





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_ = 256			,  lowerCAmelCase_ = 20_00.0			,  lowerCAmelCase_ = 768			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 2048			,  lowerCAmelCase_ = 0.1			,  )						->							Union[str, Any]:
										super().__init__()

										_snake_case													=			nn.Sequential(
										    nn.Linear(lowerCAmelCase_			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  nn.Linear(d_model * 4			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  )

										_snake_case													=			nn.Embedding(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			False

										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)

										_snake_case													=			nn.ModuleList()
										for lyr_num in range(lowerCAmelCase_					):
															# FiLM conditional T5 decoder
															_snake_case													=			DecoderLayer(d_model=lowerCAmelCase_			,  d_kv=lowerCAmelCase_			,  num_heads=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
															self.decoders.append(lowerCAmelCase_					)

										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			torch.mul(query_input.unsqueeze(-1					)			,  key_input.unsqueeze(-2					)					)
										return mask.unsqueeze(-3					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case							,       _snake_case							,       _snake_case													=			decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

										# decoder_noise_time is in [0, 1), so rescale to expected timing range.
										_snake_case													=			get_timestep_embedding(
										    decoder_noise_time * self.config.max_decoder_noise_time			,  embedding_dim=self.config.d_model			,  max_period=self.config.max_decoder_noise_time			,  ).to(dtype=self.dtype					)

										_snake_case													=			self.conditioning_emb(lowerCAmelCase_					).unsqueeze(1					)

										assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

										_snake_case													=			decoder_input_tokens.shape[1]

										# If we want to use relative positions for audio context, we can just offset
										# this sequence by the length of encodings_and_masks.
										_snake_case													=			torch.broadcast_to(
										    torch.arange(lowerCAmelCase_			,  device=decoder_input_tokens.device					)			,  (batch, seq_length)			,  )

										_snake_case													=			self.position_encoding(lowerCAmelCase_					)

										_snake_case													=			self.continuous_inputs_projection(lowerCAmelCase_					)
										inputs += position_encodings
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										# decoder: No padding present.
										_snake_case													=			torch.ones(
										    decoder_input_tokens.shape[:2]			,  device=decoder_input_tokens.device			,  dtype=inputs.dtype					)

										# Translate encoding masks to encoder-decoder masks.
										_snake_case													=			[(x, self.encoder_decoder_mask(lowerCAmelCase_			,  lowerCAmelCase_					)) for x, y in encodings_and_masks]

										# cross attend style: concat encodings
										_snake_case													=			torch.cat([x[0] for x in encodings_and_encdec_masks]			,  dim=1					)
										_snake_case													=			torch.cat([x[1] for x in encodings_and_encdec_masks]			,  dim=-1					)

										for lyr in self.decoders:
															_snake_case													=			lyr(
															    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  encoder_attention_mask=lowerCAmelCase_			,  )[0]

										_snake_case													=			self.decoder_norm(lowerCAmelCase_					)
										_snake_case													=			self.post_dropout(lowerCAmelCase_					)

										_snake_case													=			self.spec_out(lowerCAmelCase_					)
										return spec_out





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							Tuple:
										super().__init__()
										_snake_case													=			nn.ModuleList()

										# cond self attention: layer 0
										self.layer.append(
										    TaLayerSelfAttentionCond(d_model=lowerCAmelCase_			,  d_kv=lowerCAmelCase_			,  num_heads=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)					)

										# cross attention: layer 1
										self.layer.append(
										    TaLayerCrossAttention(
										        d_model=lowerCAmelCase_			,  d_kv=lowerCAmelCase_			,  num_heads=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_			,  layer_norm_epsilon=lowerCAmelCase_			,  )					)

										# Film Cond MLP + dropout: last layer
										self.layer.append(
										    TaLayerFFCond(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_			,  layer_norm_epsilon=lowerCAmelCase_					)					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Tuple:
										_snake_case													=			self.layer[0](
										    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

										if encoder_hidden_states is not None:
															_snake_case													=			torch.where(encoder_attention_mask > 0			,  0			,  -1E10					).to(
															    encoder_hidden_states.dtype					)

