



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


_lowerCAmelCase	:   Any    =   get_tests_dir("fixtures/test_sentencepiece.model")


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

_lowerCAmelCase	:   Dict    =   5_0_0_0_3
_lowerCAmelCase	:   Union[str, Any]    =   5_0_0_0_2


@require_sentencepiece
@require_tokenizers
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	PLBartTokenizer
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	False
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase__		  =  PLBartTokenizer(a_    ,language_codes='base'    ,keep_accents=a_						)
						tokenizer.save_pretrained(self.tmpdirname						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  PLBartTokenizer(a_    ,language_codes='base'    ,keep_accents=a_						)

						lowerCAmelCase__		  =  tokenizer.tokenize('This is a test'						)
						self.assertListEqual(a_    ,['▁This', '▁is', '▁a', '▁t', 'est']						)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(a_						)    ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,)

						lowerCAmelCase__		  =  tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
						self.assertListEqual(
						    a_    ,[
						        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',
						        '.',
						    ]    ,)
						lowerCAmelCase__		  =  tokenizer.convert_tokens_to_ids(a_						)
						self.assertListEqual(
						    a_    ,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ]    ,)

						lowerCAmelCase__		  =  tokenizer.convert_ids_to_tokens(a_						)
						self.assertListEqual(
						    a_    ,[
						        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>',
						        '.',
						    ]    ,)

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

						self.assertListEqual(a_    ,['__java__', '__python__', '__en_XX__', '<mask>']						)

						lowerCAmelCase__		  =  'java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
						lowerCAmelCase__		  =  tokenizer(a_						).input_ids
						self.assertEqual(
						    tokenizer.decode(a_    ,skip_special_tokens=a_    ,clean_up_tokenization_spaces=a_						)    ,a_    ,)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  PLBartTokenizer(a_    ,language_codes='multi'    ,keep_accents=a_						)

						lowerCAmelCase__		  =  tokenizer.tokenize('This is a test'						)
						self.assertListEqual(a_    ,['▁This', '▁is', '▁a', '▁t', 'est']						)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(a_						)    ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,)

						lowerCAmelCase__		  =  tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
						self.assertListEqual(
						    a_    ,[
						        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',
						        '.',
						    ]    ,)
						lowerCAmelCase__		  =  tokenizer.convert_tokens_to_ids(a_						)
						self.assertListEqual(
						    a_    ,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ]    ,)

						lowerCAmelCase__		  =  tokenizer.convert_ids_to_tokens(a_						)
						self.assertListEqual(
						    a_    ,[
						        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>',
						        '.',
						    ]    ,)
						lowerCAmelCase__		  =  tokenizer.vocab_size
						lowerCAmelCase__		  =  [tokenizer.convert_ids_to_tokens(a_						) for x in range(end - 7    ,a_						)]

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



@require_torch
@require_sentencepiece
@require_tokenizers
class 	__snake_case   ( unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	'uclanlp/plbart-python-en_XX'
		SCREAMING_SNAKE_CASE__								=	[
		    'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
		    'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
		]
		SCREAMING_SNAKE_CASE__								=	[
		    'Returns the maximum value of a b c.',
		    'Sums the values of a b c.',
		]
		SCREAMING_SNAKE_CASE__								=	[
		    134,
		    5452,
		    33460,
		    33441,
		    33463,
		    33465,
		    33463,
		    33449,
		    988,
		    20,
		    33456,
		    19,
		    33456,
		    771,
		    39,
		    4258,
		    889,
		    3318,
		    33441,
		    33463,
		    33465,
		    33463,
		    33449,
		    2471,
		    2,
		    PYTHON_CODE,
		]
		@classmethod
		def 						SCREAMING_SNAKE_CASE_					(							cls						):







