"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					["Mask2FormerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Mask2FormerForUniversalSegmentation",
					    "Mask2FormerModel",
					    "Mask2FormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure)



"""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_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]

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






										"""simple docstring"""

										super().__init__()

										snake_case        =     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."""      )

										snake_case        =     prefix_inner_dim
										snake_case        =     prefix_hidden_dim

										snake_case        =     (
										    nn.Linear(self.prefix_inner_dim						,      self.prefix_hidden_dim      )
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										snake_case        =     (
										    nn.Linear(self.prefix_hidden_dim						,      lowerCAmelCase      ) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										snake_case        =     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						,      )
										snake_case        =     GPTaLMHeadModel(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )
										snake_case        =     self.encode_prefix(lowerCAmelCase      )
										snake_case        =     self.decode_prefix(lowerCAmelCase      )
										snake_case        =     torch.cat((prefix_embeds, embedding_text)						,      dim=1      )

										if labels is not None:
																snake_case        =     self.get_dummy_token(input_ids.shape[0]						,      input_ids.device      )
																snake_case        =     torch.cat((dummy_token, input_ids)						,      dim=1      )
										snake_case        =     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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return self.encode_prefix(lowerCAmelCase      )

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     torch.split(lowerCAmelCase						,      1						,      dim=0      )
										snake_case        =     []
										snake_case        =     []
										for feature in features:
																snake_case        =     self.decode_prefix(feature.to(lowerCAmelCase      )      )  # back to the clip feature
																# Only support beam search for now
																snake_case   ,snake_case        =     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]      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										return generated_tokens, generated_seq_lengths

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase = 5						,      lowerCAmelCase = 67						,      lowerCAmelCase = 1.0						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     eos_token_id
										snake_case        =     None
										snake_case        =     None
										snake_case        =     torch.ones(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.int      )
										snake_case        =     torch.zeros(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.bool      )

										if input_embeds is not None:
																snake_case        =     input_embeds
										else:
																snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )

										for i in range(lowerCAmelCase      ):
																snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase      )
																snake_case        =     outputs.logits
																snake_case        =     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																snake_case        =     logits.softmax(-1      ).log()

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

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

										snake_case        =     scores / seq_lengths
										snake_case        =     scores.argsort(descending=lowerCAmelCase      )
										# tokens tensors are already padded to max_seq_length
										snake_case        =     [tokens[i] for i in order]
										snake_case        =     torch.stack(lowerCAmelCase						,      dim=0      )
										snake_case        =     torch.tensor([seq_lengths[i] for i in order]						,      dtype=seq_lengths.dtype      )
										return output_texts, seq_lengths


