


'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[int]						,				_SCREAMING_SNAKE_CASE				:       Tuple=1       ):
					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('.'       )[n_shave_prefix_segments:]       )
					else:
										return ".".join(path.split('.'       )[:n_shave_prefix_segments]       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       int=0       ):
					__a      :	str    =  []
					for old_item in old_list:
										__a      :	Union[str, Any]    =  old_item.replace('in_layers.0'						,				'norm1'       )
										__a      :	str    =  new_item.replace('in_layers.2'						,				'conv1'       )

										__a      :	Optional[Any]    =  new_item.replace('out_layers.0'						,				'norm2'       )
										__a      :	Tuple    =  new_item.replace('out_layers.3'						,				'conv2'       )

										__a      :	List[Any]    =  new_item.replace('emb_layers.1'						,				'time_emb_proj'       )
										__a      :	int    =  new_item.replace('skip_connection'						,				'conv_shortcut'       )

										__a      :	Tuple    =  shave_segments(_SCREAMING_SNAKE_CASE						,				n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

										mapping.append({'old': old_item, 'new': new_item}       )

					return mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       Dict=0       ):
					__a      :	List[str]    =  []
					for old_item in old_list:
										__a      :	Optional[Any]    =  old_item

										__a      :	int    =  new_item.replace('norm.weight'						,				'group_norm.weight'       )
										__a      :	int    =  new_item.replace('norm.bias'						,				'group_norm.bias'       )

										__a      :	int    =  new_item.replace('proj_out.weight'						,				'proj_attn.weight'       )
										__a      :	str    =  new_item.replace('proj_out.bias'						,				'proj_attn.bias'       )

										__a      :	Dict    =  shave_segments(_SCREAMING_SNAKE_CASE						,				n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

										mapping.append({'old': old_item, 'new': new_item}       )

					return mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Tuple=None						,				_SCREAMING_SNAKE_CASE				:       Dict=None						,				_SCREAMING_SNAKE_CASE				:       Optional[int]=None       ):
					assert isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

					# Splits the attention layers into three variables.
					if attention_paths_to_split is not None:
										for path, path_map in attention_paths_to_split.items():
															__a      :	Dict    =  old_checkpoint[path]
															__a      :	List[str]    =  old_tensor.shape[0] // 3

															__a      :	str    =  (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

															__a      :	Tuple    =  old_tensor.shape[0] // config['num_head_channels'] // 3

															__a      :	Optional[int]    =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
															__a   ,    __a   ,    __a      :	str    =  old_tensor.split(channels // num_heads						,				dim=1       )

															__a      :	Any    =  query.reshape(_SCREAMING_SNAKE_CASE       )
															__a      :	List[str]    =  key.reshape(_SCREAMING_SNAKE_CASE       )
															__a      :	List[Any]    =  value.reshape(_SCREAMING_SNAKE_CASE       )

					for path in paths:
										__a      :	int    =  path['new']

										# These have already been assigned
										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										# Global renaming happens here
										__a      :	Tuple    =  new_path.replace('middle_block.0'						,				'mid_block.resnets.0'       )
										__a      :	Dict    =  new_path.replace('middle_block.1'						,				'mid_block.attentions.0'       )
										__a      :	Optional[int]    =  new_path.replace('middle_block.2'						,				'mid_block.resnets.1'       )

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				__a      :	List[Any]    =  new_path.replace(replacement['old']						,				replacement['new']       )

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															__a      :	List[str]    =  old_checkpoint[path['old']][:, :, 0]
										else:
															__a      :	Optional[int]    =  old_checkpoint[path['old']]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[Any]       ):
					__a      :	int    =  {}

					__a      :	Dict    =  checkpoint['time_embed.0.weight']
					__a      :	Union[str, Any]    =  checkpoint['time_embed.0.bias']
					__a      :	List[str]    =  checkpoint['time_embed.2.weight']
					__a      :	Any    =  checkpoint['time_embed.2.bias']

					__a      :	List[Any]    =  checkpoint['input_blocks.0.0.weight']
					__a      :	Union[str, Any]    =  checkpoint['input_blocks.0.0.bias']

