import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   _lowerCAmelCase		(     __a      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     LongformerTokenizer
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     LongformerTokenizerFast
							_UpperCAmelCase						    =     True







							def 				snake_case  (				self     :    Any		):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase						:List[Any]						  =     [
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """\u0120""",
										    """\u0120l""",
										    """\u0120n""",
										    """\u0120lo""",
										    """\u0120low""",
										    """er""",
										    """\u0120lowest""",
										    """\u0120newer""",
										    """\u0120wider""",
										    """<unk>""",
										]
										lowerCamelCase						:List[str]						  =     dict(zip(a_	,					range(len(a_		)		)		)		)
										lowerCamelCase						:List[Any]						  =     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
										lowerCamelCase						:Any						  =     {"""unk_token""": """<unk>"""}

										lowerCamelCase						:int						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:Tuple						  =     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 				snake_case  (				self     :    Union[str, Any]	,					**__snake_case     :    List[Any]		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**a_		)







							def 				snake_case  (				self     :    str	,					**__snake_case     :    str		):
										kwargs.update(self.special_tokens_map		)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,					**a_		)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Tuple		):
										lowerCamelCase						:int						  =     """lower newer"""
										lowerCamelCase						:Optional[int]						  =     """lower newer"""
										return input_text, output_text







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Any						  =     self.tokenizer_class(self.vocab_file	,					self.merges_file	,					**self.special_tokens_map		)
										lowerCamelCase						:int						  =     """lower newer"""
										lowerCamelCase						:Union[str, Any]						  =     ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
										lowerCamelCase						:int						  =     tokenizer.tokenize(a_		)  # , add_prefix_space=True)
										self.assertListEqual(a_	,					a_		)

										lowerCamelCase						:Any						  =     tokens + [tokenizer.unk_token]
										lowerCamelCase						:str						  =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		)	,					a_		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:int						  =     self.get_tokenizer()

										self.assertListEqual(tokenizer.encode('''Hello world!'''	,					add_special_tokens=a_		)	,					[0, 31414, 232, 328, 2]		)
										self.assertListEqual(
										    tokenizer.encode('''Hello world! cécé herlolip 418'''	,					add_special_tokens=a_		)	,					[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]	,					)







							@slow
							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Optional[int]						  =     self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''		)

										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode('''sequence builders'''	,					add_special_tokens=a_		)
										lowerCamelCase						:Tuple						  =     tokenizer.encode('''multi-sequence build'''	,					add_special_tokens=a_		)

										lowerCamelCase						:Tuple						  =     tokenizer.encode(
										    '''sequence builders'''	,					add_special_tokens=a_	,					add_prefix_space=a_		)
										lowerCamelCase						:Optional[Any]						  =     tokenizer.encode(
										    '''sequence builders'''	,					'''multi-sequence build'''	,					add_special_tokens=a_	,					add_prefix_space=a_		)

										lowerCamelCase						:List[str]						  =     tokenizer.build_inputs_with_special_tokens(a_		)
										lowerCamelCase						:Union[str, Any]						  =     tokenizer.build_inputs_with_special_tokens(a_	,					a_		)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[Any]						  =     self.get_tokenizer()

										lowerCamelCase						:Dict						  =     """Encode this sequence."""
										lowerCamelCase						:Optional[Any]						  =     tokenizer.byte_encoder[""" """.encode('''utf-8'''		)[0]]

										# Testing encoder arguments
										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(a_	,					add_special_tokens=a_	,					add_prefix_space=a_		)
										lowerCamelCase						:Optional[int]						  =     tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
										self.assertNotEqual(a_	,					a_		)

										lowerCamelCase						:Optional[int]						  =     tokenizer.encode(a_	,					add_special_tokens=a_	,					add_prefix_space=a_		)
										lowerCamelCase						:Optional[int]						  =     tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
										self.assertEqual(a_	,					a_		)

										tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}		)
										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(a_	,					add_special_tokens=a_		)
										lowerCamelCase						:str						  =     tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
										self.assertNotEqual(a_	,					a_		)

										# Testing spaces after special tokens
										lowerCamelCase						:List[str]						  =     """<mask>"""
										tokenizer.add_special_tokens(
										    {'''mask_token''': AddedToken(a_	,					lstrip=a_	,					rstrip=a_		)}		)  # mask token has a left space
										lowerCamelCase						:str						  =     tokenizer.convert_tokens_to_ids(a_		)

										lowerCamelCase						:int						  =     """Encode <mask> sequence"""
										lowerCamelCase						:Tuple						  =     """Encode <mask>sequence"""

										lowerCamelCase						:List[str]						  =     tokenizer.encode(a_		)
										lowerCamelCase						:Optional[int]						  =     encoded.index(a_		)
										lowerCamelCase						:Tuple						  =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
										self.assertEqual(a_	,					a_		)

