


import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


A							:					List[str]     						=     get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
	from transformers.models.plbart.modeling_plbart import shift_tokens_right

A							:					List[Any]     						=     50003
A							:					List[Any]     						=     50002






@require_sentencepiece
@require_tokenizers
class     UpperCamelCase(  _a     ,							unittest.TestCase   ):
					snake_case_		:				Optional[Any]										=	PLBartTokenizer
					snake_case_		:				Optional[int]										=	None
					snake_case_		:				List[str]										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											super().setUp()

											# We have a SentencePiece fixture for testing
											__snake_case				=		PLBartTokenizer(SCREAMING_SNAKE_CASE     ,    language_codes="base"     ,    keep_accents=SCREAMING_SNAKE_CASE						)
											tokenizer.save_pretrained(self.tmpdirname						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		PLBartTokenizer(SCREAMING_SNAKE_CASE     ,    language_codes="base"     ,    keep_accents=SCREAMING_SNAKE_CASE						)

											__snake_case				=		tokenizer.tokenize("This is a test"						)
											self.assertListEqual(SCREAMING_SNAKE_CASE     ,    ["▁This", "▁is", "▁a", "▁t", "est"]						)

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						)     ,    [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,    )

											__snake_case				=		tokenizer.tokenize("I was born in 92000, and this is falsé."						)
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE     ,    [
											        SPIECE_UNDERLINE + "I",
											        SPIECE_UNDERLINE + "was",
											        SPIECE_UNDERLINE + "b",
											        "or",
											        "n",
											        SPIECE_UNDERLINE + "in",
											        SPIECE_UNDERLINE + "",
											        "9",
											        "2",
											        "0",
											        "0",
											        "0",
											        ",",
											        SPIECE_UNDERLINE + "and",
											        SPIECE_UNDERLINE + "this",
											        SPIECE_UNDERLINE + "is",
											        SPIECE_UNDERLINE + "f",
											        "al",
											        "s",
											        "é",
											        ".",
											    ]     ,    )
											__snake_case				=		tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						)
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE     ,    [
											        value + tokenizer.fairseq_offset
											        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
											    ]     ,    )

											__snake_case				=		tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE						)
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE     ,    [
											        SPIECE_UNDERLINE + "I",
											        SPIECE_UNDERLINE + "was",
											        SPIECE_UNDERLINE + "b",
											        "or",
											        "n",
											        SPIECE_UNDERLINE + "in",
											        SPIECE_UNDERLINE + "",
											        "<unk>",
											        "2",
											        "0",
											        "0",
											        "0",
											        ",",
											        SPIECE_UNDERLINE + "and",
											        SPIECE_UNDERLINE + "this",
											        SPIECE_UNDERLINE + "is",
											        SPIECE_UNDERLINE + "f",
											        "al",
											        "s",
											        "<unk>",
											        ".",
											    ]     ,    )

											__snake_case				=		tokenizer.vocab_size
											__snake_case				=		[tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE						) for x in range(end - 4     ,    SCREAMING_SNAKE_CASE						)]

											self.assertListEqual(SCREAMING_SNAKE_CASE     ,    ["__java__", "__python__", "__en_XX__", "<mask>"]						)

											__snake_case				=		"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE						).input_ids
											self.assertEqual(
											    tokenizer.decode(SCREAMING_SNAKE_CASE     ,    skip_special_tokens=SCREAMING_SNAKE_CASE     ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)     ,    SCREAMING_SNAKE_CASE     ,    )






					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Any:




											'''simple docstring'''

											__snake_case				=		PLBartTokenizer(SCREAMING_SNAKE_CASE     ,    language_codes="multi"     ,    keep_accents=SCREAMING_SNAKE_CASE						)

											__snake_case				=		tokenizer.tokenize("This is a test"						)
											self.assertListEqual(SCREAMING_SNAKE_CASE     ,    ["▁This", "▁is", "▁a", "▁t", "est"]						)

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						)     ,    [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,    )