					__a      :	Optional[int]    =  checkpoint['out.0.weight']
					__a      :	List[str]    =  checkpoint['out.0.bias']
					__a      :	Any    =  checkpoint['out.2.weight']
					__a      :	str    =  checkpoint['out.2.bias']

					# Retrieves the keys for the input blocks only
					__a      :	Optional[Any]    =  len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'input_blocks' in layer}       )
					__a      :	Optional[int]    =  {
					    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
					    for layer_id in range(_SCREAMING_SNAKE_CASE       )
					}

					# Retrieves the keys for the middle blocks only
					__a      :	Tuple    =  len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'middle_block' in layer}       )
					__a      :	List[Any]    =  {
					    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
					    for layer_id in range(_SCREAMING_SNAKE_CASE       )
					}

					# Retrieves the keys for the output blocks only
					__a      :	Optional[int]    =  len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'output_blocks' in layer}       )
					__a      :	Tuple    =  {
					    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
					    for layer_id in range(_SCREAMING_SNAKE_CASE       )
					}

					for i in range(1						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	List[Any]    =  (i - 1) // (config['num_res_blocks'] + 1)
										__a      :	List[Any]    =  (i - 1) % (config['num_res_blocks'] + 1)

										__a      :	Any    =  [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
										__a      :	str    =  [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

										if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
															__a      :	Optional[Any]    =  checkpoint[
															    F"""input_blocks.{i}.0.op.weight"""
															]
															__a      :	Any    =  checkpoint[
															    F"""input_blocks.{i}.0.op.bias"""
															]
															continue

										__a      :	List[str]    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
										__a      :	List[str]    =  {'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
										__a      :	List[Any]    =  {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
										assign_to_checkpoint(
										    _SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				additional_replacements=[meta_path, resnet_op]						,				config=_SCREAMING_SNAKE_CASE       )

										if len(_SCREAMING_SNAKE_CASE       ):
															__a      :	Optional[int]    =  renew_attention_paths(_SCREAMING_SNAKE_CASE       )
															__a      :	Optional[int]    =  {
															    'old': F"""input_blocks.{i}.1""",
															    'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
															}
															__a      :	Optional[int]    =  {
															    F"""input_blocks.{i}.1.qkv.bias""": {
															        'key': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
															        'query': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
															        'value': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
															    },
															    F"""input_blocks.{i}.1.qkv.weight""": {
															        'key': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
															        'query': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
															        'value': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
															    },
															}
															assign_to_checkpoint(
															    _SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				additional_replacements=[meta_path]						,				attention_paths_to_split=_SCREAMING_SNAKE_CASE						,				config=_SCREAMING_SNAKE_CASE						,				)

					__a      :	Union[str, Any]    =  middle_blocks[0]
					__a      :	List[Any]    =  middle_blocks[1]
					__a      :	Tuple    =  middle_blocks[2]

					__a      :	Any    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
					assign_to_checkpoint(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				config=_SCREAMING_SNAKE_CASE       )

					__a      :	Dict    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
					assign_to_checkpoint(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				config=_SCREAMING_SNAKE_CASE       )

					__a      :	List[Any]    =  renew_attention_paths(_SCREAMING_SNAKE_CASE       )
					__a      :	Any    =  {
					    'middle_block.1.qkv.bias': {
					        'key': 'mid_block.attentions.0.key.bias',
					        'query': 'mid_block.attentions.0.query.bias',
					        'value': 'mid_block.attentions.0.value.bias',
					    },
					    'middle_block.1.qkv.weight': {
					        'key': 'mid_block.attentions.0.key.weight',
					        'query': 'mid_block.attentions.0.query.weight',
					        'value': 'mid_block.attentions.0.value.weight',
					    },
					}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				attention_paths_to_split=_SCREAMING_SNAKE_CASE						,				config=_SCREAMING_SNAKE_CASE       )

