

'''simple docstring'''

import itertools
import string
from collections.abc import Generator, Iterable




def 							_UpperCamelCase				(   lowerCAmelCase__:					Iterable[str]		,lowerCAmelCase__:					int   )      -> Generator[tuple[str, ...], None, None]:
							SCREAMING_SNAKE_CASE_				=				iter(lowerCAmelCase__   )
							while True:
														SCREAMING_SNAKE_CASE_				=				tuple(itertools.islice(lowerCAmelCase__		,lowerCAmelCase__   )   )
														if not chunk:
																					return
														yield chunk




def 							_UpperCamelCase				(   lowerCAmelCase__:					str   )      -> str:
							SCREAMING_SNAKE_CASE_				=				''.join([c.upper() for c in dirty if c in string.ascii_letters]   )
							SCREAMING_SNAKE_CASE_				=				''

							if len(lowerCAmelCase__   ) < 2:
														return dirty

							for i in range(len(lowerCAmelCase__   ) - 1   ):
														clean += dirty[i]

														if dirty[i] == dirty[i + 1]:
																					clean += "X"

							clean += dirty[-1]

							if len(lowerCAmelCase__   ) & 1:
														clean += "X"

							return clean




def 							_UpperCamelCase				(   lowerCAmelCase__:					str   )      -> list[str]:
							# I and J are used interchangeably to allow
							# us to use a 5x5 table (25 letters)
							SCREAMING_SNAKE_CASE_				=				'ABCDEFGHIKLMNOPQRSTUVWXYZ'
							# we're using a list instead of a '2d' array because it makes the math
							# for setting up the table and doing the actual encoding/decoding simpler
							SCREAMING_SNAKE_CASE_				=				[]

							# copy key chars into the table if they are in `alphabet` ignoring duplicates
							for char in key.upper():
														if char not in table and char in alphabet:
																					table.append(lowerCAmelCase__   )

    # fill the rest of the table in with the remaining alphabet chars
							for char in alphabet:
														if char not in table:
																					table.append(lowerCAmelCase__   )

							return table




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					str   )      -> str:
							SCREAMING_SNAKE_CASE_				=				generate_table(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				prepare_input(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(lowerCAmelCase__		,2   ):
														SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				divmod(table.index(lowerCAmelCase__   )		,5   )
														SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				divmod(table.index(lowerCAmelCase__   )		,5   )

														if rowa == rowa:
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
														elif cola == cola:
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
														else:  # rectangle
																					ciphertext += table[rowa * 5 + cola]
																					ciphertext += table[rowa * 5 + cola]

							return ciphertext




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					str   )      -> str:
							SCREAMING_SNAKE_CASE_				=				generate_table(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(lowerCAmelCase__		,2   ):
														SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				divmod(table.index(lowerCAmelCase__   )		,5   )
														SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				divmod(table.index(lowerCAmelCase__   )		,5   )

														if rowa == rowa:
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
														elif cola == cola:
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
														else:  # rectangle
																					plaintext += table[rowa * 5 + cola]
																					plaintext += table[rowa * 5 + cola]

							return plaintext








'''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

							SCREAMING_SNAKE_CASE							:				List[Any]     	=			"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					int		,lowerCAmelCase__:					Tuple=None   )      -> Union[str, Any]:
							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(lowerCAmelCase__   ):
														values.append(rng.randint(0		,vocab_size - 1   )   )

							SCREAMING_SNAKE_CASE_				=				np.array(lowerCAmelCase__		,dtype=jnp.intaa   ).reshape(lowerCAmelCase__   )

							return output




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					str=None   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				ids_tensor(lowerCAmelCase__		,vocab_size=2		,rng=lowerCAmelCase__   )
							# make sure that at least one token is attended to for each batch
							SCREAMING_SNAKE_CASE_				=				1
							return attn_mask






@require_flax
class    snake_case     :






				"""simple docstring"""
				_a    	=     None
				_a    	=     ()



				def   a__					(    self       )     ->							List[str]:
											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(_lowercase       )
											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   a__					(    self       )     ->							Dict:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				flax_model.generate(_lowercase       ).sequences
																		SCREAMING_SNAKE_CASE_				=				pt_model.generate(torch.tensor(_lowercase,     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   a__					(    self       )     ->							Any:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							int:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Any:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Optional[Any]:
											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(_lowercase       )

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



				def   a__					(    self       )     ->							Tuple:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							List[str]:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Dict:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Optional[int]:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

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



				def   a__					(    self       )     ->							List[Any]:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

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





				def   a__					(    self       )     ->							int:
											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(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

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






@require_flax
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Optional[int]:
											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(_lowercase,     return_tensors='np'       ).input_ids

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

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





