





'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase					:     Dict												=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any] ):
   lowercase_			:Union[str, Any]    			= SwinConfig(
       embed_dim=1_92	,depths=(2, 2, 18, 2)	,num_heads=(6, 12, 24, 48)	,window_size=12	,out_features=["stage2", "stage3", "stage4"]	,)

   lowercase_			:int    			= DetaConfig(
       backbone_config=__lowerCamelCase	,num_queries=9_00	,encoder_ffn_dim=20_48	,decoder_ffn_dim=20_48	,num_feature_levels=5	,assign_first_stage=__lowerCamelCase	,with_box_refine=__lowerCamelCase	,two_stage=__lowerCamelCase	,)

   # set labels
   lowercase_			:Tuple    			= "huggingface/label-files"
   if "o365" in model_name:
      lowercase_			:Optional[int]    			= 3_66
      lowercase_			:List[str]    			= "object365-id2label.json"
   else:
      lowercase_			:Dict    			= 91
      lowercase_			:List[Any]    			= "coco-detection-id2label.json"

   lowercase_			:Optional[int]    			= num_labels
   lowercase_			:List[str]    			= json.load(open(cached_download(hf_hub_url(__lowerCamelCase	,__lowerCamelCase	,repo_type="dataset" ) )	,"r" ) )
   lowercase_			:Optional[Any]    			= {int(__lowerCamelCase ): v for k, v in idalabel.items()}
   lowercase_			:int    			= idalabel
   lowercase_			:List[Any]    			= {v: k for k, v in idalabel.items()}

   return config
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int ):
   lowercase_			:Optional[int]    			= []

   # stem
   # fmt: off
   rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight") )
   rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias") )
   rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight") )
   rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias") )
   # stages
   for i in range(len(config.backbone_config.depths ) ):
      for j in range(config.backbone_config.depths[i] ):
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
         rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )

      if i < 3:
         rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight') )
         rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight') )
         rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias') )

   rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight") )
   rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias") )
   rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight") )
   rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias") )
   rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight") )
   rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias") )

   # transformer encoder
   for i in range(config.encoder_layers ):
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias') )
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias') )
      rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
      rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias') )
      rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias') )
      rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight') )
      rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias') )

   # transformer decoder
   for i in range(config.decoder_layers ):
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias') )
      rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight') )
      rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias') )

   # fmt: on

   return rename_keys
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					Dict	,__lowerCamelCase					:					Optional[Any] ):
   lowercase_			:int    			= dct.pop(__lowerCamelCase )
   lowercase_			:Dict    			= val
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Any ):
   lowercase_			:Optional[Any]    			= [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
   for i in range(len(backbone_config.depths ) ):
      lowercase_			:List[str]    			= num_features[i]
      for j in range(backbone_config.depths[i] ):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         lowercase_			:Any    			= state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight' )
         lowercase_			:List[str]    			= state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias' )
         # next, add query, keys and values (in that order) to the state dict
         lowercase_			:Any    			= in_proj_weight[:dim, :]
         lowercase_			:Optional[int]    			= in_proj_bias[: dim]
         lowercase_			:Dict    			= in_proj_weight[
             dim : dim * 2, :
         ]
         lowercase_			:Union[str, Any]    			= in_proj_bias[
             dim : dim * 2
         ]
         lowercase_			:Tuple    			= in_proj_weight[
             -dim :, :
         ]
         lowercase_			:Optional[Any]    			= in_proj_bias[-dim :]
         # fmt: on
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					str ):
   # transformer decoder self-attention layers
   lowercase_			:Any    			= config.d_model
   for i in range(config.decoder_layers ):
      # read in weights + bias of input projection layer of self-attention
      lowercase_			:Tuple    			= state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
      lowercase_			:str    			= state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
      # next, add query, keys and values (in that order) to the state dict
      lowercase_			:int    			= in_proj_weight[:hidden_size, :]
      lowercase_			:Tuple    			= in_proj_bias[:hidden_size]
      lowercase_			:List[str]    			= in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      lowercase_			:int    			= in_proj_bias[hidden_size : hidden_size * 2]
      lowercase_			:Union[str, Any]    			= in_proj_weight[-hidden_size:, :]
      lowercase_			:Tuple    			= in_proj_bias[-hidden_size:]
def 							UpperCAmelCase_  (						):
   lowercase_			:Dict    			= "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_			:Any    			= Image.open(requests.get(__lowerCamelCase	,stream=__lowerCamelCase ).raw )

   return im
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[Any]	,__lowerCamelCase					:					Dict	,__lowerCamelCase					:					List[Any] ):
   lowercase_			:Optional[Any]    			= get_deta_config(__lowerCamelCase )

