


'''simple docstring'''


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 	lowerCAmelCase__			(			UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__	):
			lowerCAmelCase :	Optional[int]          =     [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]




			@register_to_config
			def __init__(		self  :     Optional[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     List[str] = None	,  lowerCamelCase__  :     Optional[int] = 5_02_57	,  lowerCamelCase__  :     str = 10_24	,  lowerCamelCase__  :     List[Any] = 7_68	,  lowerCamelCase__  :     int = 12	,  lowerCamelCase__  :     Optional[int] = 12	,  lowerCamelCase__  :     Optional[int] = None	,  lowerCamelCase__  :     Optional[int] = "gelu_new"	,  lowerCamelCase__  :     List[Any] = 0.1	,  lowerCamelCase__  :     Optional[Any] = 0.1	,  lowerCamelCase__  :     Optional[Any] = 0.1	,  lowerCamelCase__  :     Optional[int] = 1E-5	,  lowerCamelCase__  :     List[str] = 0.0_2	,  lowerCamelCase__  :     Optional[Any] = True	,  lowerCamelCase__  :     List[str] = True	,  lowerCamelCase__  :     Optional[Any] = False	,  lowerCamelCase__  :     int = False	,  )   ->Tuple:



					'''simple docstring'''







					super().__init__()

					_UpperCAmelCase		:       int								=      prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
							raise ValueError(
							    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
							    F""" `n_embd`: {n_embd} are not equal.""" )

					_UpperCAmelCase		:       Union[str, Any]								=      prefix_inner_dim
					_UpperCAmelCase		:       Tuple								=      prefix_hidden_dim

					_UpperCAmelCase		:       Union[str, Any]								=      (
					    nn.Linear(self.prefix_inner_dim	,  self.prefix_hidden_dim )
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					_UpperCAmelCase		:       Dict								=      (
					    nn.Linear(self.prefix_hidden_dim	,  lowerCamelCase__ ) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					_UpperCAmelCase		:       Optional[int]								=      GPTaConfig(
					    vocab_size=lowerCamelCase__	,  n_positions=lowerCamelCase__	,  n_embd=lowerCamelCase__	,  n_layer=lowerCamelCase__	,  n_head=lowerCamelCase__	,  n_inner=lowerCamelCase__	,  activation_function=lowerCamelCase__	,  resid_pdrop=lowerCamelCase__	,  embd_pdrop=lowerCamelCase__	,  attn_pdrop=lowerCamelCase__	,  layer_norm_epsilon=lowerCamelCase__	,  initializer_range=lowerCamelCase__	,  scale_attn_weights=lowerCamelCase__	,  use_cache=lowerCamelCase__	,  scale_attn_by_inverse_layer_idx=lowerCamelCase__	,  reorder_and_upcast_attn=lowerCamelCase__	,  )
					_UpperCAmelCase		:       int								=      GPTaLMHeadModel(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int = None	,  lowerCamelCase__  :     Optional[Any] = None	,  )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      self.transformer.transformer.wte(lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      self.encode_prefix(lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      self.decode_prefix(lowerCamelCase__ )
					_UpperCAmelCase		:       int								=      torch.cat((prefix_embeds, embedding_text)	,  dim=1 )

					if labels is not None:
							_UpperCAmelCase		:       str								=      self.get_dummy_token(input_ids.shape[0]	,  input_ids.device )
							_UpperCAmelCase		:       List[Any]								=      torch.cat((dummy_token, input_ids)	,  dim=1 )
					_UpperCAmelCase		:       Any								=      self.transformer(inputs_embeds=lowerCamelCase__	,  labels=lowerCamelCase__	,  attention_mask=lowerCamelCase__ )
					if self.prefix_hidden_dim is not None:
							return out, hidden
					else:
							return out




			def 							lowerCAmelCase__    (		self  :     Dict	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     List[str] )   ->torch.Tensor:



					'''simple docstring'''







					return torch.zeros(lowerCamelCase__	,  self.prefix_length	,  dtype=torch.intaa	,  device=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     List[Any] )   ->List[Any]:



					'''simple docstring'''







					return self.encode_prefix(lowerCamelCase__ )