											__snake_case				=		tokenizer.tokenize("I was born in 92000, and this is falsé."						)
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE     ,    [
											        SPIECE_UNDERLINE + "I",
											        SPIECE_UNDERLINE + "was",
											        SPIECE_UNDERLINE + "b",
											        "or",
											        "n",
											        SPIECE_UNDERLINE + "in",
											        SPIECE_UNDERLINE + "",
											        "9",
											        "2",
											        "0",
											        "0",
											        "0",
											        ",",
											        SPIECE_UNDERLINE + "and",
											        SPIECE_UNDERLINE + "this",
											        SPIECE_UNDERLINE + "is",
											        SPIECE_UNDERLINE + "f",
											        "al",
											        "s",
											        "é",
											        ".",
											    ]     ,    )
											__snake_case				=		tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						)
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE     ,    [
											        value + tokenizer.fairseq_offset
											        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
											    ]     ,    )

											__snake_case				=		tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE						)
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE     ,    [
											        SPIECE_UNDERLINE + "I",
											        SPIECE_UNDERLINE + "was",
											        SPIECE_UNDERLINE + "b",
											        "or",
											        "n",
											        SPIECE_UNDERLINE + "in",
											        SPIECE_UNDERLINE + "",
											        "<unk>",
											        "2",
											        "0",
											        "0",
											        "0",
											        ",",
											        SPIECE_UNDERLINE + "and",
											        SPIECE_UNDERLINE + "this",
											        SPIECE_UNDERLINE + "is",
											        SPIECE_UNDERLINE + "f",
											        "al",
											        "s",
											        "<unk>",
											        ".",
											    ]     ,    )
											__snake_case				=		tokenizer.vocab_size
											__snake_case				=		[tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE						) for x in range(end - 7     ,    SCREAMING_SNAKE_CASE						)]

											self.assertListEqual(
											    SCREAMING_SNAKE_CASE     ,    ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]						)
											__snake_case				=		"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE						).input_ids
											self.assertEqual(
											    tokenizer.decode(SCREAMING_SNAKE_CASE     ,    skip_special_tokens=SCREAMING_SNAKE_CASE     ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)     ,    SCREAMING_SNAKE_CASE     ,    )






@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase(  unittest.TestCase   ):
					snake_case_		:				Optional[Any]										=	"""uclanlp/plbart-python-en_XX"""
					snake_case_		:				Optional[Any]										=	[
					    """def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""",
					    """def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""",
					]
					snake_case_		:				int										=	[
					    """Returns the maximum value of a b c.""",
					    """Sums the values of a b c.""",
					]
					snake_case_		:				Tuple										=	[
					    134,
					    5452,
					    3_3460,
					    3_3441,
					    3_3463,
					    3_3465,
					    3_3463,
					    3_3449,
					    988,
					    20,
					    3_3456,
					    19,
					    3_3456,
					    771,
					    39,
					    4258,
					    889,
					    3318,
					    3_3441,
					    3_3463,
					    3_3465,
					    3_3463,
					    3_3449,
					    2471,
					    2,
					    PYTHON_CODE,
					]


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		List[str]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		PLBartTokenizer.from_pretrained(
											    cls.checkpoint_name     ,    language_codes="base"     ,    src_lang="python"     ,    tgt_lang="en_XX"						)
											__snake_case				=		1
											return cls


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Dict:




											'''simple docstring'''