   # load original state dict
   if model_name == "deta-swin-large":
      lowercase_			:List[str]    			= hf_hub_download(repo_id="nielsr/deta-checkpoints"	,filename="adet_swin_ft.pth" )
   elif model_name == "deta-swin-large-o365":
      lowercase_			:Optional[int]    			= hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"	,filename="deta_swin_pt_o365.pth" )
   else:
      raise ValueError(F'Model name {model_name} not supported' )

   lowercase_			:Any    			= torch.load(__lowerCamelCase	,map_location="cpu" )["model"]

   # original state dict
   for name, param in state_dict.items():
      print(__lowerCamelCase	,param.shape )

   # rename keys
   lowercase_			:Any    			= create_rename_keys(__lowerCamelCase )
   for src, dest in rename_keys:
      rename_key(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase )
   read_in_swin_q_k_v(__lowerCamelCase	,config.backbone_config )
   read_in_decoder_q_k_v(__lowerCamelCase	,__lowerCamelCase )

   # fix some prefixes
   for key in state_dict.copy().keys():
      if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
         lowercase_			:Optional[Any]    			= state_dict.pop(__lowerCamelCase )
         lowercase_			:Union[str, Any]    			= val
      if "input_proj" in key:
         lowercase_			:Dict    			= state_dict.pop(__lowerCamelCase )
         lowercase_			:Tuple    			= val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         lowercase_			:List[Any]    			= state_dict.pop(__lowerCamelCase )
         lowercase_			:List[str]    			= val

    # finally, create HuggingFace model and load state dict
   lowercase_			:Any    			= DetaForObjectDetection(__lowerCamelCase )
   model.load_state_dict(__lowerCamelCase )
   model.eval()

   lowercase_			:List[str]    			= "cuda" if torch.cuda.is_available() else "cpu"
   model.to(__lowerCamelCase )

   # load image processor
   lowercase_			:Any    			= DetaImageProcessor(format="coco_detection" )

   # verify our conversion on image
   lowercase_			:List[str]    			= prepare_img()
   lowercase_			:Union[str, Any]    			= processor(images=__lowerCamelCase	,return_tensors="pt" )
   lowercase_			:List[Any]    			= encoding["pixel_values"]
   lowercase_			:Optional[Any]    			= model(pixel_values.to(__lowerCamelCase ) )

   # verify logits
   print("Logits:"	,outputs.logits[0, :3, :3] )
   print("Boxes:"	,outputs.pred_boxes[0, :3, :3] )
   if model_name == "deta-swin-large":
      lowercase_			:str    			= torch.tensor(
          [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]] )
      lowercase_			:List[str]    			= torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]] )
   elif model_name == "deta-swin-large-o365":
      lowercase_			:List[Any]    			= torch.tensor(
          [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]] )
      lowercase_			:List[Any]    			= torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]] )
   assert torch.allclose(outputs.logits[0, :3, :3]	,expected_logits.to(__lowerCamelCase )	,atol=1e-4 )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,expected_boxes.to(__lowerCamelCase )	,atol=1e-4 )
   print("Everything ok!" )

   if pytorch_dump_folder_path:
      # Save model and processor
      logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
      Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
      model.save_pretrained(__lowerCamelCase )
      processor.save_pretrained(__lowerCamelCase )

   # Push to hub
   if push_to_hub:
      print("Pushing model and processor to hub..." )
      model.push_to_hub(F'jozhang97/{model_name}' )
      processor.push_to_hub(F'jozhang97/{model_name}' )


if __name__ == "__main__":
 lowerCAmelCase					:     int												=argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     type=str,
     default='''deta-swin-large''',
     choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
     help='''Name of the model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     help='''Path to the folder to output PyTorch model.''',
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 lowerCAmelCase					:     List[Any]												=parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''


