
import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=False  ,						SCREAMING_SNAKE_CASE_=13  ,						SCREAMING_SNAKE_CASE_=(32, 128)  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=27  ,						SCREAMING_SNAKE_CASE_=38  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=4.0  ,						SCREAMING_SNAKE_CASE_=257  ,						SCREAMING_SNAKE_CASE_=None  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      patch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      max_token_length
													__UpperCamelCase  	=      num_character_labels
													__UpperCamelCase  	=      num_bpe_labels
													__UpperCamelCase  	=      num_wordpiece_labels
													__UpperCamelCase  	=      hidden_size
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      mlp_ratio
													__UpperCamelCase  	=      patch_embeds_hidden_size
													__UpperCamelCase  	=      output_hidden_states



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )
													__UpperCamelCase  	=      self.get_config()
													return config, pixel_values



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													return MgpstrConfig(
													    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						max_token_length=self.max_token_length  ,						num_character_labels=self.num_character_labels  ,						num_bpe_labels=self.num_bpe_labels  ,						num_wordpiece_labels=self.num_wordpiece_labels  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						mlp_ratio=self.mlp_ratio  ,						output_hidden_states=self.output_hidden_states  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													with torch.no_grad():
																			__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(
													    generated_ids[0][0].shape  ,						(self.batch_size, self.max_token_length, self.num_character_labels)      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													__UpperCamelCase	,				__UpperCamelCase  	=      config_and_inputs
													__UpperCamelCase  	=      {'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
							_snake_case  		=	{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
							_snake_case  		=	False

							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrModelTester(self      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_  ,						has_text_modality=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													self.config_tester.run_common_tests()



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





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )



							@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''      )
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			self.assertIsInstance(model.get_input_embeddings()  ,						(nn.Module)      )
																			__UpperCamelCase  	=      model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_  ,						nn.Linear      )      )



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





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      inspect.signature(model.forward      )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase  	=      [*signature.parameters.keys()]

																			__UpperCamelCase  	=      ['''pixel_values''']
																			self.assertListEqual(arg_names[:1]  ,						SCREAMING_SNAKE_CASE_      )



							@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''      )
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			if not model_class.supports_gradient_checkpointing:
																									continue

																			__UpperCamelCase  	=      True
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			self.assertTrue(model.is_gradient_checkpointing      )



							def      A__		(	self      )-> str:





													'''simple docstring'''






													def check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.eval()

																			with torch.no_grad():
																									__UpperCamelCase  	=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )      )

																			__UpperCamelCase  	=      outputs.hidden_states

																			__UpperCamelCase  	=      getattr(
																			    self.model_tester  ,						'''expected_num_hidden_layers'''  ,						self.model_tester.num_hidden_layers + 1      )
																			self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_      )

																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]      )  ,						[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]  ,						)

													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      True
																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			__UpperCamelCase  	=      True

																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase  	=      _config_zero_init(SCREAMING_SNAKE_CASE_      )
													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(config=SCREAMING_SNAKE_CASE_      )
																			for name, param in model.named_parameters():
																									if isinstance(SCREAMING_SNAKE_CASE_  ,						(nn.Linear, nn.Convad, nn.LayerNorm)      ):
																															if param.requires_grad:
																																					self.assertIn(
																																					    ((param.data.mean() * 1E9).round() / 1E9).item()  ,						[0.0, 1.0]  ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"  ,						)



							@unittest.skip(reason='''Retain_grad is tested in individual model tests'''      )
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													pass





def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      '''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
						__UpperCamelCase  	=      Image.open(requests.get(snake_case			,   stream=snake_case			).raw			).convert('''RGB'''			)
						return im






@require_vision
@require_torch
class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							@slow
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      '''alibaba-damo/mgp-str-base'''
													__UpperCamelCase  	=      MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      prepare_img()
													__UpperCamelCase  	=      processor(images=SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values.to(SCREAMING_SNAKE_CASE_      )

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )

													# verify the logits
													self.assertEqual(outputs.logits[0].shape  ,						torch.Size((1, 27, 38)      )      )

													__UpperCamelCase  	=      processor.batch_decode(outputs.logits      )
													__UpperCamelCase  	=      '''ticket'''

													self.assertEqual(out_strs['''generated_text'''][0]  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      torch.tensor(
													    [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]  ,						device=SCREAMING_SNAKE_CASE_  ,						)

													self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )






import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__a							  =						logging.get_logger(__name__)

__a							  =						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

__a							  =						{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

__a							  =						{
    """microsoft/deberta-base""": 5_1_2,
    """microsoft/deberta-large""": 5_1_2,
    """microsoft/deberta-xlarge""": 5_1_2,
    """microsoft/deberta-base-mnli""": 5_1_2,
    """microsoft/deberta-large-mnli""": 5_1_2,
    """microsoft/deberta-xlarge-mnli""": 5_1_2,
}