					for i in range(_SCREAMING_SNAKE_CASE       ):
										__a      :	Tuple    =  i // (config['num_res_blocks'] + 1)
										__a      :	List[str]    =  i % (config['num_res_blocks'] + 1)
										__a      :	Optional[Any]    =  [shave_segments(_SCREAMING_SNAKE_CASE						,				2       ) for name in output_blocks[i]]
										__a      :	Dict    =  {}

										for layer in output_block_layers:
															__a   ,    __a      :	Optional[int]    =  layer.split('.'       )[0], shave_segments(_SCREAMING_SNAKE_CASE						,				1       )
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE       )
															else:
																				__a      :	str    =  [layer_name]

										if len(_SCREAMING_SNAKE_CASE       ) > 1:
															__a      :	List[str]    =  [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
															__a      :	List[Any]    =  [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

															__a      :	str    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
															__a      :	Union[str, Any]    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE       )

															__a      :	List[str]    =  {'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				additional_replacements=[meta_path]						,				config=_SCREAMING_SNAKE_CASE       )

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				__a      :	List[str]    =  list(output_block_list.values()       ).index(['conv.weight', 'conv.bias']       )
																				__a      :	Optional[int]    =  checkpoint[
																				    F"""output_blocks.{i}.{index}.conv.weight"""
																				]
																				__a      :	Any    =  checkpoint[
																				    F"""output_blocks.{i}.{index}.conv.bias"""
																				]

																				# Clear attentions as they have been attributed above.
																				if len(_SCREAMING_SNAKE_CASE       ) == 2:
																									__a      :	List[str]    =  []

															if len(_SCREAMING_SNAKE_CASE       ):
																				__a      :	List[str]    =  renew_attention_paths(_SCREAMING_SNAKE_CASE       )
																				__a      :	Optional[Any]    =  {
																				    'old': F"""output_blocks.{i}.1""",
																				    'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																				}
																				__a      :	List[Any]    =  {
																				    F"""output_blocks.{i}.1.qkv.bias""": {
																				        'key': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																				        'query': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																				        'value': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																				    },
																				    F"""output_blocks.{i}.1.qkv.weight""": {
																				        'key': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																				        'query': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																				        'value': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																				    },
																				}
																				assign_to_checkpoint(
																				    _SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				additional_replacements=[meta_path]						,				attention_paths_to_split=to_split if any('qkv' in key for key in attentions       ) else None						,				config=_SCREAMING_SNAKE_CASE						,				)
										else:
															__a      :	Any    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE						,				n_shave_prefix_segments=1       )
															for path in resnet_0_paths:
																				__a      :	int    =  '.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE       ), path['old']]       )
																				__a      :	Dict    =  '.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE       ), 'resnets', str(_SCREAMING_SNAKE_CASE       ), path['new']]       )

																				__a      :	Optional[int]    =  checkpoint[old_path]

					return new_checkpoint


if __name__ == "__main__":
	__lowercase     :						Tuple	=     argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)

	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The config json file corresponding to the architecture.',
	)

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

	__lowercase     :						Union[str, Any]	=     parser.parse_args()

	__lowercase     :						Dict	=     torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		__lowercase     :						Optional[int]	=     json.loads(f.read())

	__lowercase     :						str	=     convert_ldm_checkpoint(checkpoint, config)

	if "ldm" in config:
		del config["ldm"]

	__lowercase     :						Optional[int]	=     UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__lowercase     :						List[str]	=     DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
		__lowercase     :						Optional[int]	=     VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

		__lowercase     :						int	=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)







'''simple docstring'''



import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							__UpperCamelCase    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   CodeGenTokenizer
	A_           =   CodeGenTokenizerFast
	A_           =   True
	A_           =   {"add_prefix_space": True}
	A_           =   False




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__a      :	Tuple    =  [
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						    '<|endoftext|>',
						]
						__a      :	Union[str, Any]    =  dict(zip(__a						,    range(len(__a							)							)							)							)
						__a      :	Tuple    =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						__a      :	Dict    =  {'unk_token': '<unk>'}