import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

lowerCAmelCase					:     Dict												=collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase					:     str												='''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:Tuple    			= numpy.dtype(numpy.uintaa ).newbyteorder(">" )
   return numpy.frombuffer(bytestream.read(4 )	,dtype=__lowerCamelCase )[0]
@deprecated(__lowerCamelCase	,"Please use tf.data to implement this functionality." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:Union[str, Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_51:
         raise ValueError(
             "Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= _readaa(__lowerCamelCase )
      lowercase_			:Optional[Any]    			= bytestream.read(rows * cols * num_images )
      lowercase_			:List[str]    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      lowercase_			:Dict    			= data.reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,1 )
      return data
@deprecated(__lowerCamelCase	,"Please use tf.one_hot on tensors." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Tuple ):
   lowercase_			:int    			= labels_dense.shape[0]
   lowercase_			:Any    			= numpy.arange(__lowerCamelCase ) * num_classes
   lowercase_			:Optional[int]    			= numpy.zeros((num_labels, num_classes) )
   lowercase_			:List[Any]    			= 1
   return labels_one_hot
@deprecated(__lowerCamelCase	,"Please use tf.data to implement this functionality." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					Optional[Any]=False	,__lowerCamelCase					:					List[str]=10 ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:List[Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_49:
         raise ValueError(
             "Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
      lowercase_			:List[str]    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= bytestream.read(__lowerCamelCase )
      lowercase_			:int    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      if one_hot:
         return _dense_to_one_hot(__lowerCamelCase	,__lowerCamelCase )
      return labels
class       a_   :





     @deprecated(
         lowercase	,      "Please use alternatives such as official/mnist/_DataSet.py"
         " from tensorflow/models."	,      )
     def __init__( self		:					Tuple	,      lowercase		:					str	,      lowercase		:					Optional[int]	,      lowercase		:					List[Any]=False	,      lowercase		:					Tuple=False	,      lowercase		:					Optional[Any]=dtypes.floataa	,      lowercase		:					Tuple=True	,      lowercase		:					Optional[Any]=None	,      ):





        """simple docstring"""




        lowercase_			,							lowercase_			:Union[str, Any]    			= random_seed.get_seed(lowercase						)
        # If op level seed is not set, use whatever graph level seed is returned
        numpy.random.seed(seeda if seed is None else seeda						)
        lowercase_			:Optional[int]    			= dtypes.as_dtype(lowercase						).base_dtype
        if dtype not in (dtypes.uinta, dtypes.floataa):
           raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype						)
        if fake_data:
           lowercase_			:List[str]    			= 10_000
           lowercase_			:int    			= one_hot
        else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
           lowercase_			:List[Any]    			= images.shape[0]

           # Convert shape from [num examples, rows, columns, depth]
           # to [num examples, rows*columns] (assuming depth == 1)
           if reshape:
              assert images.shape[3] == 1
              lowercase_			:Optional[int]    			= images.reshape(
                  images.shape[0]	,      images.shape[1] * images.shape[2]						)
           if dtype == dtypes.floataa:
              # Convert from [0, 255] -> [0.0, 1.0].
              lowercase_			:Any    			= images.astype(numpy.floataa						)
              lowercase_			:Optional[int]    			= numpy.multiply(lowercase	,      1.0 / 2_55.0						)
        lowercase_			:Tuple    			= images
        lowercase_			:Any    			= labels
        lowercase_			:Dict    			= 0
        lowercase_			:Optional[Any]    			= 0





     @property
     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        return self._images





     @property
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        return self._labels





     @property
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        return self._num_examples





     @property
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        return self._epochs_completed







     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					int	,      lowercase		:					List[Any]=False	,      lowercase		:					int=True						):





        """simple docstring"""