															_snake_case													=			self.layer[1](
															    lowerCAmelCase_			,  key_value_states=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

										# Apply Film Conditional Feed Forward layer
										_snake_case													=			self.layer[-1](lowerCAmelCase_			,  lowerCAmelCase_					)

										return (hidden_states,)





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										super().__init__()
										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							str:
										# pre_self_attention_layer_norm
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)

										if conditioning_emb is not None:
															_snake_case													=			self.FiLMLayer(lowerCAmelCase_			,  lowerCAmelCase_					)

										# Self-attention block
										_snake_case													=			self.attention(lowerCAmelCase_					)

										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)

										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										super().__init__()
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Dict:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										_snake_case													=			self.attention(
										    lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  attention_mask=attention_mask.squeeze(1					)			,  )
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return layer_output





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										super().__init__()
										_snake_case													=			TaDenseGatedActDense(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Union[str, Any]:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										if conditioning_emb is not None:
															_snake_case													=			self.film(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.DenseReluDense(lowerCAmelCase_					)
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)
										_snake_case													=			NewGELUActivation()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.act(self.wi_a(lowerCAmelCase_					)					)
										_snake_case													=			self.wi_a(lowerCAmelCase_					)
										_snake_case													=			hidden_gelu * hidden_linear
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										_snake_case													=			self.wo(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							str:
										super().__init__()
										_snake_case													=			nn.Parameter(torch.ones(lowerCAmelCase_					)					)
										_snake_case													=			eps






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
										# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
										# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
										# half-precision inputs is done in fp32

										_snake_case													=			hidden_states.to(torch.floataa					).pow(2					).mean(-1			,  keepdim=lowerCAmelCase_					)
										_snake_case													=			hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

										# convert into half-precision if necessary
										if self.weight.dtype in [torch.floataa, torch.bfloataa]:
															_snake_case													=			hidden_states.to(self.weight.dtype					)

										return self.weight * hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							torch.Tensor:
										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase_			,  3.0					))					))





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  out_features * 2			,  bias=lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			self.scale_bias(lowerCAmelCase_					)
										_snake_case							,       _snake_case													=			torch.chunk(lowerCAmelCase_			,  2			,  -1					)
										_snake_case													=			x * (1 + scale) + shift
										return x










from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		'''gpt_neo'''
					lowerCAmelCase_							=		['''past_key_values''']
					lowerCAmelCase_							=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




					def __init__(						self			,  lowerCAmelCase_=5_0257			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=24			,  lowerCAmelCase_=[[["global", "local"], 12]]			,  lowerCAmelCase_=16			,  lowerCAmelCase_=None			,  lowerCAmelCase_=256			,  lowerCAmelCase_="gelu_new"			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.1			,  lowerCAmelCase_=1E-5			,  lowerCAmelCase_=0.02			,  lowerCAmelCase_=True			,  lowerCAmelCase_=5_0256			,  lowerCAmelCase_=5_0256			,  **lowerCAmelCase_			,  )						->							Tuple:
										_snake_case													=			vocab_size
										_snake_case													=			max_position_embeddings
										_snake_case													=			hidden_size
										_snake_case													=			num_layers
										_snake_case													=			num_heads
										_snake_case													=			intermediate_size
										_snake_case													=			window_size
										_snake_case													=			activation_function
										_snake_case													=			resid_dropout
										_snake_case													=			embed_dropout
										_snake_case													=			attention_dropout
										_snake_case													=			classifier_dropout
										_snake_case													=			layer_norm_epsilon
										_snake_case													=			initializer_range
										_snake_case													=			use_cache

										_snake_case													=			bos_token_id
										_snake_case													=			eos_token_id

										_snake_case													=			attention_types
										_snake_case													=			self.expand_attention_types_params(lowerCAmelCase_					)

										if len(self.attention_layers					) != self.num_layers:
															raise ValueError(
															    'Configuration for convolutional module is incorrect. '
															    'It is required that `len(config.attention_layers)` == `config.num_layers` '
															    F'''but is `len(config.attention_layers) = {len(self.attention_layers					)}`, '''
															    F'''`config.num_layers = {self.num_layers}`. '''
															    '`config.attention_layers` is prepared using `config.attention_types`. '
															    'Please verify the value of `config.attention_types` argument.'					)

										super().__init__(bos_token_id=lowerCAmelCase_			,  eos_token_id=lowerCAmelCase_			,  **lowerCAmelCase_					)






					@staticmethod
					def 		lowerCAmelCase     (						lowerCAmelCase_					)						->							Any:
										_snake_case													=			[]
										for item in attention_types:
															for _ in range(item[1]					):
																				attentions.extend(item[0]					)
										return attentions





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Any:



					'''simple docstring'''




					import torch

					_snake_case													=			input.size()
					_snake_case													=			len(UpperCamelCase__  )
					_snake_case													=			shape[dimension]

					_snake_case													=			torch.arange(0    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					_snake_case													=			torch.div(sizedim - size    ,      UpperCamelCase__    ,      rounding_mode='floor'  ) + 1
					_snake_case													=			torch.arange(UpperCamelCase__  ) + low_indices[:min_length][:, None]

					_snake_case													=			[slice(UpperCamelCase__  )] * rank
					_snake_case													=			indices
					_snake_case													=			input[s]

					_snake_case													=			list(range(0    ,      rank + 1  )  )
					perm.append(perm.pop(dimension + 1  )  )

					return sliced.permute(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict  )     ->	str:



					'''simple docstring'''




					import torch

					_snake_case													=			torch.arange(1    ,      UpperCamelCase__  )
					_snake_case													=			torch.remainder(UpperCamelCase__    ,      UpperCamelCase__  )
					_snake_case													=			remainders == 0
					_snake_case													=			candidates[divisor_indices]
					_snake_case													=			torch.max(UpperCamelCase__  )
					return largest_divisor, torch.div(UpperCamelCase__    ,      UpperCamelCase__    ,      rounding_mode='floor'  )




class      UpperCamelCase_						(					_lowerCamelCase   ):




					@property
					def 		lowerCAmelCase     (						self					)						->							Mapping[str, Mapping[int, str]]:
										_snake_case													=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
										if self.use_past:
															self.fill_with_past_key_values_(lowerCAmelCase_			,  direction='inputs'					)
															_snake_case													=			{0: 'batch', 1: 'past_sequence + sequence'}
										else:
															_snake_case													=			{0: 'batch', 1: 'sequence'}

										return common_inputs




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return self._config.num_heads




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = None			,  )						->							Mapping[str, Any]:
										_snake_case													=			super(lowerCAmelCase_			,  self					).generate_dummy_inputs(
										    lowerCAmelCase_			,  batch_size=lowerCAmelCase_			,  seq_length=lowerCAmelCase_			,  is_pair=lowerCAmelCase_			,  framework=lowerCAmelCase_					)

										# We need to order the input in the way they appears in the forward()
										_snake_case													=			OrderedDict({'input_ids': common_inputs['input_ids']}					)

										# Need to add the past_keys
										if self.use_past:
															if not is_torch_available():
																				raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
															else:
																				import torch

																				_snake_case							,       _snake_case													=			common_inputs['input_ids'].shape
																				# Not using the same length for past_key_values
																				_snake_case													=			seqlen + 2
																				_snake_case													=			(
																				    batch,
																				    self.num_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // self.num_attention_heads,
																				)
																				_snake_case													=			[
																				    (torch.zeros(lowerCAmelCase_					), torch.zeros(lowerCAmelCase_					)) for _ in range(self.num_layers					)
																				]

										_snake_case													=			common_inputs['attention_mask']
										if self.use_past:
															_snake_case													=			ordered_inputs['attention_mask'].dtype
															_snake_case													=			torch.cat(
															    [ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase_			,  lowerCAmelCase_			,  dtype=lowerCAmelCase_					)]			,  dim=1					)

										return ordered_inputs






					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return 13



