"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)





def  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					Optional[int]=False			,      _UpperCamelCase							:					List[Any]=False       )       ->					List[Any]:





						"""simple docstring"""






						snake_case             =      'backbone.' if is_semantic else ''

						snake_case             =      []
						for i in range(config.num_hidden_layers       ):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")       )
												rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")       )
												rename_keys.append(
												    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")       )
												rename_keys.append(
												    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")       )
												rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")       )
												rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")       )
												rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")       )
												rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")       )
												rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")       )
												rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")       )

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (f"""{prefix}cls_token""", 'beit.embeddings.cls_token'),
						        (f"""{prefix}patch_embed.proj.weight""", 'beit.embeddings.patch_embeddings.projection.weight'),
						        (f"""{prefix}patch_embed.proj.bias""", 'beit.embeddings.patch_embeddings.projection.bias'),
						    ]       )

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ('mask_token', 'beit.embeddings.mask_token'),
												        (
												            'rel_pos_bias.relative_position_bias_table',
												            'beit.encoder.relative_position_bias.relative_position_bias_table',
												        ),
												        (
												            'rel_pos_bias.relative_position_index',
												            'beit.encoder.relative_position_bias.relative_position_index',
												        ),
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]       )
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
												        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
												        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
												        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
												    ]       )
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
												        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]       )

						return rename_keys





def  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[int]			,      _UpperCamelCase							:					int			,      _UpperCamelCase							:					Dict=False			,      _UpperCamelCase							:					Optional[int]=False       )       ->					List[Any]:





						"""simple docstring"""






						for i in range(config.num_hidden_layers       ):
												snake_case             =      'backbone.' if is_semantic else ''
												# queries, keys and values
												snake_case             =      state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""       )
												snake_case             =      state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""       )
												snake_case             =      state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""       )

												snake_case             =      in_proj_weight[
												    : config.hidden_size, :
												]
												snake_case             =      q_bias
												snake_case             =      in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case             =      in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case             =      v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												snake_case             =      state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""       )
												snake_case             =      state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""       )

												snake_case             =      gamma_a
												snake_case             =      gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		snake_case             =      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""       )
																		snake_case             =      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""       )

																		snake_case             =      table
																		snake_case             =      index





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





						"""simple docstring"""






						snake_case             =      dct.pop(_UpperCamelCase       )
						snake_case             =      val





def  lowerCAmelCase__     (      )       ->					List[str]:





						"""simple docstring"""






						snake_case             =      'http://images.cocodataset.org/val2017/000000039769.jpg'
						snake_case             =      Image.open(requests.get(_UpperCamelCase			,      stream=_UpperCamelCase       ).raw       )
						return im





@torch.no_grad()
def  lowerCAmelCase__     (      _UpperCamelCase							:					List[str]			,      _UpperCamelCase							:					Optional[Any]       )       ->					Optional[Any]:





						"""simple docstring"""






						snake_case             =      BeitConfig()
						snake_case             =      False
						snake_case             =      False
						snake_case             =      'huggingface/label-files'
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												snake_case             =      True
												snake_case             =      True
												snake_case             =      True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												snake_case             =      True
												snake_case             =      2_1_8_4_1
												snake_case             =      'imagenet-22k-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()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[9_2_0_5]
												del idalabel[1_5_0_2_7]
												snake_case             =      idalabel
												snake_case             =      {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												snake_case             =      True
												snake_case             =      1_0_0_0
												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()}
												if "384" in checkpoint_url:
																		snake_case             =      3_8_4
												if "512" in checkpoint_url:
																		snake_case             =      5_1_2
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												snake_case             =      True
												snake_case             =      1_5_0
												snake_case             =      'ade20k-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             =      6_4_0
												snake_case             =      True
						else:
												raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''       )

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												snake_case             =      1_0_2_4
												snake_case             =      4_0_9_6
												snake_case             =      2_4
												snake_case             =      1_6
												if "ade20k" in checkpoint_url:
																		snake_case             =      6_4_0
																		snake_case             =      [7, 1_1, 1_5, 2_3]
						else:
												raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'       )

