




'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
   import torch

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=14   ,_SCREAMING_SNAKE_CASE=7   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=99   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=4   ,_SCREAMING_SNAKE_CASE=4   ,_SCREAMING_SNAKE_CASE=4   ,_SCREAMING_SNAKE_CASE=37   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=0.0_2   ,)				->     Any:
          _snake_case				= parent
          _snake_case				= batch_size
          _snake_case				= seq_length
          _snake_case				= is_training
          _snake_case				= use_input_mask
          _snake_case				= use_token_type_ids
          _snake_case				= use_labels
          _snake_case				= vocab_size
          _snake_case				= hidden_size
          _snake_case				= rotary_dim
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= intermediate_size
          _snake_case				= hidden_act
          _snake_case				= hidden_dropout_prob
          _snake_case				= attention_probs_dropout_prob
          _snake_case				= max_position_embeddings
          _snake_case				= initializer_range
          _snake_case				= None
          _snake_case				= vocab_size - 1
          _snake_case				= vocab_size - 1
          _snake_case				= vocab_size - 1






     def 							_lowercase      (					self       )				->     Dict:
          _snake_case				= ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size       )

          _snake_case				= None
          if self.use_input_mask:
               _snake_case				= random_attention_mask([self.batch_size, self.seq_length]       )

          _snake_case				= GPTJConfig(
              vocab_size=self.vocab_size   ,n_embd=self.hidden_size   ,n_layer=self.num_hidden_layers   ,n_head=self.num_attention_heads   ,n_positions=self.max_position_embeddings   ,use_cache=_SCREAMING_SNAKE_CASE   ,bos_token_id=self.bos_token_id   ,eos_token_id=self.eos_token_id   ,pad_token_id=self.pad_token_id   ,rotary_dim=self.rotary_dim   ,)

          return (config, input_ids, input_mask)






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.prepare_config_and_inputs()
          _snake_case				,   _snake_case				,   _snake_case				= config_and_inputs
          _snake_case				= {"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          _snake_case				= 20
          _snake_case				= model_class_name(_SCREAMING_SNAKE_CASE       )

          _snake_case				= model.init_cache(input_ids.shape[0]   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= jnp.ones((input_ids.shape[0], max_decoder_length)   ,dtype="i4"       )

          _snake_case				= jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1       )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)       )
          _snake_case				= model(
              input_ids[:, :-1]   ,attention_mask=_SCREAMING_SNAKE_CASE   ,past_key_values=_SCREAMING_SNAKE_CASE   ,position_ids=_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype="i4"       )
          _snake_case				= model(
              input_ids[:, -1:]   ,attention_mask=_SCREAMING_SNAKE_CASE   ,past_key_values=outputs_cache.past_key_values   ,position_ids=_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= model(_SCREAMING_SNAKE_CASE       )

          _snake_case				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
          self.parent.assertTrue(diff < 1e-3   ,msg=f"""Max diff is {diff}"""       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= 20
          _snake_case				= model_class_name(_SCREAMING_SNAKE_CASE       )

          _snake_case				= jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]   ,axis=-1   ,)

          _snake_case				= model.init_cache(input_ids.shape[0]   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1       )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)       )

          _snake_case				= model(
              input_ids[:, :-1]   ,attention_mask=_SCREAMING_SNAKE_CASE   ,past_key_values=_SCREAMING_SNAKE_CASE   ,position_ids=_SCREAMING_SNAKE_CASE   ,)
          _snake_case				= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype="i4"       )
          _snake_case				= model(
              input_ids[:, -1:]   ,past_key_values=outputs_cache.past_key_values   ,attention_mask=_SCREAMING_SNAKE_CASE   ,position_ids=_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= model(_SCREAMING_SNAKE_CASE   ,attention_mask=_SCREAMING_SNAKE_CASE       )

          _snake_case				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
          self.parent.assertTrue(diff < 1e-3   ,msg=f"""Max diff is {diff}"""       )







@require_flax
class   _a							(		__lowerCAmelCase   ,   __lowerCAmelCase   ,   unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       Tuple       							=		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE_				:       Any       							=		(FlaxGPTJForCausalLM,) if is_flax_available() else ()






     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				= FlaxGPTJModelTester(self       )






     def 							_lowercase      (					self       )				->     Tuple:
          for model_class_name in self.all_model_classes:
               _snake_case				,   _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Any:
          for model_class_name in self.all_model_classes:
               _snake_case				,   _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     @tooslow
     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= GPTaTokenizer.from_pretrained("gpt2"   ,pad_token="<|endoftext|>"   ,padding_side="left"       )
          _snake_case				= tokenizer(["Hello this is a long string", "Hey"]   ,return_tensors="np"   ,padding=_SCREAMING_SNAKE_CASE   ,truncation=_SCREAMING_SNAKE_CASE       )

          _snake_case				= FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"       )
          _snake_case				= False
          _snake_case				= model.config.eos_token_id

          _snake_case				= jax.jit(model.generate       )

          _snake_case				= jit_generate(
              inputs["input_ids"]   ,attention_mask=inputs["attention_mask"]   ,pad_token_id=tokenizer.pad_token_id       ).sequences

          _snake_case				= tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,skip_special_tokens=_SCREAMING_SNAKE_CASE       )

          _snake_case				= [
              "Hello this is a long string of text.\n\nI'm trying to get the text of the",
              "Hey, I'm a little late to the party. I'm going to",
          ]

          self.assertListEqual(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     @is_pt_flax_cross_test
     def 							_lowercase      (					self       )				->     Dict:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__       ):
                    # prepare inputs
                    _snake_case				= self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                    _snake_case				= {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _snake_case				= model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