			@torch.no_grad()
			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     Tuple )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      torch.split(lowerCamelCase__	,  1	,  dim=0 )
					_UpperCAmelCase		:       Optional[Any]								=      []
					_UpperCAmelCase		:       Optional[int]								=      []
					for feature in features:
							_UpperCAmelCase		:       Tuple								=      self.decode_prefix(feature.to(lowerCamelCase__ ) )  # back to the clip feature
							# Only support beam search for now
							_UpperCAmelCase					,    _UpperCAmelCase		:       List[Any]								=      self.generate_beam(
							    input_embeds=lowerCamelCase__	,  device=lowerCamelCase__	,  eos_token_id=lowerCamelCase__ )
							generated_tokens.append(output_tokens[0] )
							generated_seq_lengths.append(seq_lengths[0] )
					_UpperCAmelCase		:       Any								=      torch.stack(lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      torch.stack(lowerCamelCase__ )
					return generated_tokens, generated_seq_lengths






			@torch.no_grad()
			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     str=None	,  lowerCamelCase__  :     int=None	,  lowerCamelCase__  :     Optional[Any]=None	,  lowerCamelCase__  :     int = 5	,  lowerCamelCase__  :     Optional[int] = 67	,  lowerCamelCase__  :     Optional[Any] = 1.0	,  lowerCamelCase__  :     Tuple = None	,  )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      eos_token_id
					_UpperCAmelCase		:       Optional[Any]								=      None
					_UpperCAmelCase		:       str								=      None
					_UpperCAmelCase		:       Union[str, Any]								=      torch.ones(lowerCamelCase__	,  device=lowerCamelCase__	,  dtype=torch.int )
					_UpperCAmelCase		:       List[Any]								=      torch.zeros(lowerCamelCase__	,  device=lowerCamelCase__	,  dtype=torch.bool )

					if input_embeds is not None:
							_UpperCAmelCase		:       List[Any]								=      input_embeds
					else:
							_UpperCAmelCase		:       List[Any]								=      self.transformer.transformer.wte(lowerCamelCase__ )

					for i in range(lowerCamelCase__ ):
							_UpperCAmelCase		:       Union[str, Any]								=      self.transformer(inputs_embeds=lowerCamelCase__ )
							_UpperCAmelCase		:       Any								=      outputs.logits
							_UpperCAmelCase		:       Tuple								=      logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
							_UpperCAmelCase		:       Optional[Any]								=      logits.softmax(-1 ).log()

							if scores is None:
									_UpperCAmelCase					,    _UpperCAmelCase		:       int								=      logits.topk(lowerCamelCase__	,  -1 )
									_UpperCAmelCase		:       str								=      generated.expand(lowerCamelCase__	,  *generated.shape[1:] )
									_UpperCAmelCase					,    _UpperCAmelCase		:       int								=      next_tokens.permute(1	,  0 ), scores.squeeze(0 )
									if tokens is None:
											_UpperCAmelCase		:       Union[str, Any]								=      next_tokens
									else:
											_UpperCAmelCase		:       Union[str, Any]								=      tokens.expand(lowerCamelCase__	,  *tokens.shape[1:] )
											_UpperCAmelCase		:       Optional[int]								=      torch.cat((tokens, next_tokens)	,  dim=1 )
							else:
									_UpperCAmelCase		:       str								=      -float(np.inf )
									_UpperCAmelCase		:       Dict								=      0
									_UpperCAmelCase		:       Tuple								=      scores[:, None] + logits
									seq_lengths[~is_stopped] += 1
									_UpperCAmelCase		:       Tuple								=      scores_sum / seq_lengths[:, None]
									_UpperCAmelCase					,    _UpperCAmelCase		:       int								=      scores_sum_average.view(-1 ).topk(lowerCamelCase__	,  -1 )
									_UpperCAmelCase		:       str								=      next_tokens // scores_sum.shape[1]
									_UpperCAmelCase		:       int								=      seq_lengths[next_tokens_source]
									_UpperCAmelCase		:       Optional[int]								=      next_tokens % scores_sum.shape[1]
									_UpperCAmelCase		:       List[str]								=      next_tokens.unsqueeze(1 )
									_UpperCAmelCase		:       Dict								=      tokens[next_tokens_source]
									_UpperCAmelCase		:       Optional[int]								=      torch.cat((tokens, next_tokens)	,  dim=1 )
									_UpperCAmelCase		:       Dict								=      generated[next_tokens_source]
									_UpperCAmelCase		:       Dict								=      scores_sum_average * seq_lengths
									_UpperCAmelCase		:       Optional[Any]								=      is_stopped[next_tokens_source]

							_UpperCAmelCase		:       List[str]								=      self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0]	,  1	,  -1 )
							_UpperCAmelCase		:       int								=      torch.cat((generated, next_token_embed)	,  dim=1 )
							_UpperCAmelCase		:       int								=      is_stopped + next_tokens.eq(lowerCamelCase__ ).squeeze()
							if is_stopped.all():
									break

					_UpperCAmelCase		:       Union[str, Any]								=      scores / seq_lengths
					_UpperCAmelCase		:       Tuple								=      scores.argsort(descending=lowerCamelCase__ )
					# tokens tensors are already padded to max_seq_length
					_UpperCAmelCase		:       List[Any]								=      [tokens[i] for i in order]
					_UpperCAmelCase		:       Dict								=      torch.stack(lowerCamelCase__	,  dim=0 )
					_UpperCAmelCase		:       List[Any]								=      torch.tensor([seq_lengths[i] for i in order]	,  dtype=seq_lengths.dtype )
					return output_texts, seq_lengths






'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						)






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def __init__(		self  :     int	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str=13	,  lowerCamelCase__  :     Dict=7	,  lowerCamelCase__  :     str=True	,  lowerCamelCase__  :     List[str]=True	,  lowerCamelCase__  :     Dict=True	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     Tuple=99	,  lowerCamelCase__  :     Optional[int]=32	,  lowerCamelCase__  :     str=5	,  lowerCamelCase__  :     List[Any]=4	,  lowerCamelCase__  :     Any=37	,  lowerCamelCase__  :     List[Any]="gelu"	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     Tuple=0.1	,  lowerCamelCase__  :     Optional[int]=5_12	,  lowerCamelCase__  :     Any=16	,  lowerCamelCase__  :     Optional[Any]=2	,  lowerCamelCase__  :     Optional[Any]=0.0_2	,  lowerCamelCase__  :     Optional[int]=4	,  )   ->List[str]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







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

					_UpperCAmelCase		:       Any								=      None
					if self.use_attention_mask:
							_UpperCAmelCase		:       Optional[Any]								=      random_attention_mask([self.batch_size, self.seq_length] )

					_UpperCAmelCase		:       int								=      None
					if self.use_token_type_ids:
							_UpperCAmelCase		:       List[Any]								=      ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size )

					_UpperCAmelCase		:       Tuple								=      RobertaPreLayerNormConfig(
					    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	,  )

					return config, input_ids, token_type_ids, attention_mask




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      config_and_inputs
					_UpperCAmelCase		:       Optional[Any]								=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict






			def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      config_and_inputs

					_UpperCAmelCase		:       List[Any]								=      True
					_UpperCAmelCase		:       List[str]								=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					_UpperCAmelCase		:       int								=      ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2 )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Tuple          =     True

			lowerCAmelCase :	Tuple          =     (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      FlaxRobertaPreLayerNormModelTester(self )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->int:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase		:       Any								=      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      model(np.ones((1, 1) ) )
							self.assertIsNotNone(lowerCamelCase__ )






@require_flax
class 	lowerCAmelCase__			(			unittest.TestCase	):




			@slow
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       str								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Tuple								=      model(lowerCamelCase__ )[0]
					_UpperCAmelCase		:       int								=      [1, 11, 5_02_65]
					self.assertEqual(list(output.shape )	,  lowerCamelCase__ )
					# compare the actual values for a slice.
					_UpperCAmelCase		:       int								=      np.array(
					    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       List[Any]								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Optional[Any]								=      model(lowerCamelCase__ )[0]
					# compare the actual values for a slice.
					_UpperCAmelCase		:       str								=      np.array(
					    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )



