else ()
		__lowercase : List[str]			  =       (
		    {
		        "feature-extraction": OpenLlamaModel,
		        "text-classification": OpenLlamaForSequenceClassification,
		        "text-generation": OpenLlamaForCausalLM,
		        "zero-shot": OpenLlamaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowercase : Any			  =       False
		__lowercase : Dict			  =       False



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  OpenLlamaModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=A_       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						UpperCamelCase						=  type
						self.model_tester.create_and_check_model(*A_ )



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



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  3
				UpperCamelCase						=  'single_label_classification'
				UpperCamelCase						=  input_dict['input_ids']
				UpperCamelCase						=  input_ids.ne(1 ).to(A_ )
				UpperCamelCase						=  ids_tensor([self.model_tester.batch_size]       ,   self.model_tester.type_sequence_label_size )
				UpperCamelCase						=  OpenLlamaForSequenceClassification(A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  model(A_       ,   attention_mask=A_       ,   labels=A_ )
				self.assertEqual(result.logits.shape       ,   (self.model_tester.batch_size, self.model_tester.num_labels) )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  3
				UpperCamelCase						=  'multi_label_classification'
				UpperCamelCase						=  input_dict['input_ids']
				UpperCamelCase						=  input_ids.ne(1 ).to(A_ )
				UpperCamelCase						=  ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]       ,   self.model_tester.type_sequence_label_size ).to(torch.float )
				UpperCamelCase						=  OpenLlamaForSequenceClassification(A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				pass



		@parameterized.expand([('linear',), ('dynamic',)] )
		def __UpperCamelCase				(				self       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  ids_tensor([1, 10]       ,   config.vocab_size )
				UpperCamelCase						=  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
				UpperCamelCase						=  OpenLlamaModel(A_ )
				original_model.to(A_ )
				original_model.eval()
				UpperCamelCase						=  original_model(A_ ).last_hidden_state
				UpperCamelCase						=  original_model(A_ ).last_hidden_state

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


from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   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_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

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






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



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