__a							  =						{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}

def    UpperCamelCase_ (	)     ->Optional[int]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Dict:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask", "token_type_ids"]







	def __init__(      self				:			Union[str, Any]							,			snake_case__				:			Tuple							,			snake_case__				:			Union[str, Any]							,			snake_case__				:			Union[str, Any]="replace"							,			snake_case__				:			Any="[CLS]"							,			snake_case__				:			List[Any]="[SEP]"							,			snake_case__				:			str="[SEP]"							,			snake_case__				:			Tuple="[CLS]"							,			snake_case__				:			Optional[Any]="[UNK]"							,			snake_case__				:			Union[str, Any]="[PAD]"							,			snake_case__				:			List[Any]="[MASK]"							,			snake_case__				:			Any=False							,			snake_case__				:			List[str]=False							,			**snake_case__				:			str							,			):
			"""simple docstring"""





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else sep_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else cls_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else pad_token

			# Mask token behave like a normal word, i.e. include the space before it
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else mask_token

			super().__init__(
			    errors=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			unk_token=snake_case__							,			sep_token=snake_case__							,			cls_token=snake_case__							,			pad_token=snake_case__							,			mask_token=snake_case__							,			add_prefix_space=snake_case__							,			add_bos_token=snake_case__							,			**snake_case__							,			)
			A				=add_bos_token

			with open(snake_case__							,			encoding="utf-8" ) as vocab_handle:
					A				=json.load(snake_case__ )
			A				={v: k for k, v in self.encoder.items()}
			A				=errors  # how to handle errors in decoding
			A				=bytes_to_unicode()
			A				={v: k for k, v in self.byte_encoder.items()}
			with open(snake_case__							,			encoding="utf-8" ) as merges_handle:
					A				=merges_handle.read().split("\n" )[1:-1]
			A				=[tuple(merge.split() ) for merge in bpe_merges]
			A				=dict(zip(snake_case__							,			range(len(snake_case__ ) ) ) )
			A				={}
			A				=add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			A				=re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )







	@property
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			int ):
			"""simple docstring"""





			return dict(self.encoder							,			**self.added_tokens_encoder )







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

			while True:
					A				=min(snake_case__							,			key=lambda snake_case__ : self.bpe_ranks.get(snake_case__							,			float("inf" ) ) )
					if bigram not in self.bpe_ranks:
							break
					A			,    A				=bigram
					A				=[]
					A				=0
					while i < len(snake_case__ ):
							try:
									A				=word.index(snake_case__							,			snake_case__ )
							except ValueError:
									new_word.extend(word[i:] )
									break
							else:
									new_word.extend(word[i:j] )
									A				=j

							if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
							else:
									new_word.append(word[i] )
									i += 1
					A				=tuple(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Any							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None ):
			"""simple docstring"""





			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			A				=[self.cls_token_id]
			A				=[self.sep_token_id]
			return cls + token_ids_a + sep + token_ids_a + sep







	def        _a					(      self				:			List[Any]							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None							,			snake_case__				:			bool = False ):
			"""simple docstring"""





			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=snake_case__							,			token_ids_a=snake_case__							,			already_has_special_tokens=snake_case__ )

			if token_ids_a is None:
					return [1] + ([0] * len(snake_case__ )) + [1]
			return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]







	def        _a					(      self				:			str							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None ):
			"""simple docstring"""





			A				=[self.sep_token_id]
			A				=[self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep ) * [0]
			return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".join(
					    self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__ ).split(" " ) )
			return bpe_tokens







	def        _a					(      self				:			List[Any]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			return self.encoder.get(snake_case__							,			self.encoder.get(self.unk_token ) )







	def        _a					(      self				:			int							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







	def        _a					(      self				:			Union[str, Any]							,			snake_case__				:			Optional[int] ):
			"""simple docstring"""





			A				="".join(snake_case__ )
			A				=bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,			errors=self.errors )
			return text







	def        _a					(      self				:			int							,			snake_case__				:			str							,			snake_case__				:			Optional[str] = None ):
			"""simple docstring"""





			if not os.path.isdir(snake_case__ ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
					return
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as f:
					f.write(json.dumps(self.encoder							,			indent=2							,			sort_keys=snake_case__							,			ensure_ascii=snake_case__ ) + "\n" )

			A				=0
			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as writer:
					writer.write("#version: 0.2\n" )
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,			key=lambda snake_case__ : kv[1] ):
							if index != token_index:
									logger.warning(
									    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    " Please check that the tokenizer is not corrupted!" )
									A				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			List[Any]							,			snake_case__				:			Optional[Any]							,			snake_case__				:			Any=False							,			**snake_case__				:			Optional[int] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if (is_split_into_words or add_prefix_space) and (len(snake_case__ ) > 0 and not text[0].isspace()):
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			List[str]							,			snake_case__				:			"Conversation" ):
			"""simple docstring"""





			A				=[]
			for is_user, text in conversation.iter_texts():
					input_ids.extend(self.encode(snake_case__							,			add_special_tokens=snake_case__ ) + [self.eos_token_id] )
			if len(snake_case__ ) > self.model_max_length:
					A				=input_ids[-self.model_max_length :]
			return input_ids




