






"""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

			lowerCamelCase_     :   int       			=   '''0.12'''  # assumed parallelism: 8


if is_torch_available():
			import torch

def        snake_case   (     A__			,A__			,A__=None		):
			if rng is None:
						UpperCAmelCase_  :				Any      =      random.Random()

			UpperCAmelCase_  :				Dict      =      1
			for dim in shape:
						total_dims *= dim

			UpperCAmelCase_  :				Union[str, Any]      =      []
			for _ in range(_lowerCAmelCase		):
						values.append(rng.randint(0			,vocab_size - 1		)		)

			UpperCAmelCase_  :				Optional[Any]      =      np.array(_lowerCAmelCase			,dtype=jnp.intaa		).reshape(_lowerCAmelCase		)

			return output

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







@require_flax
class     UpperCamelCase_ :
	__magic_name__			   =       None
	__magic_name__			   =       ()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[int]:
				UpperCAmelCase_  ,			UpperCAmelCase_  :				Optional[Any]      =      self.model_tester.prepare_config_and_inputs_for_common()

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

				UpperCAmelCase_  :				List[Any]      =      jnp.ones_like(_lowerCamelCase		)
				UpperCAmelCase_  :				Dict      =      attention_mask[:max_batch_size, :sequence_length]

				# generate max 5 tokens
				UpperCAmelCase_  :				Optional[int]      =      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()`
							UpperCAmelCase_  :				List[Any]      =      config.eos_token_id
				return config, input_ids, attention_mask, max_length





	@is_pt_flax_cross_test
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    int:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Any      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				List[Any]      =      False
				UpperCAmelCase_  :				Union[str, Any]      =      max_length
				UpperCAmelCase_  :				Optional[int]      =      0

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				Optional[Any]      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				int      =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
							UpperCAmelCase_  :				Dict      =      getattr(_lowerCamelCase			,			_lowerCamelCase		)
							UpperCAmelCase_  :				Union[str, Any]      =      pt_model_class(_lowerCamelCase		).eval()
							UpperCAmelCase_  :				int      =      load_flax_weights_in_pytorch_model(_lowerCamelCase			,			flax_model.params		)

							UpperCAmelCase_  :				Optional[Any]      =      flax_model.generate(_lowerCamelCase		).sequences
							UpperCAmelCase_  :				int      =      pt_model.generate(torch.tensor(_lowerCamelCase			,			dtype=torch.long		)		)

							if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
										UpperCAmelCase_  :				List[str]      =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[str]:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				str      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				Tuple      =      False
				UpperCAmelCase_  :				Optional[int]      =      max_length

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				Union[str, Any]      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				int      =      model.generate(_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				str      =      jit(model.generate		)
							UpperCAmelCase_  :				Any      =      jit_generate(_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[Any]:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				str      =      True
				UpperCAmelCase_  :				Dict      =      max_length

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				Tuple      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				List[Any]      =      model.generate(_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				Union[str, Any]      =      jit(model.generate		)
							UpperCAmelCase_  :				str      =      jit_generate(_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				str      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				Optional[int]      =      False
				UpperCAmelCase_  :				Tuple      =      max_length
				UpperCAmelCase_  :				Dict      =      2

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				int      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				List[Any]      =      model.generate(_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				str      =      jit(model.generate		)
							UpperCAmelCase_  :				str      =      jit_generate(_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    int:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				str      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				int      =      False
				UpperCAmelCase_  :				Optional[int]      =      max_length
				UpperCAmelCase_  :				Optional[Any]      =      2
				UpperCAmelCase_  :				Optional[int]      =      2

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				Dict      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				List[Any]      =      model.generate(_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[0]			,			input_ids.shape[0] * config.num_return_sequences		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    int:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Tuple      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				List[str]      =      True
				UpperCAmelCase_  :				Union[str, Any]      =      max_length
				UpperCAmelCase_  :				str      =      0.8
				UpperCAmelCase_  :				Optional[int]      =      10
				UpperCAmelCase_  :				Tuple      =      0.3
				UpperCAmelCase_  :				int      =      1
				UpperCAmelCase_  :				Optional[Any]      =      8
				UpperCAmelCase_  :				Any      =      9

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				List[Any]      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				Union[str, Any]      =      model.generate(_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				List[str]      =      jit(model.generate		)
							UpperCAmelCase_  :				Optional[Any]      =      jit_generate(_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[Any]:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				int      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				Optional[Any]      =      max_length
				UpperCAmelCase_  :				Any      =      1
				UpperCAmelCase_  :				Optional[Any]      =      8
				UpperCAmelCase_  :				Union[str, Any]      =      9