						# load state_dict of original model, remove and rename some keys
						snake_case             =      torch.hub.load_state_dict_from_url(_UpperCamelCase			,      map_location='cpu'			,      check_hash=_UpperCamelCase       )
						snake_case             =      state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

						snake_case             =      create_rename_keys(_UpperCamelCase			,      has_lm_head=_UpperCamelCase			,      is_semantic=_UpperCamelCase       )
						for src, dest in rename_keys:
												rename_key(_UpperCamelCase			,      _UpperCamelCase			,      _UpperCamelCase       )
						read_in_q_k_v(_UpperCamelCase			,      _UpperCamelCase			,      has_lm_head=_UpperCamelCase			,      is_semantic=_UpperCamelCase       )
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		snake_case             =      state_dict.pop(_UpperCamelCase       )
																		if key.startswith('backbone.fpn'       ):
																								snake_case             =      key.replace('backbone.fpn'			,      'fpn'       )
																		snake_case             =      val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												snake_case             =      BeitForMaskedImageModeling(_UpperCamelCase       )
						elif "ade20k" in checkpoint_url:
												snake_case             =      BeitForSemanticSegmentation(_UpperCamelCase       )
						else:
												snake_case             =      BeitForImageClassification(_UpperCamelCase       )
						model.eval()
						model.load_state_dict(_UpperCamelCase       )

						# Check outputs on an image
						if is_semantic:
												snake_case             =      BeitImageProcessor(size=config.image_size			,      do_center_crop=_UpperCamelCase       )
												snake_case             =      load_dataset('hf-internal-testing/fixtures_ade20k'			,      split='test'       )
												snake_case             =      Image.open(ds[0]['file']       )
						else:
												snake_case             =      BeitImageProcessor(
												    size=config.image_size			,      resample=PILImageResampling.BILINEAR			,      do_center_crop=_UpperCamelCase       )
												snake_case             =      prepare_img()

						snake_case             =      image_processor(images=_UpperCamelCase			,      return_tensors='pt'       )
						snake_case             =      encoding['pixel_values']

						snake_case             =      model(_UpperCamelCase       )
						snake_case             =      outputs.logits

						# verify logits
						snake_case             =      torch.Size([1, 1_0_0_0]       )
						if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'       ):
												snake_case             =      torch.Size([1, 1_9_6, 8_1_9_2]       )
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'       ):
												snake_case             =      torch.Size([1, 1_9_6, 8_1_9_2]       )
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'       ):
												snake_case             =      torch.Size([1, 2_1_8_4_1]       )
												snake_case             =      torch.tensor([2.22_88, 2.46_71, 0.73_95]       )
												snake_case             =      2_3_9_7
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'       ):
												snake_case             =      torch.Size([1, 2_1_8_4_1]       )
												snake_case             =      torch.tensor([1.68_81, -0.27_87, 0.59_01]       )
												snake_case             =      2_3_9_6
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'       ):
												snake_case             =      torch.tensor([0.12_41, 0.07_98, -0.65_69]       )
												snake_case             =      2_8_5
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'       ):
												snake_case             =      torch.tensor([-1.23_85, -1.09_87, -1.01_08]       )
												snake_case             =      2_8_1
						elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'       ):
												snake_case             =      torch.tensor([-1.53_03, -0.94_84, -0.31_47]       )
												snake_case             =      7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'       ):
												snake_case             =      torch.tensor([0.46_10, -0.09_28, 0.20_86]       )
												snake_case             =      7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'       ):
												snake_case             =      torch.tensor([-0.48_04, 0.62_57, -0.18_37]       )
												snake_case             =      7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'       ):
												snake_case             =      torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]       )
												snake_case             =      7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'       ):
												snake_case             =      torch.tensor([-0.30_62, 0.72_61, 0.48_52]       )
												snake_case             =      7_6_1
						elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'       ):
												snake_case             =      (1, 1_5_0, 1_6_0, 1_6_0)
												snake_case             =      torch.tensor(
												    [
												        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
												        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
												        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
												    ]       )
						elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'       ):
												snake_case             =      (1, 1_5_0, 1_6_0, 1_6_0)
												snake_case             =      torch.tensor(
												    [
												        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
												        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
												        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
												    ]       )
						else:
												raise ValueError('Can\'t verify logits as model is not supported'       )

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]			,      _UpperCamelCase			,      atol=1e-3       ), "First elements of logits not as expected"
												else:
																		print('Predicted class idx:'			,      logits.argmax(-1       ).item()       )
																		assert torch.allclose(
																		    logits[0, :3]			,      _UpperCamelCase			,      atol=1e-3       ), "First elements of logits not as expected"
																		assert logits.argmax(-1       ).item() == expected_class_idx, "Predicted class index not as expected"

						Path(_UpperCamelCase       ).mkdir(exist_ok=_UpperCamelCase       )
						print(f"""Saving model 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()

					parser.add_argument(
					    "--checkpoint_url",
					    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
					    type=str,
					    help="URL to the original PyTorch checkpoint (.pth file).",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					SCREAMING_SNAKE_CASE__       =	parser.parse_args()
					convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)