


"""simple docstring"""


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class snake_case      (  _UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			MvpTokenizer
		__UpperCamelCase           =			MvpTokenizerFast
		__UpperCamelCase           =			True
		__UpperCamelCase           =			filter_roberta_detectors







		def  a_ (					self      :       List[Any]		)       ->						List[Any]:





						'''simple docstring'''







						super().setUp()
						_A					  =			[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						_A					  =			dict(zip(a__    ,   range(len(a__		)		)		)		)
						_A					  =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						_A					  =			{"unk_token": "<unk>"}

						_A					  =			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES["vocab_file"]		)
						_A					  =			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(a__		) + "\n"		)
						with open(self.merges_file    ,   "w"    ,   encoding="utf-8"		) as fp:
										fp.write("\n".join(a__		)		)







		def  a_ (					self      :       Optional[Any]    ,   **a__      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						kwargs.update(self.special_tokens_map		)
						return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **a__		)







		def  a_ (					self      :       int    ,   **a__      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







						kwargs.update(self.special_tokens_map		)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,   **a__		)







		def  a_ (					self      :       str    ,   a__      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







						return "lower newer", "lower newer"







		@cached_property
		def  a_ (					self      :       int		)       ->						Optional[int]:





						'''simple docstring'''







						return MvpTokenizer.from_pretrained("RUCAIBox/mvp"		)







		@cached_property
		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"		)







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





						'''simple docstring'''







						_A					  =			["A long paragraph for summarization.", "Another paragraph for summarization."]
						_A					  =			[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_A					  =			tokenizer(a__    ,   max_length=len(a__		)    ,   padding=a__    ,   return_tensors="pt"		)
										self.assertIsInstance(a__    ,   a__		)

										self.assertEqual((2, 9)    ,   batch.input_ids.shape		)
										self.assertEqual((2, 9)    ,   batch.attention_mask.shape		)
										_A					  =			batch.input_ids.tolist()[0]
										self.assertListEqual(a__    ,   a__		)
										# Test that special tokens are reset







		@require_torch
		def  a_ (					self      :       str		)       ->						Dict:





						'''simple docstring'''







						_A					  =			["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_A					  =			tokenizer(a__    ,   padding=a__    ,   return_tensors="pt"		)
										# check if input_ids are returned and no labels
										self.assertIn("input_ids"    ,   a__		)
										self.assertIn("attention_mask"    ,   a__		)
										self.assertNotIn("labels"    ,   a__		)
										self.assertNotIn("decoder_attention_mask"    ,   a__		)







		@require_torch
		def  a_ (					self      :       Optional[Any]		)       ->						int:





						'''simple docstring'''







						_A					  =			[
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_A					  =			tokenizer(text_target=a__    ,   max_length=32    ,   padding="max_length"    ,   return_tensors="pt"		)
										self.assertEqual(32    ,   targets["input_ids"].shape[1]		)







		@require_torch
		def  a_ (					self      :       List[str]		)       ->						Optional[int]:





						'''simple docstring'''







						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_A					  =			tokenizer(
										    ["I am a small frog" * 10_24, "I am a small frog"]    ,   padding=a__    ,   truncation=a__    ,   return_tensors="pt"		)
										self.assertIsInstance(a__    ,   a__		)
										self.assertEqual(batch.input_ids.shape    ,   (2, 10_24)		)







		@require_torch
		def  a_ (					self      :       List[str]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			["A long paragraph for summarization."]
						_A					  =			[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_A					  =			tokenizer(a__    ,   text_target=a__    ,   return_tensors="pt"		)
										_A					  =			inputs["input_ids"]
										_A					  =			inputs["labels"]
										self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
										self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
										self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
										self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)







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





						'''simple docstring'''







						pass







		def  a_ (					self      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
														_A					  =			self.rust_tokenizer_class.from_pretrained(a__    ,   **a__		)
														_A					  =			self.tokenizer_class.from_pretrained(a__    ,   **a__		)
														_A					  =			"A, <mask> AllenNLP sentence."
														_A					  =			tokenizer_r.encode_plus(a__    ,   add_special_tokens=a__    ,   return_token_type_ids=a__		)
														_A					  =			tokenizer_p.encode_plus(a__    ,   add_special_tokens=a__    ,   return_token_type_ids=a__		)

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r["token_type_ids"]		)    ,   sum(tokens_p["token_type_ids"]		)		)

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r["attention_mask"]		) / len(tokens_r["attention_mask"]		)    ,   sum(tokens_p["attention_mask"]		) / len(tokens_p["attention_mask"]		)    ,   )

														_A					  =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
														_A					  =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]		)

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p["input_ids"]    ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)
														self.assertSequenceEqual(tokens_r["input_ids"]    ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)

														self.assertSequenceEqual(
														    a__    ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)
														self.assertSequenceEqual(
														    a__    ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)



"""simple docstring"""


