




"""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 lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			CodeGenTokenizer
			lowerCamelCase__      =			CodeGenTokenizerFast
			lowerCamelCase__      =			True
			lowerCamelCase__      =			{"""add_prefix_space""": True}
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							super().setUp()

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

							_lowerCamelCase     :			Dict    =    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']  )
							_lowerCamelCase     :			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 					A_    (  self  ,      **lowercase  ):
							kwargs.update(self.special_tokens_map  )
							return CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      **lowercase  )
			def 					A_    (  self  ,      **lowercase  ):
							kwargs.update(self.special_tokens_map  )
							return CodeGenTokenizerFast.from_pretrained(self.tmpdirname  ,      **lowercase  )
			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			List[Any]    =    'lower newer'
							_lowerCamelCase     :			Union[str, Any]    =    'lower newer'
							return input_text, output_text
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    CodeGenTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map  )
							_lowerCamelCase     :			Optional[Any]    =    'lower newer'
							_lowerCamelCase     :			Any    =    ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
							_lowerCamelCase     :			Dict    =    tokenizer.tokenize(lowercase  ,      add_prefix_space=lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			str    =    tokens + [tokenizer.unk_token]
							_lowerCamelCase     :			str    =    [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase  )  ,      lowercase  )
			def 					A_    (  self  ):
							if not self.test_rust_tokenizer:
											return

							_lowerCamelCase     :			int    =    self.get_tokenizer()
							_lowerCamelCase     :			int    =    self.get_rust_tokenizer(add_prefix_space=lowercase  )

							_lowerCamelCase     :			Any    =    'lower newer'

							# Testing tokenization
							_lowerCamelCase     :			Any    =    tokenizer.tokenize(lowercase  ,      add_prefix_space=lowercase  )
							_lowerCamelCase     :			str    =    rust_tokenizer.tokenize(lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

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

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

							# Testing the unknown token
							_lowerCamelCase     :			Any    =    tokens + [rust_tokenizer.unk_token]
							_lowerCamelCase     :			Union[str, Any]    =    [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase  )  ,      lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							# It's very difficult to mix/test pretokenization with byte-level
							# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
							pass
			def 					A_    (  self  ,      lowercase=15  ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
															_lowerCamelCase     :			Dict    =    self.rust_tokenizer_class.from_pretrained(lowercase  ,      **lowercase  )

															# Simple input
															_lowerCamelCase     :			Optional[Any]    =    'This is a simple input'
															_lowerCamelCase     :			Tuple    =    ['This is a simple input 1', 'This is a simple input 2']
															_lowerCamelCase     :			List[str]    =    ('This is a simple input', 'This is a pair')
															_lowerCamelCase     :			Optional[int]    =    [
															    ('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 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      pad_token='<pad>'  )

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

							_lowerCamelCase     :			Tuple    =    tokenizer.pad_token_id

							_lowerCamelCase     :			Optional[Any]    =    tokenizer(lowercase  ,      padding='max_length'  ,      max_length=30  ,      return_tensors='np'  )
							_lowerCamelCase     :			List[Any]    =    tokenizer(lowercase  ,      padding=lowercase  ,      truncate=lowercase  ,      return_tensors='np'  )
							_lowerCamelCase     :			str    =    tokenizer(*lowercase  ,      padding='max_length'  ,      max_length=60  ,      return_tensors='np'  )
							_lowerCamelCase     :			str    =    tokenizer(lowercase  ,      padding=lowercase  ,      truncate=lowercase  ,      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 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    '$$$'
							_lowerCamelCase     :			Union[str, Any]    =    CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      bos_token=lowercase  ,      add_bos_token=lowercase  )

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

							_lowerCamelCase     :			List[str]    =    tokenizer.bos_token_id

							_lowerCamelCase     :			List[Any]    =    tokenizer(lowercase  )
							_lowerCamelCase     :			int    =    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  )  )

							_lowerCamelCase     :			Any    =    tokenizer.decode(out_s.input_ids  )
							_lowerCamelCase     :			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 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'  )

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

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






			def 					A_    (  self  ):
							pass





"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states