
"""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  A__       (	_lowerCamelCase       ,     _lowerCamelCase       ,     _lowerCamelCase):
		A_						:       List[str]				   =	[R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']

		@register_to_config
		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = 5_02_57       ,		_SCREAMING_SNAKE_CASE = 10_24       ,		_SCREAMING_SNAKE_CASE = 7_68       ,		_SCREAMING_SNAKE_CASE = 12       ,		_SCREAMING_SNAKE_CASE = 12       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = "gelu_new"       ,		_SCREAMING_SNAKE_CASE = 0.1       ,		_SCREAMING_SNAKE_CASE = 0.1       ,		_SCREAMING_SNAKE_CASE = 0.1       ,		_SCREAMING_SNAKE_CASE = 1E-5       ,		_SCREAMING_SNAKE_CASE = 0.02       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = False       ,		_SCREAMING_SNAKE_CASE = False       ,		):
			super().__init__()

			__lowerCAmelCase			:						Dict									=      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			:						int									=      prefix_inner_dim
			__lowerCAmelCase			:						Optional[Any]									=      prefix_hidden_dim

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

			__lowerCAmelCase			:						Dict									=      GPTaConfig(
			    vocab_size=_SCREAMING_SNAKE_CASE       ,		n_positions=_SCREAMING_SNAKE_CASE       ,		n_embd=_SCREAMING_SNAKE_CASE       ,		n_layer=_SCREAMING_SNAKE_CASE       ,		n_head=_SCREAMING_SNAKE_CASE       ,		n_inner=_SCREAMING_SNAKE_CASE       ,		activation_function=_SCREAMING_SNAKE_CASE       ,		resid_pdrop=_SCREAMING_SNAKE_CASE       ,		embd_pdrop=_SCREAMING_SNAKE_CASE       ,		attn_pdrop=_SCREAMING_SNAKE_CASE       ,		layer_norm_epsilon=_SCREAMING_SNAKE_CASE       ,		initializer_range=_SCREAMING_SNAKE_CASE       ,		scale_attn_weights=_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE       ,		scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE       ,		reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE       ,		)
			__lowerCAmelCase			:						str									=      GPTaLMHeadModel(_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						Optional[Any]									=      self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[int]									=      self.encode_prefix(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      self.decode_prefix(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      torch.cat((prefix_embeds, embedding_text)       ,		dim=1 )

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

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			return torch.zeros(_SCREAMING_SNAKE_CASE       ,		self.prefix_length       ,		dtype=torch.intaa       ,		device=_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			return self.encode_prefix(_SCREAMING_SNAKE_CASE )

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







		@torch.no_grad()
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE = 5       ,		_SCREAMING_SNAKE_CASE = 67       ,		_SCREAMING_SNAKE_CASE = 1.0       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						List[Any]									=      eos_token_id
			__lowerCAmelCase			:						List[Any]									=      None
			__lowerCAmelCase			:						Tuple									=      None
			__lowerCAmelCase			:						Optional[int]									=      torch.ones(_SCREAMING_SNAKE_CASE       ,		device=_SCREAMING_SNAKE_CASE       ,		dtype=torch.int )
			__lowerCAmelCase			:						Union[str, Any]									=      torch.zeros(_SCREAMING_SNAKE_CASE       ,		device=_SCREAMING_SNAKE_CASE       ,		dtype=torch.bool )

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

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

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

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

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



from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands



def 	lowerCAmelCase       (						)->       int:
				lowerCAmelCase_       :					int			 = ArgumentParser('''Transformers CLI tool'''       ,      usage='''transformers-cli <command> [<args>]'''       )
				lowerCAmelCase_       :					Dict			 = parser.add_subparsers(help='''transformers-cli command helpers'''       )

				# Register commands
				ConvertCommand.register_subcommand(lowerCAmelCase_       )
				DownloadCommand.register_subcommand(lowerCAmelCase_       )
				EnvironmentCommand.register_subcommand(lowerCAmelCase_       )
				RunCommand.register_subcommand(lowerCAmelCase_       )
				ServeCommand.register_subcommand(lowerCAmelCase_       )
				UserCommands.register_subcommand(lowerCAmelCase_       )
				AddNewModelCommand.register_subcommand(lowerCAmelCase_       )
				AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_       )
				LfsCommands.register_subcommand(lowerCAmelCase_       )
				PTtoTFCommand.register_subcommand(lowerCAmelCase_       )

				# Let's go
				lowerCAmelCase_       :					Union[str, Any]			 = parser.parse_args()

				if not hasattr(lowerCAmelCase_       ,      '''func'''       ):
								parser.print_help()
								exit(1       )

				# Run
				lowerCAmelCase_       :					List[Any]			 = args.func(lowerCAmelCase_       )
				service.run()


if __name__ == "__main__":
						main()