                    _snake_case				,   _snake_case				= pt_inputs["input_ids"].shape
                    _snake_case				= np.random.randint(0   ,seq_length - 1   ,size=(batch_size,)       )
                    for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE       ):
                         _snake_case				= 0
                         _snake_case				= 1
                         _snake_case				= 0
                         _snake_case				= 1
                    _snake_case				= pt_model_class(_SCREAMING_SNAKE_CASE       ).eval()
                    _snake_case				= model_class(_SCREAMING_SNAKE_CASE   ,dtype=jnp.floataa       )

                    _snake_case				= convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,_SCREAMING_SNAKE_CASE       )
                    _snake_case				= fx_state

                    with torch.no_grad():
                         _snake_case				= pt_model(**_SCREAMING_SNAKE_CASE       ).to_tuple()

                    _snake_case				= fx_model(**_SCREAMING_SNAKE_CASE       ).to_tuple()
                    self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(_SCREAMING_SNAKE_CASE       )   ,"Output lengths differ between Flax and PyTorch"       )
                    for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         self.assert_almost_equals(fx_output[:, -1]   ,pt_output[:, -1].numpy()   ,4e-2       )

                    with tempfile.TemporaryDirectory() as tmpdirname:
                         pt_model.save_pretrained(_SCREAMING_SNAKE_CASE       )
                         _snake_case				= model_class.from_pretrained(_SCREAMING_SNAKE_CASE   ,from_pt=_SCREAMING_SNAKE_CASE       )

                    _snake_case				= fx_model_loaded(**_SCREAMING_SNAKE_CASE       ).to_tuple()
                    self.assertEqual(
                        len(_SCREAMING_SNAKE_CASE       )   ,len(_SCREAMING_SNAKE_CASE       )   ,"Output lengths differ between Flax and PyTorch"       )
                    for fx_output_loaded, pt_output in zip(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         self.assert_almost_equals(fx_output_loaded[:, -1]   ,pt_output[:, -1].numpy()   ,4e-2       )






     @is_pt_flax_cross_test
     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__       ):
                    # prepare inputs
                    _snake_case				= self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                    _snake_case				= {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _snake_case				= model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

                    _snake_case				= pt_model_class(_SCREAMING_SNAKE_CASE       ).eval()
                    _snake_case				= model_class(_SCREAMING_SNAKE_CASE   ,dtype=jnp.floataa       )

                    _snake_case				= load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE   ,fx_model.params       )
                    _snake_case				,   _snake_case				= pt_inputs["input_ids"].shape
                    _snake_case				= np.random.randint(0   ,seq_length - 1   ,size=(batch_size,)       )
                    for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE       ):
                         _snake_case				= 0
                         _snake_case				= 1
                         _snake_case				= 0
                         _snake_case				= 1

                    # make sure weights are tied in PyTorch
                    pt_model.tie_weights()

                    with torch.no_grad():
                         _snake_case				= pt_model(**_SCREAMING_SNAKE_CASE       ).to_tuple()

                    _snake_case				= fx_model(**_SCREAMING_SNAKE_CASE       ).to_tuple()
                    self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(_SCREAMING_SNAKE_CASE       )   ,"Output lengths differ between Flax and PyTorch"       )
                    for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         self.assert_almost_equals(fx_output[:, -1]   ,pt_output[:, -1].numpy()   ,4e-2       )

                    with tempfile.TemporaryDirectory() as tmpdirname:
                         fx_model.save_pretrained(_SCREAMING_SNAKE_CASE       )
                         _snake_case				= pt_model_class.from_pretrained(_SCREAMING_SNAKE_CASE   ,from_flax=_SCREAMING_SNAKE_CASE       )

                    with torch.no_grad():
                         _snake_case				= pt_model_loaded(**_SCREAMING_SNAKE_CASE       ).to_tuple()

                    self.assertEqual(
                        len(_SCREAMING_SNAKE_CASE       )   ,len(_SCREAMING_SNAKE_CASE       )   ,"Output lengths differ between Flax and PyTorch"       )
                    for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         self.assert_almost_equals(fx_output[:, -1]   ,pt_output[:, -1].numpy()   ,4e-2       )






     @tooslow
     def 							_lowercase      (					self       )				->     List[Any]:
          for model_class_name in self.all_model_classes:
               _snake_case				= model_class_name.from_pretrained("EleutherAI/gpt-j-6B"       )
               _snake_case				= model(np.ones((1, 1)       )       )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )








'''simple docstring'''





from __future__ import annotations

import collections
import pprint
from pathlib import Path





def       __a      (  _UpperCamelCase:       str       )		->					str:

     """simple docstring"""




     return "".join(sorted(_UpperCamelCase       )       )





def       __a      (  _UpperCamelCase:       str       )		->					list[str]:

     """simple docstring"""




     return word_by_signature[signature(_UpperCamelCase       )]


UpperCamelCase_		:     str          =		Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
UpperCamelCase_		:     Dict          =		sorted({word.strip().lower() for word in data.splitlines()})

UpperCamelCase_		:     Optional[Any]          =		collections.defaultdict(list)
for word in word_list:
   word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
   UpperCamelCase_		:     List[str]          =		{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

   with open('''anagrams.txt''', '''w''') as file:
      file.write('''all_anagrams = \n ''')
      file.write(pprint.pformat(all_anagrams))


