
"""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 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__  ,     snake_case__=14  ,     snake_case__=7  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=False  ,     snake_case__=True  ,     snake_case__=99  ,     snake_case__=32  ,     snake_case__=4  ,     snake_case__=4  ,     snake_case__=4  ,     snake_case__=37  ,     snake_case__="gelu"  ,     snake_case__=0.1  ,     snake_case__=0.1  ,     snake_case__=512  ,     snake_case__=0.02  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = parent
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = batch_size
       _SCREAMING_SNAKE_CASE   :				Tuple							    = seq_length
       _SCREAMING_SNAKE_CASE   :				Dict							    = is_training
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = use_input_mask
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = use_token_type_ids
       _SCREAMING_SNAKE_CASE   :				Any							    = use_labels
       _SCREAMING_SNAKE_CASE   :				List[str]							    = vocab_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = hidden_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = rotary_dim
       _SCREAMING_SNAKE_CASE   :				List[str]							    = num_hidden_layers
       _SCREAMING_SNAKE_CASE   :				Any							    = num_attention_heads
       _SCREAMING_SNAKE_CASE   :				int							    = intermediate_size
       _SCREAMING_SNAKE_CASE   :				int							    = hidden_act
       _SCREAMING_SNAKE_CASE   :				str							    = hidden_dropout_prob
       _SCREAMING_SNAKE_CASE   :				List[str]							    = attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = max_position_embeddings
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = initializer_range
       _SCREAMING_SNAKE_CASE   :				Tuple							    = None
       _SCREAMING_SNAKE_CASE   :				str							    = vocab_size - 1
       _SCREAMING_SNAKE_CASE   :				Dict							    = vocab_size - 1
       _SCREAMING_SNAKE_CASE   :				Dict							    = vocab_size - 1
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

       _SCREAMING_SNAKE_CASE   :				str							    = None
       if self.use_input_mask:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = random_attention_mask([self.batch_size, self.seq_length]				)

       _SCREAMING_SNAKE_CASE   :				str							    = 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=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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.prepare_config_and_inputs()
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = config_and_inputs
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = 20
       _SCREAMING_SNAKE_CASE   :				List[str]							    = model_class_name(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model.init_cache(input_ids.shape[0]  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = jnp.ones((input_ids.shape[0], max_decoder_length)  ,     dtype="i4"				)

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

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

       _SCREAMING_SNAKE_CASE   :				Any							    = model(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = 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  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = 20
       _SCREAMING_SNAKE_CASE   :				Any							    = model_class_name(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]  ,     axis=-1  ,     )

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

       _SCREAMING_SNAKE_CASE   :				List[str]							    = model(
           input_ids[:, :-1]  ,     attention_mask=snake_case__  ,     past_key_values=snake_case__  ,     position_ids=snake_case__  ,     )
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,     dtype="i4"				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = model(
           input_ids[:, -1:]  ,     past_key_values=outputs_cache.past_key_values  ,     attention_mask=snake_case__  ,     position_ids=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Tuple							    = model(snake_case__  ,     attention_mask=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     A__      =			(FlaxGPTJForCausalLM,) if is_flax_available() else ()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = FlaxGPTJModelTester(self				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE   :				Dict							    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
     @tooslow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				str							    = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = False
       _SCREAMING_SNAKE_CASE   :				List[str]							    = model.config.eos_token_id

       _SCREAMING_SNAKE_CASE   :				Any							    = jax.jit(model.generate				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = jit_generate(
           inputs["input_ids"]  ,     attention_mask=inputs["attention_mask"]  ,     pad_token_id=tokenizer.pad_token_id				).sequences

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = tokenizer.batch_decode(snake_case__  ,     skip_special_tokens=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = [
           "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(snake_case__  ,     snake_case__				)
     @is_pt_flax_cross_test
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self._prepare_for_class(snake_case__  ,     snake_case__				)
           _SCREAMING_SNAKE_CASE   :				List[str]							    = {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

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

           _SCREAMING_SNAKE_CASE   :				Tuple							    = convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,     snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = fx_state

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

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

           with tempfile.TemporaryDirectory() as tmpdirname:
             pt_model.save_pretrained(snake_case__				)
             _SCREAMING_SNAKE_CASE   :				List[Any]							    = model_class.from_pretrained(snake_case__  ,     from_pt=snake_case__				)

           _SCREAMING_SNAKE_CASE   :				Tuple							    = fx_model_loaded(**snake_case__				).to_tuple()
           self.assertEqual(
               len(snake_case__				)  ,     len(snake_case__				)  ,     "Output lengths differ between Flax and PyTorch"				)
           for fx_output_loaded, pt_output in zip(snake_case__  ,     snake_case__				):
             self.assert_almost_equals(fx_output_loaded[:, -1]  ,     pt_output[:, -1].numpy()  ,     4E-2				)
     @is_pt_flax_cross_test
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = 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
           _SCREAMING_SNAKE_CASE   :				Any							    = self._prepare_for_class(snake_case__  ,     snake_case__				)
           _SCREAMING_SNAKE_CASE   :				str							    = {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = pt_model_class(snake_case__				).eval()
           _SCREAMING_SNAKE_CASE   :				List[str]							    = model_class(snake_case__  ,     dtype=jnp.floataa				)

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

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

           with torch.no_grad():
             _SCREAMING_SNAKE_CASE   :				Optional[int]							    = pt_model(**snake_case__				).to_tuple()

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

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

           with torch.no_grad():
             _SCREAMING_SNAKE_CASE   :				Optional[int]							    = pt_model_loaded(**snake_case__				).to_tuple()

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




     @tooslow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model_class_name.from_pretrained("EleutherAI/gpt-j-6B"				)
         _SCREAMING_SNAKE_CASE   :				List[str]							    = model(np.ones((1, 1)				)				)
         self.assertIsNotNone(snake_case__				)





"""simple docstring"""
from typing import List

from .keymap import KEYMAP, get_character





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Optional[int]:

  def decorator(lowerCamelCase__							:							int				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += [key]
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator





def    _lowerCAmelCase    (     *lowerCamelCase__							:							List[str]				)  ->    Tuple:

  def decorator(lowerCamelCase__							:							Dict				):
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += keys
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __new__(     cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = super().__new__(cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       if not hasattr(snake_case__  ,     "key_handler"				):
         setattr(snake_case__  ,     "key_handler"  ,     {}				)
       setattr(snake_case__  ,     "handle_input"  ,     KeyHandler.handle_input				)

       for value in attrs.values():
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = getattr(snake_case__  ,     "handle_key"  ,     []				)
         for key in handled_keys:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = value
       return new_cls




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = get_character()
       if char != KEYMAP["undefined"]:
         _SCREAMING_SNAKE_CASE   :				Dict							    = ord(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = cls.key_handler.get(snake_case__				)
       if handler:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = char
         return handler(cls				)
       else:
         return None





def    _lowerCAmelCase    (     cls							:							List[Any]				)  ->    str:
  return KeyHandler(cls.__name__,						cls.__bases__,						cls.__dict__.copy()				)



