


"""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    lowerCamelCase__						:
      def __init__(		self	:	Dict      ,       A_	:	List[Any]      ,       A_	:	str=1_4      ,       A_	:	str=7      ,       A_	:	Dict=True      ,       A_	:	Dict=True      ,       A_	:	Tuple=False      ,       A_	:	Optional[Any]=True      ,       A_	:	List[str]=9_9      ,       A_	:	Tuple=3_2      ,       A_	:	List[str]=4      ,       A_	:	Optional[Any]=4      ,       A_	:	List[str]=4      ,       A_	:	List[str]=3_7      ,       A_	:	Any="gelu"      ,       A_	:	Optional[Any]=0.1      ,       A_	:	Optional[Any]=0.1      ,       A_	:	Tuple=5_1_2      ,       A_	:	Union[str, Any]=0.02      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	seq_length
       __lowercase									=	is_training
       __lowercase									=	use_input_mask
       __lowercase									=	use_token_type_ids
       __lowercase									=	use_labels
       __lowercase									=	vocab_size
       __lowercase									=	hidden_size
       __lowercase									=	rotary_dim
       __lowercase									=	num_hidden_layers
       __lowercase									=	num_attention_heads
       __lowercase									=	intermediate_size
       __lowercase									=	hidden_act
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	max_position_embeddings
       __lowercase									=	initializer_range
       __lowercase									=	None
       __lowercase									=	vocab_size - 1
       __lowercase									=	vocab_size - 1
       __lowercase									=	vocab_size - 1
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

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

       __lowercase									=	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=A_      ,       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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.prepare_config_and_inputs()
       __lowercase							,		__lowercase							,		__lowercase									=	config_and_inputs
       __lowercase									=	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Any      ,       A_	:	Dict      ,       A_	:	List[Any]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	2_0
       __lowercase									=	model_class_name(A_							)

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

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

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

       __lowercase									=	model(A_							)

       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	Tuple      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	2_0
       __lowercase									=	model_class_name(A_							)

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

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

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

       __lowercase									=	model(A_      ,       attention_mask=A_							)

       __lowercase									=	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    lowerCamelCase__						(		_a			,   _a			,   unittest.TestCase    ):
      a :     List[Any]											=		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      a :     int											=		(FlaxGPTJForCausalLM,) if is_flax_available() else ()
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	FlaxGPTJModelTester(self							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       for model_class_name in self.all_model_classes:
        __lowercase							,		__lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(A_      ,       A_      ,       A_      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       for model_class_name in self.all_model_classes:
        __lowercase							,		__lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            A_      ,       A_      ,       A_      ,       A_							)
      @tooslow
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	GPTaTokenizer.from_pretrained("""gpt2"""      ,       pad_token="""<|endoftext|>"""      ,       padding_side="""left"""							)
       __lowercase									=	tokenizer(["""Hello this is a long string""", """Hey"""]      ,       return_tensors="""np"""      ,       padding=A_      ,       truncation=A_							)

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

       __lowercase									=	jax.jit(model.generate							)

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

       __lowercase									=	tokenizer.batch_decode(A_      ,       skip_special_tokens=A_							)

       __lowercase									=	[
           """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(A_      ,       A_							)
      @is_pt_flax_cross_test
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	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
         __lowercase									=	self._prepare_for_class(A_      ,       A_							)
         __lowercase									=	{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

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

         __lowercase									=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,       A_							)
         __lowercase									=	fx_state

         with torch.no_grad():
          __lowercase									=	pt_model(**A_							).to_tuple()

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

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

         __lowercase									=	fx_model_loaded(**A_							).to_tuple()
         self.assertEqual(
             len(A_							)      ,       len(A_							)      ,       """Output lengths differ between Flax and PyTorch"""							)
         for fx_output_loaded, pt_output in zip(A_      ,       A_							):
          self.assert_almost_equals(fx_output_loaded[:, -1]      ,       pt_output[:, -1].numpy()      ,       4e-2							)
      @is_pt_flax_cross_test
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	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
         __lowercase									=	self._prepare_for_class(A_      ,       A_							)
         __lowercase									=	{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

         __lowercase									=	pt_model_class(A_							).eval()
         __lowercase									=	model_class(A_      ,       dtype=jnp.floataa							)

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

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

         with torch.no_grad():
          __lowercase									=	pt_model(**A_							).to_tuple()

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

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

         with torch.no_grad():
          __lowercase									=	pt_model_loaded(**A_							).to_tuple()

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






      @tooslow
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       for model_class_name in self.all_model_classes:
        __lowercase									=	model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""							)
        __lowercase									=	model(np.ones((1, 1)							)							)
        self.assertIsNotNone(A_							)




"""simple docstring"""

from typing import List

from .keymap import KEYMAP, get_character


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += [key]
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator


def lowerCAmelCase_      (				*UpperCamelCase__	: List[str] ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += keys
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator





class    lowerCamelCase__						(		_a    ):
      def __new__(		cls	:	str      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	super().__new__(cls      ,       A_      ,       A_      ,       A_							)
       if not hasattr(A_      ,       """key_handler"""							):
        setattr(A_      ,       """key_handler"""      ,       {}							)
       setattr(A_      ,       """handle_input"""      ,       KeyHandler.handle_input							)

       for value in attrs.values():
        __lowercase									=	getattr(A_      ,       """handle_key"""      ,       []							)
        for key in handled_keys:
         __lowercase									=	value
       return new_cls






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Dict							):






       '''simple docstring'''


       __lowercase									=	get_character()
       if char != KEYMAP["undefined"]:
        __lowercase									=	ord(A_							)
       __lowercase									=	cls.key_handler.get(A_							)
       if handler:
        __lowercase									=	char
        return handler(cls							)
       else:
        return None






def lowerCAmelCase_      (				cls	: int ):






 """simple docstring"""







 return KeyHandler(cls.__name__				,   cls.__bases__				,   cls.__dict__.copy() )
