



import logging

from transformers.configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE_:Union[str, Any]					=						logging.getLogger(__name__)



class SCREAMING_SNAKE_CASE__					(		A_   ):




    '''simple docstring'''






    __lowerCamelCase      :			int =				"masked_bert"






    def __init__(	self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=0,		lowerCamelCase__="topK",		lowerCamelCase__="constant",		lowerCamelCase__=0.0,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=snake_case__,		**snake_case__		)

        A			:	Union[str, Any]										=       vocab_size
        A			:	Optional[int]										=       hidden_size
        A			:	Optional[int]										=       num_hidden_layers
        A			:	str										=       num_attention_heads
        A			:	List[str]										=       hidden_act
        A			:	Any										=       intermediate_size
        A			:	Tuple										=       hidden_dropout_prob
        A			:	Tuple										=       attention_probs_dropout_prob
        A			:	int										=       max_position_embeddings
        A			:	Tuple										=       type_vocab_size
        A			:	int										=       initializer_range
        A			:	Optional[Any]										=       layer_norm_eps
        A			:	Dict										=       pruning_method
        A			:	List[str]										=       mask_init
        A			:	Union[str, Any]										=       mask_scale







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 SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=14,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=4,		lowerCamelCase__=4,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=0.02,		):
        A			:	List[str]										=       parent
        A			:	Any										=       batch_size
        A			:	Dict										=       seq_length
        A			:	Tuple										=       is_training
        A			:	Any										=       use_input_mask
        A			:	Any										=       use_token_type_ids
        A			:	Any										=       use_labels
        A			:	Optional[int]										=       vocab_size
        A			:	Dict										=       hidden_size
        A			:	Dict										=       rotary_dim
        A			:	Dict										=       num_hidden_layers
        A			:	Tuple										=       num_attention_heads
        A			:	Tuple										=       intermediate_size
        A			:	Union[str, Any]										=       hidden_act
        A			:	Dict										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	Optional[int]										=       max_position_embeddings
        A			:	str										=       initializer_range
        A			:	Any										=       None
        A			:	Any										=       vocab_size - 1
        A			:	int										=       vocab_size - 1
        A			:	int										=       vocab_size - 1






    def       _lowerCAmelCase (	self		):
        A			:	str										=       ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A			:	Optional[int]										=       None
        if self.use_input_mask:
            A			:	Any										=       random_attention_mask([self.batch_size, self.seq_length]		)

        A			:	int										=       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=lowerCamelCase__,		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       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.prepare_config_and_inputs()
        A					,							A					,							A			:	List[str]										=       config_and_inputs
        A			:	Optional[int]										=       {"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Optional[int]										=       20
        A			:	Tuple										=       model_class_name(lowerCamelCase__		)

        A			:	Dict										=       model.init_cache(input_ids.shape[0],		lowerCamelCase__		)
        A			:	int										=       jnp.ones((input_ids.shape[0], max_decoder_length),		dtype="""i4"""		)

        A			:	Optional[int]										=       jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :],		(input_ids.shape[0], input_ids.shape[-1] - 1)		)
        A			:	List[Any]										=       model(
            input_ids[:, :-1],		attention_mask=lowerCamelCase__,		past_key_values=lowerCamelCase__,		position_ids=lowerCamelCase__,		)

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

        A			:	Any										=       model(lowerCamelCase__		)

        A			:	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}'''		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Any										=       20
        A			:	Any										=       model_class_name(lowerCamelCase__		)

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

        A			:	str										=       model.init_cache(input_ids.shape[0],		lowerCamelCase__		)
        A			:	Any										=       jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :],		(input_ids.shape[0], input_ids.shape[-1] - 1)		)

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

        A			:	Union[str, Any]										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)

        A			:	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}'''		)






@require_flax
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    __lowerCamelCase      :			Optional[int] =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       FlaxGPTJModelTester(self		)






    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A					,							A					,							A			:	Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A					,							A					,							A			:	str										=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






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

        A			:	Dict										=       FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		)
        A			:	str										=       False
        A			:	Optional[Any]										=       model.config.eos_token_id

        A			:	Union[str, Any]										=       jax.jit(model.generate		)

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

        A			:	Optional[Any]										=       tokenizer.batch_decode(lowerCamelCase__,		skip_special_tokens=lowerCamelCase__		)

        A			:	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(lowerCamelCase__,		lowerCamelCase__		)






    @is_pt_flax_cross_test
    def       _lowerCAmelCase (	self		):
        A					,							A			:	Optional[Any]										=       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
                A			:	Any										=       self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A			:	Dict										=       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

                A			:	int										=       convert_pytorch_state_dict_to_flax(pt_model.state_dict(),		lowerCamelCase__		)
                A			:	Dict										=       fx_state

                with torch.no_grad():
                    A			:	Optional[int]										=       pt_model(**lowerCamelCase__		).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                    pt_model.save_pretrained(lowerCamelCase__		)
                    A			:	Union[str, Any]										=       model_class.from_pretrained(lowerCamelCase__,		from_pt=lowerCamelCase__		)

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






    @is_pt_flax_cross_test
    def       _lowerCAmelCase (	self		):
        A					,							A			:	Dict										=       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
                A			:	int										=       self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A			:	List[str]										=       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

                A			:	int										=       pt_model_class(lowerCamelCase__		).eval()
                A			:	int										=       model_class(lowerCamelCase__,		dtype=jnp.floataa		)

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

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

                with torch.no_grad():
                    A			:	List[str]										=       pt_model(**lowerCamelCase__		).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                    fx_model.save_pretrained(lowerCamelCase__		)
                    A			:	str										=       pt_model_class.from_pretrained(lowerCamelCase__,		from_flax=lowerCamelCase__		)

                with torch.no_grad():
                    A			:	str										=       pt_model_loaded(**lowerCamelCase__		).to_tuple()

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






    @tooslow
    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A			:	Union[str, Any]										=       model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""		)
            A			:	List[str]										=       model(np.ones((1, 1)		)		)
            self.assertIsNotNone(lowerCamelCase__		)