						"""simple docstring"""
						lowerCAmelCase__		  =  PLBartTokenizer.from_pretrained(
						    cls.checkpoint_name    ,language_codes='base'    ,src_lang='python'    ,tgt_lang='en_XX'						)
						lowerCAmelCase__		  =  1
						return cls
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__']    ,5_0001						)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__']    ,5_0002						)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__']    ,5_0003						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
						self.assertListEqual(self.expected_src_tokens    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.assertIn(a_    ,self.tokenizer.all_special_ids						)
						lowerCAmelCase__		  =  [EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
						lowerCAmelCase__		  =  self.tokenizer.decode(a_    ,skip_special_tokens=a_						)
						lowerCAmelCase__		  =  self.tokenizer.decode(generated_ids[1:]    ,skip_special_tokens=a_						)
						self.assertEqual(a_    ,a_						)
						self.assertNotIn(self.tokenizer.eos_token    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  ['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 20]
						self.assertIsInstance(src_text[0]    ,a_						)
						lowerCAmelCase__		  =  10
						lowerCAmelCase__		  =  self.tokenizer(a_    ,max_length=a_    ,truncation=a_						).input_ids[0]
						self.assertEqual(ids[-2]    ,2						)
						self.assertEqual(ids[-1]    ,a_						)
						self.assertEqual(len(a_						)    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__']						)    ,[5_0004, 5_0001]						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  tempfile.mkdtemp()
						lowerCAmelCase__		  =  self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(a_						)
						lowerCAmelCase__		  =  PLBartTokenizer.from_pretrained(a_						)
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,a_						)
		@require_torch
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.tokenizer(self.src_text    ,text_target=self.tgt_text    ,padding=a_    ,return_tensors='pt'						)
						lowerCAmelCase__		  =  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]    ,a_						)
						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						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.tokenizer(
						    self.src_text    ,text_target=self.tgt_text    ,padding=a_    ,truncation=a_    ,max_length=len(self.expected_src_tokens						)    ,return_tensors='pt'    ,)
						lowerCAmelCase__		  =  shift_tokens_right(batch['labels']    ,self.tokenizer.pad_token_id						)

						self.assertIsInstance(a_    ,a_						)

						self.assertEqual((2, 26)    ,batch.input_ids.shape						)
						self.assertEqual((2, 26)    ,batch.attention_mask.shape						)
						lowerCAmelCase__		  =  batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens    ,a_						)
						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						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.tokenizer(self.src_text    ,padding=a_    ,truncation=a_    ,max_length=3    ,return_tensors='pt'						)
						lowerCAmelCase__		  =  self.tokenizer(
						    text_target=self.tgt_text    ,padding=a_    ,truncation=a_    ,max_length=10    ,return_tensors='pt'						)
						lowerCAmelCase__		  =  targets['input_ids']
						lowerCAmelCase__		  =  shift_tokens_right(a_    ,self.tokenizer.pad_token_id						)

						self.assertEqual(batch.input_ids.shape[1]    ,3						)
						self.assertEqual(batch.decoder_input_ids.shape[1]    ,10						)



		@require_torch
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.tokenizer._build_translation_inputs(
						    'A test'    ,return_tensors='pt'    ,src_lang='en_XX'    ,tgt_lang='java'						)

						self.assertEqual(
						    nested_simplify(a_						)    ,{
						        # A, test, EOS, en_XX
						        'input_ids': [[150, 242, 2, 5_0003]],
						        'attention_mask': [[1, 1, 1, 1]],
						        # java
						        'forced_bos_token_id': 5_0001,
						    }    ,)





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  attention_head_dim
						lowerCAmelCase__		  =  num_attention_heads * attention_head_dim

						lowerCAmelCase__		  =  in_channels

						lowerCAmelCase__		  =  torch.nn.GroupNorm(num_groups=a_    ,num_channels=a_    ,eps=1e-6    ,affine=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)

						# 3. Define transformers blocks
						lowerCAmelCase__		  =  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            a_    ,a_    ,a_    ,dropout=a_    ,cross_attention_dim=a_    ,activation_fn=a_    ,attention_bias=a_    ,double_self_attention=a_    ,norm_elementwise_affine=a_    ,)
						        for d in range(a_						)
						    ]						)

						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  hidden_states.shape
						lowerCAmelCase__		  =  batch_frames // num_frames

						lowerCAmelCase__		  =  hidden_states

						lowerCAmelCase__		  =  hidden_states[None, :].reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,2    ,1    ,3    ,4						)

						lowerCAmelCase__		  =  self.norm(a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,3    ,4    ,2    ,1						).reshape(batch_size * height * width    ,a_    ,a_						)

						lowerCAmelCase__		  =  self.proj_in(a_						)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__		  =  block(
										    a_    ,encoder_hidden_states=a_    ,timestep=a_    ,cross_attention_kwargs=a_    ,class_labels=a_    ,)

						# 3. Output
						lowerCAmelCase__		  =  self.proj_out(a_						)
						lowerCAmelCase__		  =  (
						    hidden_states[None, None, :]
						    .reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						    .permute(0    ,3    ,4    ,1    ,2						)
						    .contiguous()
						)
						lowerCAmelCase__		  =  hidden_states.reshape(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=a_						)
