






"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_					=      get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase_       (__A				,						unittest.TestCase     ):
	__magic_name__	      =				XGLMTokenizer
	__magic_name__	      =				XGLMTokenizerFast
	__magic_name__	      =				True
	__magic_name__	      =				True





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Dict:
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCAmelCase_     : Tuple			     =					XGLMTokenizer(lowerCAmelCase_	,	keep_accents=lowerCAmelCase_						)
				tokenizer.save_pretrained(self.tmpdirname						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Any:
				UpperCAmelCase_     : List[str]			     =					"<pad>"
				UpperCAmelCase_     : Dict			     =					1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_						)	,	lowerCAmelCase_						)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_						)	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					list(self.get_tokenizer().get_vocab().keys()						)

				self.assertEqual(vocab_keys[0]	,	"<s>"						)
				self.assertEqual(vocab_keys[1]	,	"<pad>"						)
				self.assertEqual(len(lowerCAmelCase_						)	,	1_008						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Any:
				self.assertEqual(self.get_tokenizer().vocab_size	,	1_008						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Optional[Any]:
				UpperCAmelCase_     : Any			     =					XGLMTokenizer(lowerCAmelCase_	,	keep_accents=lowerCAmelCase_						)

				UpperCAmelCase_     : str			     =					tokenizer.tokenize("This is a test"						)
				self.assertListEqual(lowerCAmelCase_	,	["▁This", "▁is", "▁a", "▁t", "est"]						)

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

				UpperCAmelCase_     : List[Any]			     =					tokenizer.tokenize("I was born in 92000, and this is falsé."						)
				self.assertListEqual(
				    lowerCAmelCase_	,	[
				        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",
				        ".",
				    ]	,	)
				UpperCAmelCase_     : List[str]			     =					tokenizer.convert_tokens_to_ids(lowerCAmelCase_						)
				self.assertListEqual(
				    lowerCAmelCase_	,	[
				        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]
				    ]	,	)

				UpperCAmelCase_     : Tuple			     =					tokenizer.convert_ids_to_tokens(lowerCAmelCase_						)
				self.assertListEqual(
				    lowerCAmelCase_	,	[
				        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>",
				        ".",
				    ]	,	)





	@cached_property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Dict:
				return XGLMTokenizer.from_pretrained("facebook/xglm-564M"						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			List[str]:
				with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(lowerCAmelCase_	,	f.name						)
							UpperCAmelCase_     : Union[str, Any]			     =					XGLMTokenizer(f.name	,	keep_accents=lowerCAmelCase_						)
							UpperCAmelCase_     : List[str]			     =					pickle.dumps(lowerCAmelCase_						)
				pickle.loads(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Union[str, Any]:
				if not self.test_rust_tokenizer:
							return

				UpperCAmelCase_     : Optional[int]			     =					self.get_tokenizer()
				UpperCAmelCase_     : str			     =					self.get_rust_tokenizer()

				UpperCAmelCase_     : Tuple			     =					"I was born in 92000, and this is falsé."

				UpperCAmelCase_     : Dict			     =					tokenizer.tokenize(lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					rust_tokenizer.tokenize(lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Optional[Any]			     =					tokenizer.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					rust_tokenizer.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Optional[int]			     =					self.get_rust_tokenizer()
				UpperCAmelCase_     : Dict			     =					tokenizer.encode(lowerCAmelCase_						)
				UpperCAmelCase_     : List[Any]			     =					rust_tokenizer.encode(lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Dict:
				UpperCAmelCase_     : Any			     =					"Hello World!"
				UpperCAmelCase_     : Optional[Any]			     =					[2, 31_227, 4_447, 35]

				self.assertListEqual(lowerCAmelCase_	,	self.big_tokenizer.encode(lowerCAmelCase_						)						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Dict:
				UpperCAmelCase_     : Optional[Any]			     =					(
				    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
				    " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
				)
				# fmt: off
				UpperCAmelCase_     : Optional[int]			     =					[2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
				# fmt: on

				self.assertListEqual(lowerCAmelCase_	,	self.big_tokenizer.encode(lowerCAmelCase_						)						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			int:
				# fmt: off
				UpperCAmelCase_     : Dict			     =					{
				    "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
				    "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
				}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=lowerCAmelCase_	,	model_name="facebook/xglm-564M"	,	padding=lowerCAmelCase_	,	)











"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

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


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







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Dict=768						)						->			List[Any]:
				super().__init__(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					proj_size

				UpperCAmelCase_     : Optional[Any]			     =					CLIPVisionModel(lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					PaintByExampleMapper(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					nn.LayerNorm(config.hidden_size						)
				UpperCAmelCase_     : List[Any]			     =					nn.Linear(config.hidden_size	,	self.proj_size						)

				# uncondition for scaling
				UpperCAmelCase_     : Optional[int]			     =					nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict=False						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					self.model(pixel_values=lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					clip_output.pooler_output
				UpperCAmelCase_     : List[Any]			     =					self.mapper(latent_states[:, None]						)
				UpperCAmelCase_     : List[str]			     =					self.final_layer_norm(lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					self.proj_out(lowerCAmelCase_						)
				if return_uncond_vector:
							return latent_states, self.uncond_vector

				return latent_states







class 			UpperCamelCase_       (nn.Module     ):





	def __init__(      self					: Dict	,	lowerCAmelCase_					: Union[str, Any]						)						->			Tuple:
				super().__init__()
				UpperCAmelCase_     : List[Any]			     =					(config.num_hidden_layers + 1) // 5
				UpperCAmelCase_     : Optional[Any]			     =					config.hidden_size
				UpperCAmelCase_     : List[str]			     =					1
				UpperCAmelCase_     : Union[str, Any]			     =					nn.ModuleList(
				    [
				        BasicTransformerBlock(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	activation_fn="gelu"	,	attention_bias=lowerCAmelCase_						)
				        for _ in range(lowerCAmelCase_						)
				    ]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[str]						)						->			str:
				for block in self.blocks:
							UpperCAmelCase_     : int			     =					block(lowerCAmelCase_						)

				return hidden_states



