
'''simple docstring'''







import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
						import os

						import jax.numpy as jnp
						from jax import jit

						from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
						from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

						_snake_case				:   Optional[int]     =				"""0.12"""  # assumed parallelism: 8


if is_torch_available():
						import torch



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=None  ):
						if rng is None:
												_SCREAMING_SNAKE_CASE				= random.Random()

						_SCREAMING_SNAKE_CASE				= 1
						for dim in shape:
												total_dims *= dim

						_SCREAMING_SNAKE_CASE				= []
						for _ in range(_SCREAMING_SNAKE_CASE  ):
												values.append(rng.randint(0	,			vocab_size - 1  )  )

						_SCREAMING_SNAKE_CASE				= np.array(_SCREAMING_SNAKE_CASE	,			dtype=jnp.intaa  ).reshape(_SCREAMING_SNAKE_CASE  )

						return output



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Any=None  ):
						_SCREAMING_SNAKE_CASE				= ids_tensor(_SCREAMING_SNAKE_CASE	,			vocab_size=2	,			rng=_SCREAMING_SNAKE_CASE  )
						# make sure that at least one token is attended to for each batch
						_SCREAMING_SNAKE_CASE				= 1
						return attn_mask


@require_flax
class 			lowerCAmelCase :
		a							:					Union[str, Any]       =					None
		a							:					Optional[Any]       =					()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								_SCREAMING_SNAKE_CASE				= 2
								_SCREAMING_SNAKE_CASE				= inputs["input_ids"].shape[-1] // 2
								_SCREAMING_SNAKE_CASE				= inputs["input_ids"][:max_batch_size, :sequence_length]

								_SCREAMING_SNAKE_CASE				= jnp.ones_like(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								_SCREAMING_SNAKE_CASE				= input_ids.shape[-1] + 5
								if config.eos_token_id is not None and config.pad_token_id is None:
														# hack to allow generate for models such as GPT2 as is done in `generate()`
														_SCREAMING_SNAKE_CASE				= config.eos_token_id
								return config, input_ids, attention_mask, max_length




		@is_pt_flax_cross_test
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= max_length
								_SCREAMING_SNAKE_CASE				= 0

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model_class.__name__[4:]  # Skip the "Flax" at the beginning
														_SCREAMING_SNAKE_CASE				= getattr(UpperCamelCase					,						UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= pt_model_class(UpperCamelCase      ).eval()
														_SCREAMING_SNAKE_CASE				= load_flax_weights_in_pytorch_model(UpperCamelCase					,						flax_model.params      )

														_SCREAMING_SNAKE_CASE				= flax_model.generate(UpperCamelCase      ).sequences
														_SCREAMING_SNAKE_CASE				= pt_model.generate(torch.tensor(UpperCamelCase					,						dtype=torch.long      )      )

														if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																				_SCREAMING_SNAKE_CASE				= flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

														self.assertListEqual(pt_generation_outputs.numpy().tolist()					,						flax_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= max_length

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= max_length

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= max_length
								_SCREAMING_SNAKE_CASE				= 2

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= max_length
								_SCREAMING_SNAKE_CASE				= 2
								_SCREAMING_SNAKE_CASE				= 2

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[0]					,						input_ids.shape[0] * config.num_return_sequences      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= max_length
								_SCREAMING_SNAKE_CASE				= 0.8
								_SCREAMING_SNAKE_CASE				= 10
								_SCREAMING_SNAKE_CASE				= 0.3
								_SCREAMING_SNAKE_CASE				= 1
								_SCREAMING_SNAKE_CASE				= 8
								_SCREAMING_SNAKE_CASE				= 9

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= max_length
								_SCREAMING_SNAKE_CASE				= 1
								_SCREAMING_SNAKE_CASE				= 8
								_SCREAMING_SNAKE_CASE				= 9

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()
								_SCREAMING_SNAKE_CASE				= max_length
								_SCREAMING_SNAKE_CASE				= 2
								_SCREAMING_SNAKE_CASE				= 1
								_SCREAMING_SNAKE_CASE				= 8
								_SCREAMING_SNAKE_CASE				= 9

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()

								# pad attention mask on the left
								_SCREAMING_SNAKE_CASE				= attention_mask.at[(0, 0)].set(0      )

								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= max_length

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase					,						attention_mask=UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase					,						attention_mask=UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()

								# pad attention mask on the left
								_SCREAMING_SNAKE_CASE				= attention_mask.at[(0, 0)].set(0      )

								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= max_length

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase					,						attention_mask=UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase					,						attention_mask=UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._get_input_ids_and_config()

								# pad attention mask on the left
								_SCREAMING_SNAKE_CASE				= attention_mask.at[(0, 0)].set(0      )

								_SCREAMING_SNAKE_CASE				= 2
								_SCREAMING_SNAKE_CASE				= max_length

								for model_class in self.all_generative_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= model.generate(UpperCamelCase					,						attention_mask=UpperCamelCase      ).sequences
														self.assertEqual(generation_outputs.shape[-1]					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= jit(model.generate      )
														_SCREAMING_SNAKE_CASE				= jit_generate(UpperCamelCase					,						attention_mask=UpperCamelCase      ).sequences

														self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()      )


@require_flax
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"      )
								_SCREAMING_SNAKE_CASE				= FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"      )

								_SCREAMING_SNAKE_CASE				= "Hello world"
								_SCREAMING_SNAKE_CASE				= tokenizer(UpperCamelCase					,						return_tensors="np"      ).input_ids

								# typos are quickly detected (the correct argument is `do_sample`)
								with self.assertRaisesRegex(UpperCamelCase					,						"do_samples"      ):
														model.generate(UpperCamelCase					,						do_samples=UpperCamelCase      )

								# arbitrary arguments that will not be used anywhere are also not accepted
								with self.assertRaisesRegex(UpperCamelCase					,						"foo"      ):
														_SCREAMING_SNAKE_CASE				= {"foo": "bar"}
														model.generate(UpperCamelCase					,						**UpperCamelCase      )

'''simple docstring'''







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					int       =					LayoutLMTokenizer
		a							:					Optional[int]       =					LayoutLMTokenizerFast
		a							:					Optional[int]       =					True
		a							:					Any       =					True




		def     lowercase       (				self      ):
								super().setUp()

								_SCREAMING_SNAKE_CASE				= [
								    "[UNK]",
								    "[CLS]",
								    "[SEP]",
								    "want",
								    "##want",
								    "##ed",
								    "wa",
								    "un",
								    "runn",
								    "##ing",
								    ",",
								    "low",
								    "lowest",
								]
								_SCREAMING_SNAKE_CASE				= os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file					,						"w"					,						encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )




		def     lowercase       (				self					,						**UpperCamelCase      ):
								return LayoutLMTokenizer.from_pretrained(self.tmpdirname					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= "UNwant\u00E9d,running"
								_SCREAMING_SNAKE_CASE				= "unwanted, running"
								return input_text, output_text




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class(self.vocab_file      )

								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize("UNwant\u00E9d,running"      )
								self.assertListEqual(UpperCamelCase					,						["un", "##want", "##ed", ",", "runn", "##ing"]      )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase      )					,						[7, 4, 5, 10, 8, 9]      )




		def     lowercase       (				self      ):
								pass