        if fake_data:
           lowercase_			:List[str]    			= [1] * 784
           lowercase_			:Dict    			= [1] + [0] * 9 if self.one_hot else 0
           return (
               [fake_image for _ in range(lowercase						)],
               [fake_label for _ in range(lowercase						)],
           )
        lowercase_			:Optional[Any]    			= self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
           lowercase_			:Dict    			= numpy.arange(self._num_examples						)
           numpy.random.shuffle(lowercase						)
           lowercase_			:int    			= self.images[perma]
           lowercase_			:Tuple    			= self.labels[perma]
        # Go to the next epoch
        if start + batch_size > self._num_examples:
           # Finished epoch
           self._epochs_completed += 1
           # Get the rest examples in this epoch
           lowercase_			:Dict    			= self._num_examples - start
           lowercase_			:List[str]    			= self._images[start : self._num_examples]
           lowercase_			:Optional[Any]    			= self._labels[start : self._num_examples]
           # Shuffle the data
           if shuffle:
              lowercase_			:str    			= numpy.arange(self._num_examples						)
              numpy.random.shuffle(lowercase						)
              lowercase_			:Optional[int]    			= self.images[perm]
              lowercase_			:List[Any]    			= self.labels[perm]
           # Start next epoch
           lowercase_			:List[str]    			= 0
           lowercase_			:Any    			= batch_size - rest_num_examples
           lowercase_			:Optional[int]    			= self._index_in_epoch
           lowercase_			:Tuple    			= self._images[start:end]
           lowercase_			:Optional[Any]    			= self._labels[start:end]
           return (
               numpy.concatenate((images_rest_part, images_new_part)	,      axis=0						),
               numpy.concatenate((labels_rest_part, labels_new_part)	,      axis=0						),
           )
        else:
           self._index_in_epoch += batch_size
           lowercase_			:Any    			= self._index_in_epoch
           return self._images[start:end], self._labels[start:end]







@deprecated(__lowerCamelCase	,"Please write your own downloading logic." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Dict ):
   if not gfile.Exists(__lowerCamelCase ):
      gfile.MakeDirs(__lowerCamelCase )
   lowercase_			:Dict    			= os.path.join(__lowerCamelCase	,__lowerCamelCase )
   if not gfile.Exists(__lowerCamelCase ):
      urllib.request.urlretrieve(__lowerCamelCase	,__lowerCamelCase )  # noqa: S310
      with gfile.GFile(__lowerCamelCase ) as f:
         lowercase_			:List[str]    			= f.size()
      print("Successfully downloaded"	,__lowerCamelCase	,__lowerCamelCase	,"bytes." )
   return filepath


@deprecated(
    __lowerCamelCase	,"Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					int=False	,__lowerCamelCase					:					Dict=False	,__lowerCamelCase					:					List[Any]=dtypes.floataa	,__lowerCamelCase					:					List[Any]=True	,__lowerCamelCase					:					int=50_00	,__lowerCamelCase					:					Optional[Any]=None	,__lowerCamelCase					:					str=DEFAULT_SOURCE_URL	,):
   if fake_data:

      def fake():
         return _DataSet(
             []	,[]	,fake_data=__lowerCamelCase	,one_hot=__lowerCamelCase	,dtype=__lowerCamelCase	,seed=__lowerCamelCase )

      lowercase_			:int    			= fake()
      lowercase_			:Optional[Any]    			= fake()
      lowercase_			:Tuple    			= fake()
      return _Datasets(train=__lowerCamelCase	,validation=__lowerCamelCase	,test=__lowerCamelCase )

   if not source_url:  # empty string check
      lowercase_			:str    			= DEFAULT_SOURCE_URL

   lowercase_			:Optional[int]    			= "train-images-idx3-ubyte.gz"
   lowercase_			:Optional[int]    			= "train-labels-idx1-ubyte.gz"
   lowercase_			:Optional[Any]    			= "t10k-images-idx3-ubyte.gz"
   lowercase_			:int    			= "t10k-labels-idx1-ubyte.gz"

   lowercase_			:Tuple    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + train_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Any    			= _extract_images(__lowerCamelCase )

   lowercase_			:Optional[Any]    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + train_labels_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Any    			= _extract_labels(__lowerCamelCase	,one_hot=__lowerCamelCase )

   lowercase_			:Any    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_images(__lowerCamelCase )

   lowercase_			:int    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_labels_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_labels(__lowerCamelCase	,one_hot=__lowerCamelCase )

   if not 0 <= validation_size <= len(__lowerCamelCase ):
      lowercase_			:Union[str, Any]    			= (
          "Validation size should be between 0 and "
          F'{len(__lowerCamelCase )}. Received: {validation_size}.'
      )
      raise ValueError(__lowerCamelCase )

   lowercase_			:int    			= train_images[:validation_size]
   lowercase_			:Optional[int]    			= train_labels[:validation_size]
   lowercase_			:List[str]    			= train_images[validation_size:]
   lowercase_			:int    			= train_labels[validation_size:]

   lowercase_			:Dict    			= {"dtype": dtype, "reshape": reshape, "seed": seed}

   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:str    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )

   return _Datasets(train=__lowerCamelCase	,validation=__lowerCamelCase	,test=__lowerCamelCase )


