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						( __A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = CodeGenTokenizer
				lowerCamelCase_    = CodeGenTokenizerFast
				lowerCamelCase_    = True
				lowerCamelCase_    = {'''add_prefix_space''': True}
				lowerCamelCase_    = False
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						super().setUp()

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

						A_		:    str         =				os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['vocab_file'] )
						A_		:    Tuple         =				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(lowercase ) + '\n' )
						with open(self.merges_file		,     'w'		,     encoding='utf-8' ) as fp:
								fp.write('\n'.join(lowercase ) )
				def   lowerCAmelCase_       (						self		,     **lowercase ):



						"""simple docstring"""





						kwargs.update(self.special_tokens_map )
						return CodeGenTokenizer.from_pretrained(self.tmpdirname		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     **lowercase ):



						"""simple docstring"""





						kwargs.update(self.special_tokens_map )
						return CodeGenTokenizerFast.from_pretrained(self.tmpdirname		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				'lower newer'
						A_		:    List[str]         =				'lower newer'
						return input_text, output_text
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

						A_		:    List[str]         =				tokens + [tokenizer.unk_token]
						A_		:    List[str]         =				[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase )		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						if not self.test_rust_tokenizer:
								return

						A_		:    Optional[Any]         =				self.get_tokenizer()
						A_		:    Any         =				self.get_rust_tokenizer(add_prefix_space=lowercase )

						A_		:    Union[str, Any]         =				'lower newer'

						# Testing tokenization
						A_		:    Dict         =				tokenizer.tokenize(lowercase		,     add_prefix_space=lowercase )
						A_		:    Union[str, Any]         =				rust_tokenizer.tokenize(lowercase )
						self.assertListEqual(lowercase		,     lowercase )

						# Testing conversion to ids without special tokens
						A_		:    int         =				tokenizer.encode(lowercase		,     add_special_tokens=lowercase		,     add_prefix_space=lowercase )
						A_		:    List[Any]         =				rust_tokenizer.encode(lowercase		,     add_special_tokens=lowercase )
						self.assertListEqual(lowercase		,     lowercase )

						# Testing conversion to ids with special tokens
						A_		:    Dict         =				self.get_rust_tokenizer(add_prefix_space=lowercase )
						A_		:    List[Any]         =				tokenizer.encode(lowercase		,     add_prefix_space=lowercase )
						A_		:    Dict         =				rust_tokenizer.encode(lowercase )
						self.assertListEqual(lowercase		,     lowercase )

						# Testing the unknown token
						A_		:    Dict         =				tokens + [rust_tokenizer.unk_token]
						A_		:    Any         =				[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase )		,     lowercase )
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						pass
				def   lowerCAmelCase_       (						self		,     lowercase=1_5 ):



						"""simple docstring"""





						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
										A_		:    Dict         =				self.rust_tokenizer_class.from_pretrained(lowercase		,     **lowercase )

										# Simple input
										A_		:    List[str]         =				'This is a simple input'
										A_		:    List[Any]         =				['This is a simple input 1', 'This is a simple input 2']
										A_		:    Optional[Any]         =				('This is a simple input', 'This is a pair')
										A_		:    Tuple         =				[
										    ('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(lowercase		,     tokenizer_r.encode		,     lowercase		,     max_length=lowercase		,     padding='max_length' )

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

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

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

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

										# Pair input
										self.assertRaises(
										    lowercase		,     tokenizer_r.batch_encode_plus		,     lowercase		,     max_length=lowercase		,     padding='max_length'		,     )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				CodeGenTokenizer.from_pretrained(self.tmpdirname		,     pad_token='<pad>' )

						# Simple input
						A_		:    Any         =				'This is a simple input'
						A_		:    Any         =				['This is a simple input looooooooong', 'This is a simple input']
						A_		:    Any         =				('This is a simple input', 'This is a pair')
						A_		:    Any         =				[
						    ('This is a simple input loooooong', 'This is a simple input'),
						    ('This is a simple pair loooooong', 'This is a simple pair'),
						]

						A_		:    Union[str, Any]         =				tokenizer.pad_token_id

						A_		:    Union[str, Any]         =				tokenizer(lowercase		,     padding='max_length'		,     max_length=3_0		,     return_tensors='np' )
						A_		:    str         =				tokenizer(lowercase		,     padding=lowercase		,     truncate=lowercase		,     return_tensors='np' )
						A_		:    Tuple         =				tokenizer(*lowercase		,     padding='max_length'		,     max_length=6_0		,     return_tensors='np' )
						A_		:    Optional[Any]         =				tokenizer(lowercase		,     padding=lowercase		,     truncate=lowercase		,     return_tensors='np' )

						# s
						# test single string max_length padding
						self.assertEqual(out_s['input_ids'].shape[-1]		,     3_0 )
						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]		,     3_3 )
						# 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]		,     6_0 )
						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]		,     5_2 )
						# 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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				'$$$'
						A_		:    Dict         =				CodeGenTokenizer.from_pretrained(self.tmpdirname		,     bos_token=lowercase		,     add_bos_token=lowercase )

						A_		:    Dict         =				'This is a simple input'
						A_		:    Dict         =				['This is a simple input 1', 'This is a simple input 2']

						A_		:    Optional[int]         =				tokenizer.bos_token_id

						A_		:    List[str]         =				tokenizer(lowercase )
						A_		:    str         =				tokenizer(lowercase )

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

						A_		:    Dict         =				tokenizer.decode(out_s.input_ids )
						A_		:    List[str]         =				tokenizer.batch_decode(out_sa.input_ids )

						self.assertEqual(decode_s.split()[0]		,     lowercase )
						self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )

						A_		:    Optional[Any]         =				'\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
						A_		:    str         =				'\nif len_a > len_b:      result = a\nelse:      result = b'

						A_		:    Dict         =				tokenizer.encode(lowercase )
						A_		:    Dict         =				['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
						A_		:    Optional[int]         =				tokenizer.decode(lowercase		,     truncate_before_pattern=lowercase )
						self.assertEqual(lowercase		,     lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						pass







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    int         =				UNetaDModel(
						    block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=3		,     out_channels=3		,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[Any]         =				VQModel(
						    block_out_channels=[3_2, 6_4]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=3		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				CLIPTextConfig(
						    bos_token_id=0		,     eos_token_id=2		,     hidden_size=3_2		,     intermediate_size=3_7		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_0_0_0		,     )
						return CLIPTextModel(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    Dict         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy' ).images

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy'		,     return_dict=lowercase )[0]

						A_		:    Union[str, Any]         =				image[0, -3:, -3:, -1]
						A_		:    int         =				image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A_		:    List[str]         =				np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
						A_		:    str         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance





@slow
@require_torch
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=5		,     output_type='numpy' ).images

						A_		:    List[Any]         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance






