


import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


UpperCamelCase :				Optional[Any]						    =       {
    '''vit_b32''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 32,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 16,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 768,
        '''image_resolution''': 840,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''vision_layers''': 24,
        '''vision_width''': 1_024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}
def 							UpperCamelCase_  (				__snake_case			:						List[str]	,			__snake_case			:						List[Any]=""	,			__snake_case			:						Tuple="/"     )->      str:




						'''simple docstring'''
						__magic_name__ 		=			[]

						for k, v in params.items():
												__magic_name__ 		=			parent_key + sep + k if parent_key else k

												if isinstance(snake_case__	,			collections.MutableMapping     ):
																		items.extend(flatten_nested_dict(snake_case__	,			snake_case__	,			sep=snake_case__     ).items()     )
												else:
																		items.append((new_key, v)     )
						return dict(snake_case__     )
def 							UpperCamelCase_  (				__snake_case			:						int     )->      Optional[Any]:




						'''simple docstring'''
						return jax.tree_util.tree_map(lambda __snake_case     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x	,			snake_case__     )
def 							UpperCamelCase_  (				__snake_case			:						int	,			__snake_case			:						Union[str, Any]     )->      int:




						'''simple docstring'''
						__magic_name__		,			__magic_name__		,			__magic_name__ 		=			pt_attn_layer.in_proj_weight.chunk(3	,			dim=0     )
						__magic_name__		,			__magic_name__		,			__magic_name__ 		=			pt_attn_layer.in_proj_bias.chunk(3	,			dim=0     )

						__magic_name__ 		=			pt_attn_layer.out_proj.weight
						__magic_name__ 		=			pt_attn_layer.out_proj.bias

						__magic_name__ 		=			q_proj
						__magic_name__ 		=			q_proj_bias

						__magic_name__ 		=			k_proj
						__magic_name__ 		=			k_proj_bias

						__magic_name__ 		=			v_proj
						__magic_name__ 		=			v_proj_bias

						__magic_name__ 		=			out_proj_weights
						__magic_name__ 		=			out_proj_bias
def 							UpperCamelCase_  (				__snake_case			:						Optional[int]	,			__snake_case			:						List[str]     )->      int:




						'''simple docstring'''
						copy_linear(hf_mlp.fca	,			pt_mlp.c_fc     )
						copy_linear(hf_mlp.fca	,			pt_mlp.c_proj     )
def 							UpperCamelCase_  (				__snake_case			:						Optional[Any]	,			__snake_case			:						int     )->      int:




						'''simple docstring'''
						__magic_name__ 		=			pt_linear.weight
						__magic_name__ 		=			pt_linear.bias
def 							UpperCamelCase_  (				__snake_case			:						Optional[Any]	,			__snake_case			:						Optional[int]     )->      Optional[int]:




						'''simple docstring'''
						# copy layer norms
						copy_linear(hf_layer.layer_norma	,			pt_layer.ln_a     )
						copy_linear(hf_layer.layer_norma	,			pt_layer.ln_a     )

						# copy MLP
						copy_mlp(hf_layer.mlp	,			pt_layer.mlp     )

						# copy attn
						copy_attn_layer(hf_layer.self_attn	,			pt_layer.attn     )
def 							UpperCamelCase_  (				__snake_case			:						int	,			__snake_case			:						Union[str, Any]     )->      Any:




						'''simple docstring'''
						for hf_layer, pt_layer in zip(snake_case__	,			snake_case__     ):
												copy_layer(snake_case__	,			snake_case__     )
def 							UpperCamelCase_  (				__snake_case			:						List[Any]	,			__snake_case			:						Dict     )->      int:




						'''simple docstring'''
						# copy  embeds
						__magic_name__ 		=			pt_model.token_embedding.weight
						__magic_name__ 		=			pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm	,			pt_model.ln_final     )

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers	,			pt_model.transformer.resblocks     )
def 							UpperCamelCase_  (				__snake_case			:						List[str]	,			__snake_case			:						Union[str, Any]     )->      Optional[int]:




						'''simple docstring'''
						# copy projection
						__magic_name__ 		=			pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model	,			snake_case__     )
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						Optional[Any]     )->      Any:




						'''simple docstring'''
						# copy projection
						__magic_name__ 		=			pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layernorm	,			pt_model.visual.ln_pre     )
						copy_linear(hf_model.vision_model.post_layernorm	,			pt_model.visual.ln_post     )

						# copy embeds
						__magic_name__ 		=			pt_model.visual.conva.weight.data
						__magic_name__ 		=			pt_model.visual.class_embedding
						__magic_name__ 		=			pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers	,			pt_model.visual.transformer.resblocks     )
def 							UpperCamelCase_  (				__snake_case			:						List[str]	,			__snake_case			:						List[str]     )->      Any:




						'''simple docstring'''
						__magic_name__ 		=			flatten_nested_dict(flax_params['backbone']['merged_class_token']     )

						__magic_name__ 		=			torch.from_numpy(flax_class_token_params['scale']     )
						__magic_name__ 		=			torch.from_numpy(flax_class_token_params['bias']     )
						__magic_name__ 		=			nn.Parameter(snake_case__     )
						__magic_name__ 		=			nn.Parameter(snake_case__     )
def 							UpperCamelCase_  (				__snake_case			:						int	,			__snake_case			:						List[Any]     )->      Dict:




						'''simple docstring'''
						__magic_name__ 		=			hf_model.state_dict()
						__magic_name__ 		=			{}

						# Rename class prediction head flax params to pytorch HF
						__magic_name__ 		=			flatten_nested_dict(flax_params['class_head']     )

						for flax_key, v in flax_class_params.items():
												__magic_name__ 		=			flax_key.replace('/'	,			'.'     )
												__magic_name__ 		=			torch_key.replace('.kernel'	,			'.weight'     )
												__magic_name__ 		=			torch_key.replace('Dense_0'	,			'dense0'     )
												__magic_name__ 		=			'class_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		__magic_name__ 		=			v.T

												__magic_name__ 		=			nn.Parameter(torch.from_numpy(snake_case__     )     )

						# Rename box prediction box flax params to pytorch HF
						__magic_name__ 		=			flatten_nested_dict(flax_params['obj_box_head']     )

						for flax_key, v in flax_box_params.items():
												__magic_name__ 		=			flax_key.replace('/'	,			'.'     )
												__magic_name__ 		=			torch_key.replace('.kernel'	,			'.weight'     )
												__magic_name__ 		=			torch_key.replace('_'	,			''     ).lower()
												__magic_name__ 		=			'box_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		__magic_name__ 		=			v.T

												__magic_name__ 		=			nn.Parameter(torch.from_numpy(snake_case__     )     )

						# Copy flax params to PyTorch params
						for name, param in new_params.items():
												if name in pt_params.keys():
																		pt_params[name].copy_(snake_case__     )
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						str     )->      Dict:




						'''simple docstring'''
						for k, v in flax_attn_params.items():
												if k.startswith('transformer'     ):
																		__magic_name__ 		=			k.replace('transformer.resblocks'	,			'text_model.encoder.layers'     )
												else:
																		__magic_name__ 		=			k.replace('visual.transformer.resblocks'	,			'vision_model.encoder.layers'     )

												__magic_name__ 		=			torch_key.replace('attn'	,			'self_attn'     )
												__magic_name__ 		=			torch_key.replace('key'	,			'k_proj'     )
												__magic_name__ 		=			torch_key.replace('value'	,			'v_proj'     )
												__magic_name__ 		=			torch_key.replace('query'	,			'q_proj'     )
												__magic_name__ 		=			torch_key.replace('out'	,			'out_proj'     )

												if "bias" in torch_key and v.ndim == 2:
																		__magic_name__ 		=			v.shape[0] * v.shape[1]
																		__magic_name__ 		=			v.reshape(snake_case__     )

												if "weight" in torch_key and "out" in torch_key:
																		__magic_name__ 		=			(v.shape[0] * v.shape[1], v.shape[2])
																		__magic_name__ 		=			v.reshape(snake_case__     ).T

												if "weight" in torch_key and "out" not in torch_key:
																		__magic_name__ 		=			(v.shape[0], v.shape[1] * v.shape[2])
																		__magic_name__ 		=			v.reshape(snake_case__     ).T

												# Copy flax CLIP attn params to HF PyTorch params
												__magic_name__ 		=			torch.from_numpy(snake_case__     )
												hf_backbone.state_dict()[torch_key].copy_(snake_case__     )
def 							UpperCamelCase_  (				__snake_case			:						List[Any]     )->      List[str]:




						'''simple docstring'''
						__magic_name__ 		=			{}
						__magic_name__ 		=			[]

						for k, v in params.items():
												if "attn." in k:
																		__magic_name__ 		=			k[: k.rindex('attn.'     ) + 5]
																		if base in processed_attn_layers:
																								continue

																		processed_attn_layers.append(snake_case__     )
																		__magic_name__ 		=			params[base + 'out.weight'].shape[-1]
																		__magic_name__ 		=			params[base + 'out.weight'].reshape(snake_case__	,			snake_case__     ).T
																		__magic_name__ 		=			params[base + 'out.bias']
												else:
																		__magic_name__ 		=			v
						return new_params
def 							UpperCamelCase_  (				__snake_case			:						List[Any]	,			__snake_case			:						int     )->      str:




						'''simple docstring'''
						__magic_name__ 		=			CLIP(**snake_case__     )
						torch_model.eval()
						__magic_name__ 		=			torch_model.state_dict()

						__magic_name__ 		=			flatten_nested_dict(flax_params['backbone']['clip']     )
						__magic_name__ 		=			{}

						for flax_key, v in flax_clip_params.items():
												__magic_name__ 		=			flax_key.replace('/'	,			'.'     )
												__magic_name__ 		=			torch_key.replace('text.token_embedding.embedding'	,			'token_embedding.kernel'     )

												if (
												    torch_key.startswith('text.transformer'     )
												    or torch_key.startswith('text.text_projection'     )
												    or torch_key.startswith('text.ln_final'     )
												    or torch_key.startswith('text.positional_embedding'     )
												):
																		__magic_name__ 		=			torch_key[5:]

												__magic_name__ 		=			torch_key.replace('text_projection.kernel'	,			'text_projection'     )
												__magic_name__ 		=			torch_key.replace('visual.proj.kernel'	,			'visual.proj'     )
												__magic_name__ 		=			torch_key.replace('.scale'	,			'.weight'     )
												__magic_name__ 		=			torch_key.replace('.kernel'	,			'.weight'     )

												if "conv" in torch_key or "downsample.0.weight" in torch_key:
																		__magic_name__ 		=			v.transpose(3	,			2	,			0	,			1     )

												elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																		# Fully connected layers are transposed, embeddings are not
																		__magic_name__ 		=			v.T

												__magic_name__ 		=			v

						__magic_name__ 		=			_convert_attn_layers(snake_case__     )
						new_torch_params.update(snake_case__     )
						__magic_name__ 		=			{}

						# Copy flax CLIP backbone params to PyTorch params
						for name, param in new_torch_params.items():
												if name in torch_clip_params.keys():
																		__magic_name__ 		=			torch.from_numpy(new_torch_params[name]     )
																		torch_clip_params[name].copy_(snake_case__     )
												else:
																		__magic_name__ 		=			param

						return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 							UpperCamelCase_  (				__snake_case			:						List[str]	,			__snake_case			:						str	,			__snake_case			:						int	,			__snake_case			:						Union[str, Any]	,			__snake_case			:						int=None     )->      Optional[Any]:




						'''simple docstring'''
						__magic_name__ 		=			Repository(snake_case__	,			clone_from=F'''google/{pytorch_dump_folder_path}'''     )
						repo.git_pull()

						if config_path is not None:
												__magic_name__ 		=			OwlViTConfig.from_pretrained(snake_case__     )
						else:
												__magic_name__ 		=			OwlViTConfig()

						__magic_name__ 		=			OwlViTModel(snake_case__     ).eval()
						__magic_name__ 		=			OwlViTForObjectDetection(snake_case__     ).eval()

						copy_text_model_and_projection(snake_case__	,			snake_case__     )
						copy_vision_model_and_projection(snake_case__	,			snake_case__     )
						__magic_name__ 		=			pt_backbone.logit_scale
						copy_flax_attn_params(snake_case__	,			snake_case__     )

						__magic_name__ 		=			hf_backbone
						copy_class_merge_token(snake_case__	,			snake_case__     )
						copy_class_box_heads(snake_case__	,			snake_case__     )

						# Save HF model
						hf_model.save_pretrained(repo.local_dir     )

						# Initialize image processor
						__magic_name__ 		=			OwlViTImageProcessor(
						    size=config.vision_config.image_size	,			crop_size=config.vision_config.image_size     )
						# Initialize tokenizer
						__magic_name__ 		=			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'	,			pad_token='!'	,			model_max_length=16     )

						# Initialize processor
						__magic_name__ 		=			OwlViTProcessor(image_processor=snake_case__	,			tokenizer=snake_case__     )
						image_processor.save_pretrained(repo.local_dir     )
						processor.save_pretrained(repo.local_dir     )

						repo.git_add()
						repo.git_commit('Upload model and processor'     )
						repo.git_push()


if __name__ == "__main__":
					UpperCamelCase :				int						    =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--owlvit_version''',
					    default=None,
					    type=str,
					    required=True,
					    help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
					)
					parser.add_argument(
					    '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
					)
					parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
					)
					UpperCamelCase :				str						    =       parser.parse_args()

					# Initialize PyToch clip model
					UpperCamelCase :				Union[str, Any]						    =       args.owlvit_version
					if model_name == "clip_b16":
										UpperCamelCase :				Union[str, Any]						    =       CONFIGS['''vit_b16''']
					elif model_name == "clip_b32":
										UpperCamelCase :				Optional[Any]						    =       CONFIGS['''vit_b32''']
					elif model_name == "clip_l14":
										UpperCamelCase :				str						    =       CONFIGS['''vit_l14''']

					# Load from checkpoint and convert params to float-32
					UpperCamelCase :				List[str]						    =       checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
					UpperCamelCase :				Dict						    =       jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
					del variables

					# Convert CLIP backbone
					UpperCamelCase :				Any						    =       convert_clip_backbone(flax_params, torch_config)

					convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)





import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCamelCase :				Optional[Any]						    =       logging.get_logger(__name__)

@dataclass
class     __snake_case							:
					SCREAMING_SNAKE_CASE_		:					str
					SCREAMING_SNAKE_CASE_		:					str
					SCREAMING_SNAKE_CASE_		:					Optional[str] 				= None
					SCREAMING_SNAKE_CASE_		:					Optional[str] 				= None
					def   a (	self					:	Tuple     )       ->				Dict:



											'''simple docstring'''





											return json.dumps(dataclasses.asdict(self     )  ,			indent=2     ) + "\n"







@dataclass(frozen=_UpperCAmelCase)
class     __snake_case							:
					SCREAMING_SNAKE_CASE_		:					List[int]
					SCREAMING_SNAKE_CASE_		:					Optional[List[int]] 				= None
					SCREAMING_SNAKE_CASE_		:					Optional[List[int]] 				= None
					SCREAMING_SNAKE_CASE_		:					Optional[Union[int, float]] 				= None
					def   a (	self					:	List[str]     )       ->				str:



											'''simple docstring'''





											return json.dumps(dataclasses.asdict(self     )     ) + "\n"







class     __snake_case							:
					def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	List[str]     )       ->				List[Any]:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	str     )       ->				List[Any]:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	List[str]  ,			_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[Any]:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	Optional[int]  ,			_lowerCAmelCase					:	Union[str, Any]     )       ->				int:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	Any     )       ->				int:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	int  ,			_lowerCAmelCase					:	Optional[int]     )       ->				Dict:



											'''simple docstring'''





											if len(self.get_labels()     ) > 1:
																	__magic_name__ 		=			self.get_labels()[int(example.label     )]
											return example







					@classmethod
					def   a (	cls					:	Dict  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Optional[Any]=None     )       ->				int:



											'''simple docstring'''





											with open(_lowerCAmelCase  ,			'r'  ,			encoding='utf-8-sig'     ) as f:
																	return list(csv.reader(_lowerCAmelCase  ,			delimiter='\t'  ,			quotechar=_lowerCAmelCase     )     )







class     __snake_case							(      _UpperCAmelCase):
					def __init__(	self					:	List[Any]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			_lowerCAmelCase					:	List[Any]="classification"  ,			_lowerCAmelCase					:	List[Any]=False     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			[] if labels is None else labels
											__magic_name__ 		=			[] if examples is None else examples
											__magic_name__ 		=			mode
											__magic_name__ 		=			verbose
					def __len__(	self					:	Any     )       ->				Union[str, Any]:



											'''simple docstring'''





											return len(self.examples     )
					def __getitem__(	self					:	Tuple  ,			_lowerCAmelCase					:	Dict     )       ->				Any:



											'''simple docstring'''





											if isinstance(_lowerCAmelCase  ,			_lowerCAmelCase     ):
																	return SingleSentenceClassificationProcessor(labels=self.labels  ,			examples=self.examples[idx]     )
											return self.examples[idx]
					@classmethod
					def   a (	cls					:	List[Any]  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	Union[str, Any]=""  ,			_lowerCAmelCase					:	Optional[Any]=0  ,			_lowerCAmelCase					:	Optional[int]=1  ,			_lowerCAmelCase					:	List[str]=None  ,			_lowerCAmelCase					:	str=False  ,			**_lowerCAmelCase					:	Dict     )       ->				Union[str, Any]:



											'''simple docstring'''





											__magic_name__ 		=			cls(**_lowerCAmelCase     )
											processor.add_examples_from_csv(
											    _lowerCAmelCase  ,			split_name=_lowerCAmelCase  ,			column_label=_lowerCAmelCase  ,			column_text=_lowerCAmelCase  ,			column_id=_lowerCAmelCase  ,			skip_first_row=_lowerCAmelCase  ,			overwrite_labels=_lowerCAmelCase  ,			overwrite_examples=_lowerCAmelCase  ,			)
											return processor
					@classmethod
					def   a (	cls					:	Optional[Any]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Optional[Any]=None  ,			**_lowerCAmelCase					:	Union[str, Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			cls(**_lowerCAmelCase     )
											processor.add_examples(_lowerCAmelCase  ,			labels=_lowerCAmelCase     )
											return processor
					def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[Any]=""  ,			_lowerCAmelCase					:	Optional[Any]=0  ,			_lowerCAmelCase					:	str=1  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	List[str]=False  ,			_lowerCAmelCase					:	Dict=False  ,			_lowerCAmelCase					:	Any=False  ,			)       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self._read_tsv(_lowerCAmelCase     )
											if skip_first_row:
																	__magic_name__ 		=			lines[1:]
											__magic_name__ 		=			[]
											__magic_name__ 		=			[]
											__magic_name__ 		=			[]
											for i, line in enumerate(_lowerCAmelCase     ):
																	texts.append(line[column_text]     )
																	labels.append(line[column_label]     )
																	if column_id is not None:
																							ids.append(line[column_id]     )
																	else:
																							__magic_name__ 		=			F'''{split_name}-{i}''' if split_name else str(_lowerCAmelCase     )
																							ids.append(_lowerCAmelCase     )

											return self.add_examples(
											    _lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase  ,			overwrite_labels=_lowerCAmelCase  ,			overwrite_examples=_lowerCAmelCase     )
					def   a (	self					:	Any  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Optional[Any]=None  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	Optional[int]=False  ,			_lowerCAmelCase					:	List[str]=False     )       ->				Dict:



											'''simple docstring'''





											if labels is not None and len(_lowerCAmelCase     ) != len(_lowerCAmelCase     ):
																	raise ValueError(
																	    F'''Text and labels have mismatched lengths {len(_lowerCAmelCase     )} and {len(_lowerCAmelCase     )}'''     )
											if ids is not None and len(_lowerCAmelCase     ) != len(_lowerCAmelCase     ):
																	raise ValueError(F'''Text and ids have mismatched lengths {len(_lowerCAmelCase     )} and {len(_lowerCAmelCase     )}'''     )
											if ids is None:
																	__magic_name__ 		=			[None] * len(_lowerCAmelCase     )
											if labels is None:
																	__magic_name__ 		=			[None] * len(_lowerCAmelCase     )
											__magic_name__ 		=			[]
											__magic_name__ 		=			set()
											for text_or_text_and_label, label, guid in zip(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     ):
																	if isinstance(_lowerCAmelCase  ,			(tuple, list)     ) and label is None:
																							__magic_name__		,			__magic_name__ 		=			text_or_text_and_label
																	else:
																							__magic_name__ 		=			text_or_text_and_label
																	added_labels.add(_lowerCAmelCase     )
																	examples.append(InputExample(guid=_lowerCAmelCase  ,			text_a=_lowerCAmelCase  ,			text_b=_lowerCAmelCase  ,			label=_lowerCAmelCase     )     )

											# Update examples
											if overwrite_examples:
																	__magic_name__ 		=			examples
											else:
																	self.examples.extend(_lowerCAmelCase     )

											# Update labels
											if overwrite_labels:
																	__magic_name__ 		=			list(_lowerCAmelCase     )
											else:
																	__magic_name__ 		=			list(set(self.labels     ).union(_lowerCAmelCase     )     )

											return self.examples







					def   a (	self					:	Optional[int]  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	Optional[int]=None  ,			_lowerCAmelCase					:	Dict=False  ,			_lowerCAmelCase					:	Tuple=0  ,			_lowerCAmelCase					:	List[str]=True  ,			_lowerCAmelCase					:	List[str]=None  ,			)       ->				Dict:



											'''simple docstring'''





											if max_length is None:
																	__magic_name__ 		=			tokenizer.max_len

											__magic_name__ 		=			{label: i for i, label in enumerate(self.labels     )}

											__magic_name__ 		=			[]
											for ex_index, example in enumerate(self.examples     ):
																	if ex_index % 1_0_0_0_0 == 0:
																							logger.info(F'''Tokenizing example {ex_index}'''     )

																	__magic_name__ 		=			tokenizer.encode(
																	    example.text_a  ,			add_special_tokens=_lowerCAmelCase  ,			max_length=min(_lowerCAmelCase  ,			tokenizer.max_len     )  ,			)
																	all_input_ids.append(_lowerCAmelCase     )

											__magic_name__ 		=			max(len(_lowerCAmelCase     ) for input_ids in all_input_ids     )

											__magic_name__ 		=			[]
											for ex_index, (input_ids, example) in enumerate(zip(_lowerCAmelCase  ,			self.examples     )     ):
																	if ex_index % 1_0_0_0_0 == 0:
																							logger.info(F'''Writing example {ex_index}/{len(self.examples     )}'''     )
																	# The mask has 1 for real tokens and 0 for padding tokens. Only real
																	# tokens are attended to.
																	__magic_name__ 		=			[1 if mask_padding_with_zero else 0] * len(_lowerCAmelCase     )

																	# Zero-pad up to the sequence length.
																	__magic_name__ 		=			batch_length - len(_lowerCAmelCase     )
																	if pad_on_left:
																							__magic_name__ 		=			([pad_token] * padding_length) + input_ids
																							__magic_name__ 		=			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
																	else:
																							__magic_name__ 		=			input_ids + ([pad_token] * padding_length)
																							__magic_name__ 		=			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

																	if len(_lowerCAmelCase     ) != batch_length:
																							raise ValueError(F'''Error with input length {len(_lowerCAmelCase     )} vs {batch_length}'''     )
																	if len(_lowerCAmelCase     ) != batch_length:
																							raise ValueError(F'''Error with input length {len(_lowerCAmelCase     )} vs {batch_length}'''     )

																	if self.mode == "classification":
																							__magic_name__ 		=			label_map[example.label]
																	elif self.mode == "regression":
																							__magic_name__ 		=			float(example.label     )
																	else:
																							raise ValueError(self.mode     )

																	if ex_index < 5 and self.verbose:
																							logger.info('*** Example ***'     )
																							logger.info(F'''guid: {example.guid}'''     )
																							logger.info(F'''input_ids: {" ".join([str(_lowerCAmelCase     ) for x in input_ids]     )}'''     )
																							logger.info(F'''attention_mask: {" ".join([str(_lowerCAmelCase     ) for x in attention_mask]     )}'''     )
																							logger.info(F'''label: {example.label} (id = {label})'''     )

																	features.append(InputFeatures(input_ids=_lowerCAmelCase  ,			attention_mask=_lowerCAmelCase  ,			label=_lowerCAmelCase     )     )

											if return_tensors is None:
																	return features
											elif return_tensors == "tf":
																	if not is_tf_available():
																							raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'     )
																	import tensorflow as tf

																	def gen():
																							for ex in features:
																													yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

																	__magic_name__ 		=			tf.data.Dataset.from_generator(
																	    _lowerCAmelCase  ,			({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)  ,			({'input_ids': tf.TensorShape([None]     ), 'attention_mask': tf.TensorShape([None]     )}, tf.TensorShape([]     ))  ,			)
																	return dataset
											elif return_tensors == "pt":
																	if not is_torch_available():
																							raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'     )
																	import torch
																	from torch.utils.data import TensorDataset

																	__magic_name__ 		=			torch.tensor([f.input_ids for f in features]  ,			dtype=torch.long     )
																	__magic_name__ 		=			torch.tensor([f.attention_mask for f in features]  ,			dtype=torch.long     )
																	if self.mode == "classification":
																							__magic_name__ 		=			torch.tensor([f.label for f in features]  ,			dtype=torch.long     )
																	elif self.mode == "regression":
																							__magic_name__ 		=			torch.tensor([f.label for f in features]  ,			dtype=torch.float     )

																	__magic_name__ 		=			TensorDataset(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     )
																	return dataset
											else:
																	raise ValueError('return_tensors should be one of \'tf\' or \'pt\''     )

