

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 SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				):






			"""simple docstring"""




			@register_to_config
			def __init__(     self			,					__UpperCamelCase = 1_28			,					__UpperCamelCase = 2_56			,					__UpperCamelCase = 2000.0			,					__UpperCamelCase = 7_68			,					__UpperCamelCase = 12			,					__UpperCamelCase = 12			,					__UpperCamelCase = 64			,					__UpperCamelCase = 20_48			,					__UpperCamelCase = 0.1			,					):

				"""simple docstring"""


				super().__init__()

				snake_case_   		=			nn.Sequential(
				    nn.Linear(__UpperCamelCase			,					d_model * 4			,					bias=__UpperCamelCase				)			,					nn.SiLU()			,					nn.Linear(d_model * 4			,					d_model * 4			,					bias=__UpperCamelCase				)			,					nn.SiLU()			,					)

				snake_case_   		=			nn.Embedding(__UpperCamelCase			,					__UpperCamelCase				)
				snake_case_   		=			False

				snake_case_   		=			nn.Linear(__UpperCamelCase			,					__UpperCamelCase			,					bias=__UpperCamelCase				)

				snake_case_   		=			nn.Dropout(p=__UpperCamelCase				)

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

				snake_case_   		=			TaLayerNorm(__UpperCamelCase				)

				snake_case_   		=			nn.Dropout(p=__UpperCamelCase				)
				snake_case_   		=			nn.Linear(__UpperCamelCase			,					__UpperCamelCase			,					bias=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			torch.mul(query_input.unsqueeze(-1				)			,					key_input.unsqueeze(-2				)				)
				return mask.unsqueeze(-3				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				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(__UpperCamelCase				).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(__UpperCamelCase			,					device=decoder_input_tokens.device				)			,					(batch, seq_length)			,					)

				snake_case_   		=			self.position_encoding(__UpperCamelCase				)

				snake_case_   		=			self.continuous_inputs_projection(__UpperCamelCase				)
				inputs += position_encodings
				snake_case_   		=			self.dropout(__UpperCamelCase				)

				# 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(__UpperCamelCase			,					__UpperCamelCase				)) 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(
					    __UpperCamelCase			,					conditioning_emb=__UpperCamelCase			,					encoder_hidden_states=__UpperCamelCase			,					encoder_attention_mask=__UpperCamelCase			,					)[0]

				snake_case_   		=			self.decoder_norm(__UpperCamelCase				)
				snake_case_   		=			self.post_dropout(__UpperCamelCase				)

				snake_case_   		=			self.spec_out(__UpperCamelCase				)
				return spec_out







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=1E-6				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.ModuleList()

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

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

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







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					):

				"""simple docstring"""


				snake_case_   		=			self.layer[0](
				    __UpperCamelCase			,					conditioning_emb=__UpperCamelCase			,					attention_mask=__UpperCamelCase			,					)

				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](
					    __UpperCamelCase			,					key_value_states=__UpperCamelCase			,					attention_mask=__UpperCamelCase			,					)

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

				return (hidden_states,)







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			TaLayerNorm(__UpperCamelCase				)
				snake_case_   		=			TaFiLMLayer(in_features=d_model * 4			,					out_features=__UpperCamelCase				)
				snake_case_   		=			Attention(query_dim=__UpperCamelCase			,					heads=__UpperCamelCase			,					dim_head=__UpperCamelCase			,					out_bias=__UpperCamelCase			,					scale_qk=__UpperCamelCase				)
				snake_case_   		=			nn.Dropout(__UpperCamelCase				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					):

				"""simple docstring"""


				snake_case_   		=			self.layer_norm(__UpperCamelCase				)

				if conditioning_emb is not None:
					snake_case_   		=			self.FiLMLayer(__UpperCamelCase			,					__UpperCamelCase				)

				# Self-attention block
				snake_case_   		=			self.attention(__UpperCamelCase				)

				snake_case_   		=			hidden_states + self.dropout(__UpperCamelCase				)

				return hidden_states







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			Attention(query_dim=__UpperCamelCase			,					heads=__UpperCamelCase			,					dim_head=__UpperCamelCase			,					out_bias=__UpperCamelCase			,					scale_qk=__UpperCamelCase				)
				snake_case_   		=			TaLayerNorm(__UpperCamelCase			,					eps=__UpperCamelCase				)
				snake_case_   		=			nn.Dropout(__UpperCamelCase				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					):

				"""simple docstring"""


				snake_case_   		=			self.layer_norm(__UpperCamelCase				)
				snake_case_   		=			self.attention(
				    __UpperCamelCase			,					encoder_hidden_states=__UpperCamelCase			,					attention_mask=attention_mask.squeeze(1				)			,					)
				snake_case_   		=			hidden_states + self.dropout(__UpperCamelCase				)
				return layer_output







