
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
	import torch





class 		__lowerCamelCase     :



							def __init__(     self	,				__snake_case	,				__snake_case=1_4	,				__snake_case=7	,				__snake_case=True	,				__snake_case=True	,				__snake_case=False	,				__snake_case=True	,				__snake_case=9_9	,				__snake_case=3_2	,				__snake_case=4	,				__snake_case=4	,				__snake_case=[[["global", "local"], 2]]	,				__snake_case=3_7	,				__snake_case="gelu"	,				__snake_case=0.1	,				__snake_case=0.1	,				__snake_case=5_1_2	,				__snake_case=7	,				__snake_case=0.02	,				)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     parent
								UpperCAmelCase:					Tuple 						=     batch_size
								UpperCAmelCase:					Dict 						=     seq_length
								UpperCAmelCase:					Dict 						=     is_training
								UpperCAmelCase:					Union[str, Any] 						=     use_input_mask
								UpperCAmelCase:					Union[str, Any] 						=     use_token_type_ids
								UpperCAmelCase:					str 						=     use_labels
								UpperCAmelCase:					int 						=     vocab_size
								UpperCAmelCase:					List[str] 						=     hidden_size
								UpperCAmelCase:					Optional[Any] 						=     num_hidden_layers
								UpperCAmelCase:					Dict 						=     num_attention_heads
								UpperCAmelCase:					Tuple 						=     attention_types
								UpperCAmelCase:					str 						=     intermediate_size
								UpperCAmelCase:					str 						=     hidden_act
								UpperCAmelCase:					Optional[int] 						=     hidden_dropout_prob
								UpperCAmelCase:					str 						=     attention_probs_dropout_prob
								UpperCAmelCase:					Optional[Any] 						=     max_position_embeddings
								UpperCAmelCase:					Union[str, Any] 						=     window_size
								UpperCAmelCase:					Optional[int] 						=     initializer_range
								UpperCAmelCase:					List[Any] 						=     None
								UpperCAmelCase:					Tuple 						=     vocab_size - 1
								UpperCAmelCase:					Union[str, Any] 						=     vocab_size - 1
								UpperCAmelCase:					Dict 						=     vocab_size - 1



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size							)

								UpperCAmelCase:					Dict 						=     None
								if self.use_input_mask:
									UpperCAmelCase:					List[str] 						=     random_attention_mask([self.batch_size, self.seq_length]							)

								UpperCAmelCase:					Union[str, Any] 						=     GPTNeoConfig(
								    vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_layers=self.num_hidden_layers	,				num_heads=self.num_attention_heads	,				max_position_embeddings=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	,				window_size=self.window_size	,				attention_types=self.attention_types	,				)

								return (config, input_ids, input_mask)



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.prepare_config_and_inputs()
								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					List[str] 						=     config_and_inputs
								UpperCAmelCase:					str 						=     {"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     2_0
								UpperCAmelCase:					Optional[int] 						=     model_class_name(__snake_case							)

								UpperCAmelCase:					Optional[int] 						=     model.init_cache(input_ids.shape[0]	,				__snake_case							)
								UpperCAmelCase:					Union[str, Any] 						=     jnp.ones((input_ids.shape[0], max_decoder_length)	,				dtype="i4"							)

								UpperCAmelCase:					Any 						=     jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1							)[None, :]	,				(input_ids.shape[0], input_ids.shape[-1] - 1)							)
								UpperCAmelCase:					Dict 						=     model(
								    input_ids[:, :-1]	,				attention_mask=__snake_case	,				past_key_values=__snake_case	,				position_ids=__snake_case	,				)

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

								UpperCAmelCase:					int 						=     model(__snake_case							)

								UpperCAmelCase:					Dict 						=     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	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     2_0
								UpperCAmelCase:					int 						=     model_class_name(__snake_case							)

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

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

								UpperCAmelCase:					Union[str, Any] 						=     model(
								    input_ids[:, :-1]	,				attention_mask=__snake_case	,				past_key_values=__snake_case	,				position_ids=__snake_case	,				)
								UpperCAmelCase:					Dict 						=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,				dtype="i4"							)
								UpperCAmelCase:					List[str] 						=     model(
								    input_ids[:, -1:]	,				past_key_values=outputs_cache.past_key_values	,				attention_mask=__snake_case	,				position_ids=__snake_case	,				)

								UpperCAmelCase:					Optional[int] 						=     model(__snake_case	,				attention_mask=__snake_case							)

								UpperCAmelCase:					Tuple 						=     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		):
							lowerCamelCase__:				int      =							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
							lowerCamelCase__:				Optional[int]      =							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     FlaxGPTNeoModelTester(self							)



							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								for model_class_name in self.all_model_classes:
									UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Union[str, Any] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)



							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								for model_class_name in self.all_model_classes:
									UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					List[str] 						=     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							)



							@slow
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     GPTaTokenizer.from_pretrained("gpt2"	,				pad_token="<|endoftext|>"	,				padding_side="left"							)
								UpperCAmelCase:					List[str] 						=     tokenizer(["Hello this is a long string", "Hey"]	,				return_tensors="np"	,				padding=__snake_case	,				truncation=__snake_case							)

								UpperCAmelCase:					Optional[Any] 						=     FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"							)
								UpperCAmelCase:					Tuple 						=     False
								UpperCAmelCase:					int 						=     model.config.eos_token_id

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

								UpperCAmelCase:					Optional[int] 						=     jit_generate(
								    inputs["input_ids"]	,				attention_mask=inputs["attention_mask"]	,				pad_token_id=tokenizer.pad_token_id							).sequences

								UpperCAmelCase:					Dict 						=     tokenizer.batch_decode(__snake_case	,				skip_special_tokens=__snake_case							)

								UpperCAmelCase:					List[str] 						=     [
								    "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     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Tuple 						=     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:					str 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
										UpperCAmelCase:					Dict 						=     {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

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

										UpperCAmelCase:					Tuple 						=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,				__snake_case							)
										UpperCAmelCase:					Tuple 						=     fx_state

										with torch.no_grad():
											UpperCAmelCase:					List[str] 						=     pt_model(**__snake_case							).to_tuple()

										UpperCAmelCase:					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:
											pt_model.save_pretrained(__snake_case							)
											UpperCAmelCase:					List[str] 						=     model_class.from_pretrained(__snake_case	,				from_pt=__snake_case							)

										UpperCAmelCase:					str 						=     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     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					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
										UpperCAmelCase:					Optional[int] 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
										UpperCAmelCase:					Any 						=     {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

										UpperCAmelCase:					Optional[Any] 						=     pt_model_class(__snake_case							).eval()
										UpperCAmelCase:					Tuple 						=     model_class(__snake_case	,				dtype=jnp.floataa							)

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

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

										with torch.no_grad():
											UpperCAmelCase:					List[Any] 						=     pt_model(**__snake_case							).to_tuple()

										UpperCAmelCase:					List[Any] 						=     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							)
											UpperCAmelCase:					Any 						=     pt_model_class.from_pretrained(__snake_case	,				from_flax=__snake_case							)

										with torch.no_grad():
											UpperCAmelCase:					Optional[Any] 						=     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							)




							@slow
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								for model_class_name in self.all_model_classes:
									UpperCAmelCase:					Union[str, Any] 						=     model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"							)
									UpperCAmelCase:					Optional[int] 						=     model(np.ones((1, 1)							)							)
									self.assertIsNotNone(__snake_case							)


import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
