"""simple docstring"""
import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]   )   ->			int:





						"""simple docstring"""






						snake_case        =     SwinConfig()
						snake_case        =     swin_name.split('_'   )

						snake_case        =     name_split[1]
						snake_case        =     int(name_split[4]   )
						snake_case        =     int(name_split[3][-1]   )

						if model_size == "tiny":
												snake_case        =     9_6
												snake_case        =     (2, 2, 6, 2)
												snake_case        =     (3, 6, 1_2, 2_4)
						elif model_size == "small":
												snake_case        =     9_6
												snake_case        =     (2, 2, 1_8, 2)
												snake_case        =     (3, 6, 1_2, 2_4)
						elif model_size == "base":
												snake_case        =     1_2_8
												snake_case        =     (2, 2, 1_8, 2)
												snake_case        =     (4, 8, 1_6, 3_2)
						else:
												snake_case        =     1_9_2
												snake_case        =     (2, 2, 1_8, 2)
												snake_case        =     (6, 1_2, 2_4, 4_8)

						if "in22k" in swin_name:
												snake_case        =     2_1_8_4_1
						else:
												snake_case        =     1_0_0_0
												snake_case        =     'huggingface/label-files'
												snake_case        =     'imagenet-1k-id2label.json'
												snake_case        =     json.load(open(hf_hub_download(_UpperCamelCase	,     _UpperCamelCase	,     repo_type='dataset'   )	,     'r'   )   )
												snake_case        =     {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
												snake_case        =     idalabel
												snake_case        =     {v: k for k, v in idalabel.items()}

						snake_case        =     img_size
						snake_case        =     num_classes
						snake_case        =     embed_dim
						snake_case        =     depths
						snake_case        =     num_heads
						snake_case        =     window_size

						return config





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						if "patch_embed.proj" in name:
												snake_case        =     name.replace('patch_embed.proj'	,     'embeddings.patch_embeddings.projection'   )
						if "patch_embed.norm" in name:
												snake_case        =     name.replace('patch_embed.norm'	,     'embeddings.norm'   )
						if "layers" in name:
												snake_case        =     'encoder.' + name
						if "attn.proj" in name:
												snake_case        =     name.replace('attn.proj'	,     'attention.output.dense'   )
						if "attn" in name:
												snake_case        =     name.replace('attn'	,     'attention.self'   )
						if "norm1" in name:
												snake_case        =     name.replace('norm1'	,     'layernorm_before'   )
						if "norm2" in name:
												snake_case        =     name.replace('norm2'	,     'layernorm_after'   )
						if "mlp.fc1" in name:
												snake_case        =     name.replace('mlp.fc1'	,     'intermediate.dense'   )
						if "mlp.fc2" in name:
												snake_case        =     name.replace('mlp.fc2'	,     'output.dense'   )

						if name == "norm.weight":
												snake_case        =     'layernorm.weight'
						if name == "norm.bias":
												snake_case        =     'layernorm.bias'

						if "head" in name:
												snake_case        =     name.replace('head'	,     'classifier'   )
						else:
												snake_case        =     'swin.' + name

						return name





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Dict   )   ->			str:





						"""simple docstring"""






						for key in orig_state_dict.copy().keys():
												snake_case        =     orig_state_dict.pop(_UpperCamelCase   )

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		snake_case        =     key.split('.'   )
																		snake_case        =     int(key_split[1]   )
																		snake_case        =     int(key_split[3]   )
																		snake_case        =     model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								snake_case        =     val[:dim, :]
																								snake_case        =     val[
																								    dim : dim * 2, :
																								]
																								snake_case        =     val[-dim:, :]
																		else:
																								snake_case        =     val[
																								    :dim
																								]
																								snake_case        =     val[
																								    dim : dim * 2
																								]
																								snake_case        =     val[
																								    -dim:
																								]
												else:
																		snake_case        =     val

						return orig_state_dict





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Optional[int]   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     timm.create_model(_UpperCamelCase	,     pretrained=_UpperCamelCase   )
						timm_model.eval()

						snake_case        =     get_swin_config(_UpperCamelCase   )
						snake_case        =     SwinForImageClassification(_UpperCamelCase   )
						model.eval()

						snake_case        =     convert_state_dict(timm_model.state_dict()	,     _UpperCamelCase   )
						model.load_state_dict(_UpperCamelCase   )

						snake_case        =     'http://images.cocodataset.org/val2017/000000039769.jpg'

						snake_case        =     AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'	,     '-'   )   )   )
						snake_case        =     Image.open(requests.get(_UpperCamelCase	,     stream=_UpperCamelCase   ).raw   )
						snake_case        =     image_processor(images=_UpperCamelCase	,     return_tensors='pt'   )

						snake_case        =     timm_model(inputs['pixel_values']   )
						snake_case        =     model(**_UpperCamelCase   ).logits

						assert torch.allclose(_UpperCamelCase	,     _UpperCamelCase	,     atol=1e-3   )

						print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""   )
						model.save_pretrained(_UpperCamelCase   )

						print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
						image_processor.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--swin_name",
					    default="swin_tiny_patch4_window7_224",
					    type=str,
					    help="Name of the Swin timm model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)

					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



"""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