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				List[Any]      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				Optional[Any]      =      model.generate(_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				Optional[int]      =      jit(model.generate		)
							UpperCAmelCase_  :				List[Any]      =      jit_generate(_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[int]:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Union[str, Any]      =      self._get_input_ids_and_config()
				UpperCAmelCase_  :				Any      =      max_length
				UpperCAmelCase_  :				Any      =      2
				UpperCAmelCase_  :				List[str]      =      1
				UpperCAmelCase_  :				str      =      8
				UpperCAmelCase_  :				Any      =      9

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				List[str]      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				Optional[Any]      =      model.generate(_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				Optional[int]      =      jit(model.generate		)
							UpperCAmelCase_  :				int      =      jit_generate(_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Dict:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Tuple      =      self._get_input_ids_and_config()

				# pad attention mask on the left
				UpperCAmelCase_  :				Union[str, Any]      =      attention_mask.at[(0, 0)].set(0		)

				UpperCAmelCase_  :				List[str]      =      False
				UpperCAmelCase_  :				List[Any]      =      max_length

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				Tuple      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				Any      =      model.generate(_lowerCamelCase			,			attention_mask=_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				List[str]      =      jit(model.generate		)
							UpperCAmelCase_  :				Tuple      =      jit_generate(_lowerCamelCase			,			attention_mask=_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Union[str, Any]:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Tuple      =      self._get_input_ids_and_config()

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

				UpperCAmelCase_  :				List[str]      =      True
				UpperCAmelCase_  :				Tuple      =      max_length

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				Union[str, Any]      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				str      =      model.generate(_lowerCamelCase			,			attention_mask=_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				Union[str, Any]      =      jit(model.generate		)
							UpperCAmelCase_  :				Tuple      =      jit_generate(_lowerCamelCase			,			attention_mask=_lowerCamelCase		).sequences

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Union[str, Any]:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Tuple      =      self._get_input_ids_and_config()

				# pad attention mask on the left
				UpperCAmelCase_  :				List[Any]      =      attention_mask.at[(0, 0)].set(0		)

				UpperCAmelCase_  :				List[str]      =      2
				UpperCAmelCase_  :				int      =      max_length

				for model_class in self.all_generative_model_classes:
							UpperCAmelCase_  :				List[str]      =      model_class(_lowerCamelCase		)

							UpperCAmelCase_  :				Optional[int]      =      model.generate(_lowerCamelCase			,			attention_mask=_lowerCamelCase		).sequences
							self.assertEqual(generation_outputs.shape[-1]			,			_lowerCamelCase		)

							UpperCAmelCase_  :				Dict      =      jit(model.generate		)
							UpperCAmelCase_  :				Optional[Any]      =      jit_generate(_lowerCamelCase			,			attention_mask=_lowerCamelCase		).sequences

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







@require_flax
class     UpperCamelCase_ (unittest.TestCase  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				UpperCAmelCase_  :				str      =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"		)
				UpperCAmelCase_  :				int      =      FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"		)

				UpperCAmelCase_  :				Tuple      =      "Hello world"
				UpperCAmelCase_  :				Dict      =      tokenizer(_lowerCamelCase			,			return_tensors="np"		).input_ids

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

				# arbitrary arguments that will not be used anywhere are also not accepted
				with self.assertRaisesRegex(_lowerCamelCase			,			"foo"		):
							UpperCAmelCase_  :				List[Any]      =      {"foo": "bar"}
							model.generate(_lowerCamelCase			,			**_lowerCamelCase		)











"""simple docstring"""
lowerCamelCase_       			=   {
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowerCamelCase_       			=   {value: key for key, value in encode_dict.items()}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      ""
			for letter in word.lower():
						if letter.isalpha() or letter == " ":
									encoded += encode_dict[letter]
						else:
									raise Exception("encode() accepts only letters of the alphabet and spaces"		)
			return encoded

def        snake_case   (     A__		):
			if set(A__		) - {"A", "B", " "} != set():
						raise Exception("decode() accepts only 'A', 'B' and spaces"		)
			UpperCAmelCase_  :				Dict      =      ""
			for word in coded.split():
						while len(A__		) != 0:
									decoded += decode_dict[word[:5]]
									UpperCAmelCase_  :				str      =      word[5:]
						decoded += " "
			return decoded.strip()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



