



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      :







     '''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    ,	):
       UpperCAmelCase_						=				parent
       UpperCAmelCase_						=				batch_size
       UpperCAmelCase_						=				seq_length
       UpperCAmelCase_						=				is_training
       UpperCAmelCase_						=				use_input_mask
       UpperCAmelCase_						=				use_token_type_ids
       UpperCAmelCase_						=				use_labels
       UpperCAmelCase_						=				vocab_size
       UpperCAmelCase_						=				hidden_size
       UpperCAmelCase_						=				rotary_dim
       UpperCAmelCase_						=				num_hidden_layers
       UpperCAmelCase_						=				num_attention_heads
       UpperCAmelCase_						=				intermediate_size
       UpperCAmelCase_						=				hidden_act
       UpperCAmelCase_						=				hidden_dropout_prob
       UpperCAmelCase_						=				attention_probs_dropout_prob
       UpperCAmelCase_						=				max_position_embeddings
       UpperCAmelCase_						=				initializer_range
       UpperCAmelCase_						=				None
       UpperCAmelCase_						=				vocab_size - 1
       UpperCAmelCase_						=				vocab_size - 1
       UpperCAmelCase_						=				vocab_size - 1



     def    A__		(			self     ):
       UpperCAmelCase_						=				ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size     )

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

       UpperCAmelCase_						=				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    A__		(			self     ):
       UpperCAmelCase_						=				self.prepare_config_and_inputs()
       UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				config_and_inputs
       UpperCAmelCase_						=				{"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict



     def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				20
       UpperCAmelCase_						=				model_class_name(lowerCAmelCase     )

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

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

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

       UpperCAmelCase_						=				model(lowerCAmelCase     )

       UpperCAmelCase_						=				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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				20
       UpperCAmelCase_						=				model_class_name(lowerCAmelCase     )

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

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

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

       UpperCAmelCase_						=				model(lowerCAmelCase    ,	attention_mask=lowerCAmelCase     )

       UpperCAmelCase_						=				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      (						lowercase__,     lowercase__,     unittest.TestCase  ):







     '''simple docstring'''







     lowerCAmelCase_				:    Union[str, Any]									=  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     lowerCAmelCase_				:    Dict									=  (FlaxGPTJForCausalLM,) if is_flax_available() else ()



     def    A__		(			self     ):
       UpperCAmelCase_						=				FlaxGPTJModelTester(self     )



     def    A__		(			self     ):
       for model_class_name in self.all_model_classes:
         UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



     def    A__		(			self     ):
       for model_class_name in self.all_model_classes:
         UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



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

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

       UpperCAmelCase_						=				jax.jit(model.generate     )

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

       UpperCAmelCase_						=				tokenizer.batch_decode(lowerCAmelCase    ,	skip_special_tokens=lowerCAmelCase     )

       UpperCAmelCase_						=				[
           "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    A__		(			self     ):
       UpperCAmelCase_					,						UpperCAmelCase_						=				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
           UpperCAmelCase_						=				self._prepare_for_class(lowerCAmelCase    ,	lowerCAmelCase     )
           UpperCAmelCase_						=				{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

           UpperCAmelCase_						=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,	lowerCAmelCase     )
           UpperCAmelCase_						=				fx_state

           with torch.no_grad():
             UpperCAmelCase_						=				pt_model(**lowerCAmelCase     ).to_tuple()

           UpperCAmelCase_						=				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     )
             UpperCAmelCase_						=				model_class.from_pretrained(lowerCAmelCase    ,	from_pt=lowerCAmelCase     )

           UpperCAmelCase_						=				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    A__		(			self     ):
       UpperCAmelCase_					,						UpperCAmelCase_						=				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
           UpperCAmelCase_						=				self._prepare_for_class(lowerCAmelCase    ,	lowerCAmelCase     )
           UpperCAmelCase_						=				{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

           UpperCAmelCase_						=				pt_model_class(lowerCAmelCase     ).eval()
           UpperCAmelCase_						=				model_class(lowerCAmelCase    ,	dtype=jnp.floataa     )

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

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

           with torch.no_grad():
             UpperCAmelCase_						=				pt_model(**lowerCAmelCase     ).to_tuple()

           UpperCAmelCase_						=				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     )
             UpperCAmelCase_						=				pt_model_class.from_pretrained(lowerCAmelCase    ,	from_flax=lowerCAmelCase     )

           with torch.no_grad():
             UpperCAmelCase_						=				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    A__		(			self     ):
       for model_class_name in self.all_model_classes:
         UpperCAmelCase_						=				model_class_name.from_pretrained("EleutherAI/gpt-j-6B"     )
         UpperCAmelCase_						=				model(np.ones((1, 1)     )     )
         self.assertIsNotNone(lowerCAmelCase     )






def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						res += a

				a += a
				b >>= 1

		return res







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						UpperCAmelCase_						=				((res % c) + (a % c)) % c

				a += a
				b >>= 1

		return res