										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(a_		)
										lowerCamelCase						:Optional[int]						  =     encoded.index(a_		)
										lowerCamelCase						:Any						  =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
										self.assertNotEqual(a_	,					a_		)







							def 				snake_case  (				self     :    Tuple		):
										pass







							def 				snake_case  (				self     :    List[str]		):



										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																lowerCamelCase						:str						  =     self.rust_tokenizer_class.from_pretrained(a_	,					**a_		)
																lowerCamelCase						:Dict						  =     self.tokenizer_class.from_pretrained(a_	,					**a_		)
																lowerCamelCase						:List[str]						  =     """A, <mask> AllenNLP sentence."""
																lowerCamelCase						:Tuple						  =     tokenizer_r.encode_plus(a_	,					add_special_tokens=a_	,					return_token_type_ids=a_		)
																lowerCamelCase						:Optional[int]						  =     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''']		)	,					)

																lowerCamelCase						:int						  =     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																lowerCamelCase						:int						  =     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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
																self.assertSequenceEqual(tokens_r['''input_ids''']	,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

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







							def 				snake_case  (				self     :    List[str]		):



										for trim_offsets, add_prefix_space in itertools.product([True, False]	,					repeat=2		):
													lowerCamelCase						:Any						  =     self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)

													lowerCamelCase						:Optional[Any]						  =     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
													lowerCamelCase						:Any						  =     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

													self.assertEqual(pre_tokenizer_state['''add_prefix_space''']	,					a_		)

													self.assertEqual(post_processor_state['''add_prefix_space''']	,					a_		)
													self.assertEqual(post_processor_state['''trim_offsets''']	,					a_		)







							def 				snake_case  (				self     :    List[str]		):



										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																lowerCamelCase						:Optional[int]						  =     """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																lowerCamelCase						:int						  =     F"{text_of_1_token} {text_of_1_token}"

																lowerCamelCase						:Optional[int]						  =     self.rust_tokenizer_class.from_pretrained(
																    a_	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)
																lowerCamelCase						:Dict						  =     tokenizer_r(a_	,					return_offsets_mapping=a_	,					add_special_tokens=a_		)
																self.assertEqual(encoding.offset_mapping[0]	,					(0, len(a_		))		)
																self.assertEqual(
																    encoding.offset_mapping[1]	,					(len(a_		) + 1, len(a_		) + 1 + len(a_		))	,					)

																lowerCamelCase						:Union[str, Any]						  =     self.rust_tokenizer_class.from_pretrained(
																    a_	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)
																lowerCamelCase						:str						  =     tokenizer_r(a_	,					return_offsets_mapping=a_	,					add_special_tokens=a_		)
																self.assertEqual(encoding.offset_mapping[0]	,					(0, len(a_		))		)
																self.assertEqual(
																    encoding.offset_mapping[1]	,					(len(a_		) + 1, len(a_		) + 1 + len(a_		))	,					)

																lowerCamelCase						:Union[str, Any]						  =     self.rust_tokenizer_class.from_pretrained(
																    a_	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)
																lowerCamelCase						:Dict						  =     tokenizer_r(a_	,					return_offsets_mapping=a_	,					add_special_tokens=a_		)
																self.assertEqual(encoding.offset_mapping[0]	,					(0, len(a_		))		)
																self.assertEqual(
																    encoding.offset_mapping[1]	,					(len(a_		), len(a_		) + 1 + len(a_		))	,					)

																lowerCamelCase						:Tuple						  =     self.rust_tokenizer_class.from_pretrained(
																    a_	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)
																lowerCamelCase						:Union[str, Any]						  =     tokenizer_r(a_	,					return_offsets_mapping=a_	,					add_special_tokens=a_		)
																self.assertEqual(encoding.offset_mapping[0]	,					(0, len(a_		))		)
																self.assertEqual(
																    encoding.offset_mapping[1]	,					(len(a_		), len(a_		) + 1 + len(a_		))	,					)

																lowerCamelCase						:Tuple						  =     F" {text}"

																# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																# )
																# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																# self.assertEqual(
																#     encoding.offset_mapping[1],
																#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																# )

																lowerCamelCase						:Optional[Any]						  =     self.rust_tokenizer_class.from_pretrained(
																    a_	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)
																lowerCamelCase						:Union[str, Any]						  =     tokenizer_r(a_	,					return_offsets_mapping=a_	,					add_special_tokens=a_		)
																self.assertEqual(encoding.offset_mapping[0]	,					(1, 1 + len(a_		))		)
																self.assertEqual(
																    encoding.offset_mapping[1]	,					(1 + len(a_		) + 1, 1 + len(a_		) + 1 + len(a_		))	,					)

																lowerCamelCase						:Tuple						  =     self.rust_tokenizer_class.from_pretrained(
																    a_	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)
																lowerCamelCase						:Any						  =     tokenizer_r(a_	,					return_offsets_mapping=a_	,					add_special_tokens=a_		)
																self.assertEqual(encoding.offset_mapping[0]	,					(0, 1 + len(a_		))		)
																self.assertEqual(
																    encoding.offset_mapping[1]	,					(1 + len(a_		), 1 + len(a_		) + 1 + len(a_		))	,					)

																lowerCamelCase						:List[str]						  =     self.rust_tokenizer_class.from_pretrained(
																    a_	,					use_fast=a_	,					add_prefix_space=a_	,					trim_offsets=a_		)
																lowerCamelCase						:Optional[Any]						  =     tokenizer_r(a_	,					return_offsets_mapping=a_	,					add_special_tokens=a_		)
																self.assertEqual(encoding.offset_mapping[0]	,					(0, 1 + len(a_		))		)
																self.assertEqual(
																    encoding.offset_mapping[1]	,					(1 + len(a_		), 1 + len(a_		) + 1 + len(a_		))	,					)




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
			import torch

			from transformers import AutoModelForCausalLM
@require_torch
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:List[Any]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     -1

										lowerCamelCase						:List[str]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.decode(greedy_ids[0]		)

										with CaptureStdout() as cs:
													lowerCamelCase						:str						  =     TextStreamer(__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case	,					streamer=__snake_case		)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										lowerCamelCase						:Optional[int]						  =     cs.out[:-1]

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:int						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     -1

										lowerCamelCase						:Dict						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[Any]						  =     tokenizer.decode(greedy_ids[0]		)

										lowerCamelCase						:List[str]						  =     TextIteratorStreamer(__snake_case		)
										lowerCamelCase						:List[str]						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()
										lowerCamelCase						:Any						  =     ''''''
										for new_text in streamer:
													streamer_text += new_text

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Dict						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[str]						  =     -1

										lowerCamelCase						:Optional[Any]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[str]						  =     greedy_ids[:, input_ids.shape[1] :]
										lowerCamelCase						:Union[str, Any]						  =     tokenizer.decode(new_greedy_ids[0]		)

										with CaptureStdout() as cs:
													lowerCamelCase						:List[str]						  =     TextStreamer(__snake_case	,					skip_prompt=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case	,					streamer=__snake_case		)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										lowerCamelCase						:int						  =     cs.out[:-1]

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
										# with actual models -- the dummy models' tokenizers are not aligned with their models, and
										# `skip_special_tokens=True` has no effect on them
										lowerCamelCase						:List[Any]						  =     AutoTokenizer.from_pretrained('''distilgpt2'''		)
										lowerCamelCase						:Union[str, Any]						  =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Union[str, Any]						  =     torch.ones((1, 5)	,					device=__snake_case		).long() * model.config.bos_token_id
										with CaptureStdout() as cs:
													lowerCamelCase						:Dict						  =     TextStreamer(__snake_case	,					skip_special_tokens=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=1	,					do_sample=__snake_case	,					streamer=__snake_case		)

										# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
										# re-tokenized, must only contain one token
										lowerCamelCase						:Tuple						  =     cs.out[:-1]  # Remove the final "\n"
										lowerCamelCase						:int						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
										self.assertEqual(streamer_text_tokenized.input_ids.shape	,					(1, 1)		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:List[str]						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Optional[int]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Tuple						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     TextIteratorStreamer(__snake_case	,					timeout=0.0_0_1		)
										lowerCamelCase						:Dict						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()

										# The streamer will timeout after 0.001 seconds, so an exception will be raised
										with self.assertRaises(__snake_case		):
													lowerCamelCase						:Dict						  =     ''''''
													for new_text in streamer:
																streamer_text += new_text



