



'''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			,				UpperCamelCase			,				UpperCamelCase=14			,				UpperCamelCase=7			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=False			,				UpperCamelCase=True			,				UpperCamelCase=99			,				UpperCamelCase=32			,				UpperCamelCase=4			,				UpperCamelCase=4			,				UpperCamelCase=4			,				UpperCamelCase=37			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=512			,				UpperCamelCase=0.02			,				)     ->			List[Any]:
						__lowerCAmelCase												=  parent
						__lowerCAmelCase												=  batch_size
						__lowerCAmelCase												=  seq_length
						__lowerCAmelCase												=  is_training
						__lowerCAmelCase												=  use_input_mask
						__lowerCAmelCase												=  use_token_type_ids
						__lowerCAmelCase												=  use_labels
						__lowerCAmelCase												=  vocab_size
						__lowerCAmelCase												=  hidden_size
						__lowerCAmelCase												=  rotary_dim
						__lowerCAmelCase												=  num_hidden_layers
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  intermediate_size
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  hidden_dropout_prob
						__lowerCAmelCase												=  attention_probs_dropout_prob
						__lowerCAmelCase												=  max_position_embeddings
						__lowerCAmelCase												=  initializer_range
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  vocab_size - 1
						__lowerCAmelCase												=  vocab_size - 1
						__lowerCAmelCase												=  vocab_size - 1



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size     )

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

						__lowerCAmelCase												=  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=UpperCamelCase			,				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        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.prepare_config_and_inputs()
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  config_and_inputs
						__lowerCAmelCase												=  {"input_ids": input_ids, "attention_mask": attention_mask}
						return config, inputs_dict



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[int]:
						__lowerCAmelCase												=  20
						__lowerCAmelCase												=  model_class_name(UpperCamelCase     )

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

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

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

						__lowerCAmelCase												=  model(UpperCamelCase     )

						__lowerCAmelCase												=  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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Tuple:
						__lowerCAmelCase												=  20
						__lowerCAmelCase												=  model_class_name(UpperCamelCase     )

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

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

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

						__lowerCAmelCase												=  model(UpperCamelCase			,				attention_mask=UpperCamelCase     )

						__lowerCAmelCase												=  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__	(		UpperCamelCase__							,       UpperCamelCase__							,       unittest.TestCase ):
					a      :     Union[str, Any]        =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
					a      :     Any        =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()



					def        UpperCAmelCase_ (    self     )     ->			int:
						__lowerCAmelCase												=  FlaxGPTJModelTester(self     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						for model_class_name in self.all_model_classes:
							__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						for model_class_name in self.all_model_classes:
							__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )



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

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

						__lowerCAmelCase												=  jax.jit(model.generate     )

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

						__lowerCAmelCase												=  tokenizer.batch_decode(UpperCamelCase			,				skip_special_tokens=UpperCamelCase     )

						__lowerCAmelCase												=  [
						    "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(UpperCamelCase			,				UpperCamelCase     )



					@is_pt_flax_cross_test
					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase							,       __lowerCAmelCase												=  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
								__lowerCAmelCase												=  self._prepare_for_class(UpperCamelCase			,				UpperCamelCase     )
								__lowerCAmelCase												=  {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

								__lowerCAmelCase												=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,				UpperCamelCase     )
								__lowerCAmelCase												=  fx_state

								with torch.no_grad():
									__lowerCAmelCase												=  pt_model(**UpperCamelCase     ).to_tuple()

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

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

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



					@is_pt_flax_cross_test
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase												=  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
								__lowerCAmelCase												=  self._prepare_for_class(UpperCamelCase			,				UpperCamelCase     )
								__lowerCAmelCase												=  {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

								__lowerCAmelCase												=  pt_model_class(UpperCamelCase     ).eval()
								__lowerCAmelCase												=  model_class(UpperCamelCase			,				dtype=jnp.floataa     )

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

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

								with torch.no_grad():
									__lowerCAmelCase												=  pt_model(**UpperCamelCase     ).to_tuple()

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

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

								with torch.no_grad():
									__lowerCAmelCase												=  pt_model_loaded(**UpperCamelCase     ).to_tuple()

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



					@tooslow
					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						for model_class_name in self.all_model_classes:
							__lowerCAmelCase												=  model_class_name.from_pretrained("EleutherAI/gpt-j-6B"     )
							__lowerCAmelCase												=  model(np.ones((1, 1)     )     )
							self.assertIsNotNone(UpperCamelCase     )




'''simple docstring'''



from __future__ import annotations



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




	if not nums:
		raise ValueError("List is empty"					)
	return sum(lowerCamelCase					) / len(lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()