											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]     ,    5_0_0_0_1						)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]     ,    5_0_0_0_2						)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]     ,    5_0_0_0_3						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
											self.assertListEqual(self.expected_src_tokens     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											self.assertIn(SCREAMING_SNAKE_CASE     ,    self.tokenizer.all_special_ids						)
											__snake_case				=		[EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
											__snake_case				=		self.tokenizer.decode(SCREAMING_SNAKE_CASE     ,    skip_special_tokens=SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.tokenizer.decode(generated_ids[1:]     ,    skip_special_tokens=SCREAMING_SNAKE_CASE						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											self.assertNotIn(self.tokenizer.eos_token     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 2_0]
											self.assertIsInstance(src_text[0]     ,    SCREAMING_SNAKE_CASE						)
											__snake_case				=		1_0
											__snake_case				=		self.tokenizer(SCREAMING_SNAKE_CASE     ,    max_length=SCREAMING_SNAKE_CASE     ,    truncation=SCREAMING_SNAKE_CASE						).input_ids[0]
											self.assertEqual(ids[-2]     ,    2						)
											self.assertEqual(ids[-1]     ,    SCREAMING_SNAKE_CASE						)
											self.assertEqual(len(SCREAMING_SNAKE_CASE						)     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]						)     ,    [5_0_0_0_4, 5_0_0_0_1]						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	Any:




											'''simple docstring'''

											__snake_case				=		tempfile.mkdtemp()
											__snake_case				=		self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PLBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						)
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,    SCREAMING_SNAKE_CASE						)


					@require_torch
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.tokenizer(self.src_text     ,    text_target=self.tgt_text     ,    padding=SCREAMING_SNAKE_CASE     ,    return_tensors="pt"						)
											__snake_case				=		shift_tokens_right(batch["labels"]     ,    self.tokenizer.pad_token_id						)

											# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
											self.assertEqual(batch.input_ids[1][-2:].tolist()     ,    [2, PYTHON_CODE]						)
											self.assertEqual(batch.decoder_input_ids[1][0]     ,    SCREAMING_SNAKE_CASE						)
											self.assertEqual(batch.decoder_input_ids[1][-1]     ,    2						)
											self.assertEqual(batch.labels[1][-2:].tolist()     ,    [2, EN_CODE]						)


					@require_torch
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.tokenizer(
											    self.src_text     ,    text_target=self.tgt_text     ,    padding=SCREAMING_SNAKE_CASE     ,    truncation=SCREAMING_SNAKE_CASE     ,    max_length=len(self.expected_src_tokens						)     ,    return_tensors="pt"     ,    )
											__snake_case				=		shift_tokens_right(batch["labels"]     ,    self.tokenizer.pad_token_id						)

											self.assertIsInstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											self.assertEqual((2, 2_6)     ,    batch.input_ids.shape						)
											self.assertEqual((2, 2_6)     ,    batch.attention_mask.shape						)
											__snake_case				=		batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens     ,    SCREAMING_SNAKE_CASE						)
											self.assertEqual(2     ,    batch.decoder_input_ids[0, -1]						)  # EOS
											# Test that special tokens are reset
											self.assertEqual(self.tokenizer.prefix_tokens     ,    []						)
											self.assertEqual(self.tokenizer.suffix_tokens     ,    [self.tokenizer.eos_token_id, PYTHON_CODE]						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.tokenizer(self.src_text     ,    padding=SCREAMING_SNAKE_CASE     ,    truncation=SCREAMING_SNAKE_CASE     ,    max_length=3     ,    return_tensors="pt"						)
											__snake_case				=		self.tokenizer(
											    text_target=self.tgt_text     ,    padding=SCREAMING_SNAKE_CASE     ,    truncation=SCREAMING_SNAKE_CASE     ,    max_length=1_0     ,    return_tensors="pt"						)
											__snake_case				=		targets["input_ids"]
											__snake_case				=		shift_tokens_right(SCREAMING_SNAKE_CASE     ,    self.tokenizer.pad_token_id						)

											self.assertEqual(batch.input_ids.shape[1]     ,    3						)
											self.assertEqual(batch.decoder_input_ids.shape[1]     ,    1_0						)






					@require_torch
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.tokenizer._build_translation_inputs(
											    "A test"     ,    return_tensors="pt"     ,    src_lang="en_XX"     ,    tgt_lang="java"						)

											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE						)     ,    {
											        # A, test, EOS, en_XX
											        "input_ids": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
											        "attention_mask": [[1, 1, 1, 1]],
											        # java
											        "forced_bos_token_id": 5_0_0_0_1,
											    }     ,    )









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

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


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






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





