
'''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     __snake_case		(   a__    ,   a__    ,   a__):
					_lowerCAmelCase			     =	[R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']
					@register_to_config
					def __init__(     self,    A,    A,    A = None,    A = 5_0257,    A = 1024,    A = 768,    A = 12,    A = 12,    A = None,    A = "gelu_new",    A = 0.1,    A = 0.1,    A = 0.1,    A = 1e-5,    A = 0.02,    A = True,    A = True,    A = False,    A = False,    ):

									"""simple docstring"""


									super().__init__()

									lowerCamelCase					:			str									= 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.'''      )

									lowerCamelCase					:			str									= prefix_inner_dim
									lowerCamelCase					:			int									= prefix_hidden_dim

									lowerCamelCase					:			Optional[int]									= (
									    nn.Linear(self.prefix_inner_dim,    self.prefix_hidden_dim      )
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									lowerCamelCase					:			int									= (
									    nn.Linear(self.prefix_hidden_dim,    A      ) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									lowerCamelCase					:			int									= GPTaConfig(
									    vocab_size=A,    n_positions=A,    n_embd=A,    n_layer=A,    n_head=A,    n_inner=A,    activation_function=A,    resid_pdrop=A,    embd_pdrop=A,    attn_pdrop=A,    layer_norm_epsilon=A,    initializer_range=A,    scale_attn_weights=A,    use_cache=A,    scale_attn_by_inverse_layer_idx=A,    reorder_and_upcast_attn=A,    )
									lowerCamelCase					:			Optional[int]									= GPTaLMHeadModel(A      )
					def     UpperCAmelCase_							(     self,    A,    A,    A = None,    A = None,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.transformer.transformer.wte(A      )
									lowerCamelCase					:			Union[str, Any]									= self.encode_prefix(A      )
									lowerCamelCase					:			List[Any]									= self.decode_prefix(A      )
									lowerCamelCase					:			int									= torch.cat((prefix_embeds, embedding_text),    dim=1      )

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

									"""simple docstring"""


									return torch.zeros(A,    self.prefix_length,    dtype=torch.intaa,    device=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.encode_prefix(A      )
					@torch.no_grad()
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


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




					@torch.no_grad()
					def     UpperCAmelCase_							(     self,    A=None,    A=None,    A=None,    A = 5,    A = 67,    A = 1.0,    A = None,    ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= eos_token_id
									lowerCamelCase					:			int									= None
									lowerCamelCase					:			List[Any]									= None
									lowerCamelCase					:			Union[str, Any]									= torch.ones(A,    device=A,    dtype=torch.int      )
									lowerCamelCase					:			Dict									= torch.zeros(A,    device=A,    dtype=torch.bool      )

									if input_embeds is not None:
													lowerCamelCase					:			Union[str, Any]									= input_embeds
									else:
													lowerCamelCase					:			Union[str, Any]									= self.transformer.transformer.wte(A      )

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

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

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

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


'''simple docstring'''






import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A			      =      {
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]):
				if got_ver is None or want_ver is None:
								raise ValueError(
								    F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
								    F''' reinstalling {pkg}.''')
				if not ops[op](version.parse(UpperCAmelCase__)       ,							version.parse(UpperCAmelCase__)):
								raise ImportError(
								    F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''')





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Optional[str] = None):
				lowerCamelCase					:			List[Any]									= F'''\n{hint}''' if hint is not None else ''

				# non-versioned check
				if re.match(R'^[\w_\-\d]+$'       ,							UpperCAmelCase__):
								lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			Optional[Any]									= requirement, None, None
				else:
								lowerCamelCase					:			Optional[Any]									= re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'       ,							UpperCAmelCase__)
								if not match:
												raise ValueError(
												    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
												    F''' got {requirement}''')
								lowerCamelCase			, lowerCamelCase					:			Dict									= match[0]
								lowerCamelCase					:			Dict									= want_full.split(',')  # there could be multiple requirements
								lowerCamelCase					:			Union[str, Any]									= {}
								for w in want_range:
												lowerCamelCase					:			int									= re.findall(R'^([\s!=<>]{1,2})(.+)'       ,							UpperCAmelCase__)
												if not match:
																raise ValueError(
																    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
																    F''' but got {requirement}''')
												lowerCamelCase			, lowerCamelCase					:			List[Any]									= match[0]
												lowerCamelCase					:			Optional[int]									= want_ver
												if op not in ops:
																raise ValueError(F'''{requirement}: need one of {list(ops.keys())}, but got {op}''')

    # special case
				if pkg == "python":
								lowerCamelCase					:			Optional[int]									= '.'.join([str(UpperCAmelCase__) for x in sys.version_info[:3]])
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
								return

				# check if any version is installed
				try:
								lowerCamelCase					:			Any									= importlib.metadata.version(UpperCAmelCase__)
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''')

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			List[str]									= 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
				return require_version(UpperCAmelCase__       ,							UpperCAmelCase__)