						__a      :	List[Any]    =  os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['vocab_file']							)
						__a      :	List[str]    =  os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['merges_file']							)
						with open(self.vocab_file						,    'w'						,    encoding='utf-8'							) as fp:
											fp.write(json.dumps(__a							) + '\n'							)
						with open(self.merges_file						,    'w'						,    encoding='utf-8'							) as fp:
											fp.write('\n'.join(__a							)							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						kwargs.update(self.special_tokens_map							)
						return CodeGenTokenizer.from_pretrained(self.tmpdirname						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						kwargs.update(self.special_tokens_map							)
						return CodeGenTokenizerFast.from_pretrained(self.tmpdirname						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Tuple    =  'lower newer'
						__a      :	Tuple    =  'lower newer'
						return input_text, output_text




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  CodeGenTokenizer(self.vocab_file						,    self.merges_file						,    **self.special_tokens_map							)
						__a      :	str    =  'lower newer'
						__a      :	Tuple    =  ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
						__a      :	Dict    =  tokenizer.tokenize(__a						,    add_prefix_space=__a							)
						self.assertListEqual(__a						,    __a							)

						__a      :	List[str]    =  tokens + [tokenizer.unk_token]
						__a      :	Any    =  [14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__a							)						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						if not self.test_rust_tokenizer:
											return

						__a      :	List[Any]    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_rust_tokenizer(add_prefix_space=__a							)

						__a      :	Any    =  'lower newer'

						# Testing tokenization
						__a      :	Dict    =  tokenizer.tokenize(__a						,    add_prefix_space=__a							)
						__a      :	Dict    =  rust_tokenizer.tokenize(__a							)
						self.assertListEqual(__a						,    __a							)

						# Testing conversion to ids without special tokens
						__a      :	int    =  tokenizer.encode(__a						,    add_special_tokens=__a						,    add_prefix_space=__a							)
						__a      :	Tuple    =  rust_tokenizer.encode(__a						,    add_special_tokens=__a							)
						self.assertListEqual(__a						,    __a							)

						# Testing conversion to ids with special tokens
						__a      :	Tuple    =  self.get_rust_tokenizer(add_prefix_space=__a							)
						__a      :	Union[str, Any]    =  tokenizer.encode(__a						,    add_prefix_space=__a							)
						__a      :	int    =  rust_tokenizer.encode(__a							)
						self.assertListEqual(__a						,    __a							)

						# Testing the unknown token
						__a      :	Any    =  tokens + [rust_tokenizer.unk_token]
						__a      :	Tuple    =  [14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__a							)						,    __a							)




	def 				__UpperCAmelCase							(       self						,    *__a						,    **__a							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self						,    __a=15							):

						'''simple docstring'''





						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																__a      :	Optional[int]    =  self.rust_tokenizer_class.from_pretrained(__a						,    **__a							)

																# Simple input
																__a      :	List[Any]    =  'This is a simple input'
																__a      :	Tuple    =  ['This is a simple input 1', 'This is a simple input 2']
																__a      :	Tuple    =  ('This is a simple input', 'This is a pair')
																__a      :	str    =  [
																    ('This is a simple input 1', 'This is a simple input 2'),
																    ('This is a simple pair 1', 'This is a simple pair 2'),
																]

																# Simple input tests
																self.assertRaises(__a						,    tokenizer_r.encode						,    __a						,    max_length=__a						,    padding='max_length'							)

																# Simple input
																self.assertRaises(__a						,    tokenizer_r.encode_plus						,    __a						,    max_length=__a						,    padding='max_length'							)

																# Simple input
																self.assertRaises(
																    __a						,    tokenizer_r.batch_encode_plus						,    __a						,    max_length=__a						,    padding='max_length'						,    )

																# Pair input
																self.assertRaises(__a						,    tokenizer_r.encode						,    __a						,    max_length=__a						,    padding='max_length'							)

																# Pair input
																self.assertRaises(__a						,    tokenizer_r.encode_plus						,    __a						,    max_length=__a						,    padding='max_length'							)

																# Pair input
																self.assertRaises(
																    __a						,    tokenizer_r.batch_encode_plus						,    __a						,    max_length=__a						,    padding='max_length'						,    )




