






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:   Optional[Any]		,     __A:   Tuple		,     __A:   Optional[Any]=14		,     __A:   Optional[Any]=7		,     __A:   Optional[Any]=True		,     __A:   Optional[int]=True		,     __A:   str=False		,     __A:   Any=True		,     __A:   Optional[Any]=99		,     __A:   Optional[Any]=32		,     __A:   Optional[int]=4		,     __A:   List[Any]=4		,     __A:   Optional[int]=4		,     __A:   Dict=37		,     __A:   Union[str, Any]="gelu"		,     __A:   Any=0.1		,     __A:   Optional[int]=0.1		,     __A:   List[Any]=5_12		,     __A:   List[str]=0.02		,     )    ->				List[Any]:
												_A								= parent
												_A								= batch_size
												_A								= seq_length
												_A								= is_training
												_A								= use_input_mask
												_A								= use_token_type_ids
												_A								= use_labels
												_A								= vocab_size
												_A								= hidden_size
												_A								= rotary_dim
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= intermediate_size
												_A								= hidden_act
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= initializer_range
												_A								= None
												_A								= vocab_size - 1
												_A								= vocab_size - 1
												_A								= vocab_size - 1

							def       __A (  self:   str   )    ->				Any:
												_A								= ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size   )

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

												_A								= 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       __A (  self:   Any   )    ->				List[str]:
												_A								= self.prepare_config_and_inputs()
												_A							,_A							,_A								= config_and_inputs
												_A								= {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict

							def       __A (  self:   Dict		,     __A:   int		,     __A:   Tuple		,     __A:   int		,     __A:   int   )    ->				Dict:
												_A								= 20
												_A								= model_class_name(__A   )

												_A								= model.init_cache(input_ids.shape[0]		,     __A   )
												_A								= jnp.ones((input_ids.shape[0], max_decoder_length)		,     dtype='''i4'''   )

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

												_A								= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,     dtype='''i4'''   )
												_A								= model(
												    input_ids[:, -1:]		,     attention_mask=__A		,     past_key_values=outputs_cache.past_key_values		,     position_ids=__A		,     )

												_A								= model(__A   )

												_A								= 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:   Any		,     __A:   Optional[Any]		,     __A:   Any		,     __A:   Optional[int]		,     __A:   Dict   )    ->				str:
												_A								= 20
												_A								= model_class_name(__A   )

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

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

												_A								= model(
												    input_ids[:, :-1]		,     attention_mask=__A		,     past_key_values=__A		,     position_ids=__A		,     )
												_A								= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,     dtype='''i4'''   )
												_A								= model(
												    input_ids[:, -1:]		,     past_key_values=outputs_cache.past_key_values		,     attention_mask=__A		,     position_ids=__A		,     )

												_A								= model(__A		,     attention_mask=__A   )

												_A								= 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				( snake_case  ,	snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							A_						  =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= FlaxGPTJModelTester(self   )

							def       __A (  self:   Any   )    ->				Optional[Any]:
												for model_class_name in self.all_model_classes:
																	_A							,_A							,_A								= self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(__A		,     __A		,     __A		,     __A   )

							def       __A (  self:   int   )    ->				Union[str, Any]:
												for model_class_name in self.all_model_classes:
																	_A							,_A							,_A								= self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    __A		,     __A		,     __A		,     __A   )

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

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

												_A								= jax.jit(model.generate   )

												_A								= jit_generate(
												    inputs['''input_ids''']		,     attention_mask=inputs['''attention_mask''']		,     pad_token_id=tokenizer.pad_token_id   ).sequences

												_A								= tokenizer.batch_decode(__A		,     skip_special_tokens=__A   )

												_A								= [
												    '''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       __A (  self:   Optional[Any]   )    ->				Union[str, Any]:
												_A							,_A								= 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								= self._prepare_for_class(__A		,     __A   )
																						_A								= {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

																						_A								= convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,     __A   )
																						_A								= fx_state

																						with torch.no_grad():
																											_A								= pt_model(**__A   ).to_tuple()

																						_A								= 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   )
																											_A								= model_class.from_pretrained(__A		,     from_pt=__A   )

																						_A								= 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       __A (  self:   List[Any]   )    ->				List[str]:
												_A							,_A								= 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								= self._prepare_for_class(__A		,     __A   )
																						_A								= {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

																						_A								= pt_model_class(__A   ).eval()
																						_A								= model_class(__A		,     dtype=jnp.floataa   )

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

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

																						with torch.no_grad():
																											_A								= pt_model(**__A   ).to_tuple()

																						_A								= 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   )
																											_A								= pt_model_class.from_pretrained(__A		,     from_flax=__A   )

																						with torch.no_grad():
																											_A								= 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       __A (  self:   Tuple   )    ->				Tuple:
												for model_class_name in self.all_model_classes:
																	_A								= model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''   )
																	_A								= model(np.ones((1, 1)   )   )
																	self.assertIsNotNone(__A   )











import flax.linen as nn
import jax
import jax.numpy as jnp





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   Tuple   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   Dict   )    ->				Tuple:
												_A							,_A							,_A							,_A								= hidden_states.shape
												_A								= jax.image.resize(
												    __A		,     shape=(batch, height * 2, width * 2, channels)		,     method='''nearest'''		,     )
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   List[str]   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(2, 2)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Union[str, Any]		,     __A:   List[Any]   )    ->				Union[str, Any]:
												# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
												# hidden_states = jnp.pad(hidden_states, pad_width=pad)
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					None
							A_						  =					0.0
							A_						  =					None
							A_						  =					jnp.floataa

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= self.in_channels if self.out_channels is None else self.out_channels

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= nn.Dense(__A		,     dtype=self.dtype   )

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Dropout(self.dropout_prob   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

												_A								= None
												if use_nin_shortcut:
																	_A								= nn.Conv(
																	    __A		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding='''VALID'''		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any=True   )    ->				List[Any]:
												_A								= hidden_states
												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.conva(__A   )

												_A								= self.time_emb_proj(nn.swish(__A   )   )
												_A								= jnp.expand_dims(jnp.expand_dims(__A		,     1   )		,     1   )
												_A								= hidden_states + temb

												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.dropout(__A		,     __A   )
												_A								= self.conva(__A   )

												if self.conv_shortcut is not None:
																	_A								= self.conv_shortcut(__A   )

												return hidden_states + residual