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_       							=    logging.get_logger(__name__)

a_       							=    {
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'blip_text_model'







		def __init__(					self      :       int    ,   a__      :       List[str]=3_05_24    ,   a__      :       List[str]=7_68    ,   a__      :       List[Any]=7_68    ,   a__      :       int=30_72    ,   a__      :       List[str]=7_68    ,   a__      :       Dict=12    ,   a__      :       Optional[int]=8    ,   a__      :       Optional[Any]=5_12    ,   a__      :       List[Any]="gelu"    ,   a__      :       Optional[Any]=1E-1_2    ,   a__      :       Any=0.0    ,   a__      :       int=0.0    ,   a__      :       Dict=0.0_2    ,   a__      :       Optional[Any]=3_05_22    ,   a__      :       Any=2    ,   a__      :       int=0    ,   a__      :       Union[str, Any]=1_02    ,   a__      :       Tuple=True    ,   a__      :       Optional[int]=True    ,   **a__      :       Any    ,   )       ->						List[Any]:





						'''simple docstring'''







						super().__init__(
						    pad_token_id=a__    ,   bos_token_id=a__    ,   eos_token_id=a__    ,   sep_token_id=a__    ,   **a__    ,   )

						_A					  =			vocab_size
						_A					  =			hidden_size
						_A					  =			encoder_hidden_size
						_A					  =			intermediate_size
						_A					  =			projection_dim
						_A					  =			hidden_dropout_prob
						_A					  =			num_hidden_layers
						_A					  =			num_attention_heads
						_A					  =			max_position_embeddings
						_A					  =			layer_norm_eps
						_A					  =			hidden_act
						_A					  =			initializer_range
						_A					  =			attention_probs_dropout_prob
						_A					  =			is_decoder
						_A					  =			use_cache







		@classmethod
		def  a_ (					cls      :       Optional[Any]    ,   a__      :       Union[str, os.PathLike]    ,   **a__      :       Optional[Any]		)       ->						"PretrainedConfig":





						'''simple docstring'''







						cls._set_token_in_kwargs(a__		)

						_A  ,		_A					  =			cls.get_config_dict(a__    ,   **a__		)

						# get the text config dict if we are loading from BlipConfig
						if config_dict.get("model_type"		) == "blip":
										_A					  =			config_dict["text_config"]

						if "model_type" in config_dict and hasattr(cls    ,   "model_type"		) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
										    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

						return cls.from_dict(a__    ,   **a__		)





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'blip_vision_model'







		def __init__(					self      :       Optional[Any]    ,   a__      :       Any=7_68    ,   a__      :       List[str]=30_72    ,   a__      :       str=5_12    ,   a__      :       Any=12    ,   a__      :       int=12    ,   a__      :       int=3_84    ,   a__      :       Tuple=16    ,   a__      :       str="gelu"    ,   a__      :       Tuple=1E-5    ,   a__      :       List[str]=0.0    ,   a__      :       List[Any]=1E-1_0    ,   **a__      :       int    ,   )       ->						List[str]:





						'''simple docstring'''







						super().__init__(**a__		)

						_A					  =			hidden_size
						_A					  =			intermediate_size
						_A					  =			projection_dim
						_A					  =			num_hidden_layers
						_A					  =			num_attention_heads
						_A					  =			patch_size
						_A					  =			image_size
						_A					  =			initializer_range
						_A					  =			attention_dropout
						_A					  =			layer_norm_eps
						_A					  =			hidden_act







		@classmethod
		def  a_ (					cls      :       Any    ,   a__      :       Union[str, os.PathLike]    ,   **a__      :       int		)       ->						"PretrainedConfig":





						'''simple docstring'''







						cls._set_token_in_kwargs(a__		)

						_A  ,		_A					  =			cls.get_config_dict(a__    ,   **a__		)

						# get the vision config dict if we are loading from BlipConfig
						if config_dict.get("model_type"		) == "blip":
										_A					  =			config_dict["vision_config"]

						if "model_type" in config_dict and hasattr(cls    ,   "model_type"		) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
										    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

						return cls.from_dict(a__    ,   **a__		)





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'blip'
		__UpperCamelCase           =			True







		def __init__(					self      :       List[Any]    ,   a__      :       Optional[int]=None    ,   a__      :       str=None    ,   a__      :       List[str]=5_12    ,   a__      :       Any=2.6_5_9_2    ,   a__      :       str=2_56    ,   **a__      :       Optional[int]    ,   )       ->						Dict:





						'''simple docstring'''







						super().__init__(**a__		)

						if text_config is None:
										_A					  =			{}
										logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."		)

						if vision_config is None:
										_A					  =			{}
										logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."		)

						_A					  =			BlipTextConfig(**a__		)
						_A					  =			BlipVisionConfig(**a__		)

						_A					  =			self.vision_config.hidden_size

						_A					  =			projection_dim
						_A					  =			logit_scale_init_value
						_A					  =			1.0
						_A					  =			0.0_2
						_A					  =			image_text_hidden_size







		@classmethod
		def  a_ (					cls      :       Tuple    ,   a__      :       BlipTextConfig    ,   a__      :       BlipVisionConfig    ,   **a__      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						return cls(text_config=text_config.to_dict()    ,   vision_config=vision_config.to_dict()    ,   **a__		)







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





						'''simple docstring'''







						_A					  =			copy.deepcopy(self.__dict__		)
						_A					  =			self.text_config.to_dict()
						_A					  =			self.vision_config.to_dict()
						_A					  =			self.__class__.model_type
						return output