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  CodeGenTokenizer.from_pretrained(self.tmpdirname						,    pad_token='<pad>'							)

						# Simple input
						__a      :	str    =  'This is a simple input'
						__a      :	Any    =  ['This is a simple input looooooooong', 'This is a simple input']
						__a      :	Optional[int]    =  ('This is a simple input', 'This is a pair')
						__a      :	Optional[Any]    =  [
						    ('This is a simple input loooooong', 'This is a simple input'),
						    ('This is a simple pair loooooong', 'This is a simple pair'),
						]

						__a      :	int    =  tokenizer.pad_token_id

						__a      :	List[Any]    =  tokenizer(__a						,    padding='max_length'						,    max_length=30						,    return_tensors='np'							)
						__a      :	Union[str, Any]    =  tokenizer(__a						,    padding=__a						,    truncate=__a						,    return_tensors='np'							)
						__a      :	Optional[Any]    =  tokenizer(*__a						,    padding='max_length'						,    max_length=60						,    return_tensors='np'							)
						__a      :	List[Any]    =  tokenizer(__a						,    padding=__a						,    truncate=__a						,    return_tensors='np'							)

						# s
						# test single string max_length padding
						self.assertEqual(out_s['input_ids'].shape[-1]						,    30							)
						self.assertTrue(pad_token_id in out_s['input_ids']							)
						self.assertTrue(0 in out_s['attention_mask']							)

						# s2
						# test automatic padding
						self.assertEqual(out_sa['input_ids'].shape[-1]						,    33							)
						# long slice doesn't have padding
						self.assertFalse(pad_token_id in out_sa['input_ids'][0]							)
						self.assertFalse(0 in out_sa['attention_mask'][0]							)
						# short slice does have padding
						self.assertTrue(pad_token_id in out_sa['input_ids'][1]							)
						self.assertTrue(0 in out_sa['attention_mask'][1]							)

						# p
						# test single pair max_length padding
						self.assertEqual(out_p['input_ids'].shape[-1]						,    60							)
						self.assertTrue(pad_token_id in out_p['input_ids']							)
						self.assertTrue(0 in out_p['attention_mask']							)

						# p2
						# test automatic padding pair
						self.assertEqual(out_pa['input_ids'].shape[-1]						,    52							)
						# long slice pair doesn't have padding
						self.assertFalse(pad_token_id in out_pa['input_ids'][0]							)
						self.assertFalse(0 in out_pa['attention_mask'][0]							)
						# short slice pair does have padding
						self.assertTrue(pad_token_id in out_pa['input_ids'][1]							)
						self.assertTrue(0 in out_pa['attention_mask'][1]							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  '$$$'
						__a      :	List[str]    =  CodeGenTokenizer.from_pretrained(self.tmpdirname						,    bos_token=__a						,    add_bos_token=__a							)

						__a      :	Union[str, Any]    =  'This is a simple input'
						__a      :	List[Any]    =  ['This is a simple input 1', 'This is a simple input 2']

						__a      :	List[Any]    =  tokenizer.bos_token_id

						__a      :	List[str]    =  tokenizer(__a							)
						__a      :	Optional[Any]    =  tokenizer(__a							)

						self.assertEqual(out_s.input_ids[0]						,    __a							)
						self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids							)							)

						__a      :	Any    =  tokenizer.decode(out_s.input_ids							)
						__a      :	Union[str, Any]    =  tokenizer.batch_decode(out_sa.input_ids							)

						self.assertEqual(decode_s.split()[0]						,    __a							)
						self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa							)							)




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'							)

						__a      :	Optional[int]    =  '\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
						__a      :	Tuple    =  '\nif len_a > len_b:      result = a\nelse:      result = b'

						__a      :	Optional[int]    =  tokenizer.encode(__a							)
						__a      :	Union[str, Any]    =  ['^#', re.escape('<|endoftext|>'							), '^\'\'\'', '^"""', '\n\n\n']
						__a      :	Tuple    =  tokenizer.decode(__a						,    truncate_before_pattern=__a							)
						self.assertEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass