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			TaDenseGatedActDense(d_model=__UpperCamelCase			,					d_ff=__UpperCamelCase			,					dropout_rate=__UpperCamelCase				)
				snake_case_   		=			TaFiLMLayer(in_features=d_model * 4			,					out_features=__UpperCamelCase				)
				snake_case_   		=			TaLayerNorm(__UpperCamelCase			,					eps=__UpperCamelCase				)
				snake_case_   		=			nn.Dropout(__UpperCamelCase				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=None				):

				"""simple docstring"""


				snake_case_   		=			self.layer_norm(__UpperCamelCase				)
				if conditioning_emb is not None:
					snake_case_   		=			self.film(__UpperCamelCase			,					__UpperCamelCase				)

				snake_case_   		=			self.DenseReluDense(__UpperCamelCase				)
				snake_case_   		=			hidden_states + self.dropout(__UpperCamelCase				)
				return hidden_states







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.Linear(__UpperCamelCase			,					__UpperCamelCase			,					bias=__UpperCamelCase				)
				snake_case_   		=			nn.Linear(__UpperCamelCase			,					__UpperCamelCase			,					bias=__UpperCamelCase				)
				snake_case_   		=			nn.Linear(__UpperCamelCase			,					__UpperCamelCase			,					bias=__UpperCamelCase				)
				snake_case_   		=			nn.Dropout(__UpperCamelCase				)
				snake_case_   		=			NewGELUActivation()







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.act(self.wi_a(__UpperCamelCase				)				)
				snake_case_   		=			self.wi_a(__UpperCamelCase				)
				snake_case_   		=			hidden_gelu * hidden_linear
				snake_case_   		=			self.dropout(__UpperCamelCase				)

				snake_case_   		=			self.wo(__UpperCamelCase				)
				return hidden_states







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=1E-6				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.Parameter(torch.ones(__UpperCamelCase				)				)
				snake_case_   		=			eps







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			hidden_states.to(torch.floataa				).pow(2				).mean(-1			,					keepdim=__UpperCamelCase				)
				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 SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(__UpperCamelCase			,					3.0				))				))







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.Linear(__UpperCamelCase			,					out_features * 2			,					bias=__UpperCamelCase				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.scale_bias(__UpperCamelCase				)
				snake_case_		,							snake_case_   		=			torch.chunk(__UpperCamelCase			,					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


A									=  logging.get_logger(__name__)

A									=  {
    '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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""gpt_neo"""
			__A    	=	["""past_key_values"""]
			__A    	=	{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


			def __init__(     self			,					__UpperCamelCase=5_02_57			,					__UpperCamelCase=20_48			,					__UpperCamelCase=20_48			,					__UpperCamelCase=24			,					__UpperCamelCase=[[["global", "local"], 12]]			,					__UpperCamelCase=16			,					__UpperCamelCase=None			,					__UpperCamelCase=2_56			,					__UpperCamelCase="gelu_new"			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.1			,					__UpperCamelCase=1E-5			,					__UpperCamelCase=0.02			,					__UpperCamelCase=True			,					__UpperCamelCase=5_02_56			,					__UpperCamelCase=5_02_56			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				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(__UpperCamelCase				)

				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=__UpperCamelCase			,					eos_token_id=__UpperCamelCase			,					**__UpperCamelCase				)







			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			[]
				for item in attention_types:
					for _ in range(item[1]				):
						attentions.extend(item[0]				)
				return attentions







def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	snake_case_   		=			input.size()
	snake_case_   		=			len(lowercase__						)
	snake_case_   		=			shape[dimension]

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

	snake_case_   		=			[slice(lowercase__						)] * rank
	snake_case_   		=			indices
	snake_case_   		=			input[s]

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

	return sliced.permute(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

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






class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
				if self.use_past:
					self.fill_with_past_key_values_(__UpperCamelCase			,					direction='inputs'				)
					snake_case_   		=			{0: 'batch', 1: 'past_sequence + sequence'}
				else:
					snake_case_   		=			{0: 'batch', 1: 'sequence'}

				return common_inputs


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self._config.num_heads


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = -1			,					__UpperCamelCase = -1			,					__UpperCamelCase = False			,					__UpperCamelCase = None			,					):

				"""simple docstring"""


				snake_case_   		=			super(__UpperCamelCase			,					self				).generate_dummy_inputs(
				    __UpperCamelCase			,					batch_size=__UpperCamelCase			,					seq_length=__UpperCamelCase			,					is_pair=__UpperCamelCase			,					framework=__UpperCamelCase				)

				# 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(__UpperCamelCase				), torch.zeros(__UpperCamelCase				)) 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(__UpperCamelCase			,					__UpperCamelCase			,					dtype=__UpperCamelCase				)]			,					dim=1				)

				return ordered_inputs







			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 13
