

'''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     a			:






						"""simple docstring"""




						def __init__(							self					:      List[str]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      List[Any]=1_4			,			snake_case_					:      int=7			,			snake_case_					:      int=True			,			snake_case_					:      int=True			,			snake_case_					:      List[str]=False			,			snake_case_					:      Tuple=True			,			snake_case_					:      Tuple=9_9			,			snake_case_					:      List[Any]=3_2			,			snake_case_					:      Union[str, Any]=4			,			snake_case_					:      Any=4			,			snake_case_					:      Optional[int]=4			,			snake_case_					:      Optional[Any]=3_7			,			snake_case_					:      Any="gelu"			,			snake_case_					:      List[str]=0.1			,			snake_case_					:      List[str]=0.1			,			snake_case_					:      str=5_1_2			,			snake_case_					:      List[Any]=0.0_2			,			):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



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

								snake_case__						:       Optional[int]       						=  None
								if self.use_input_mask:
										snake_case__						:       List[str]       						=  random_attention_mask([self.batch_size, self.seq_length]  )

								snake_case__						:       List[str]       						=  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=snake_case_			,			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       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.prepare_config_and_inputs()
								snake_case__						,       snake_case__						,       snake_case__						:       Union[str, Any]       						=  config_and_inputs
								snake_case__						:       Optional[Any]       						=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Dict			,			snake_case_					:      List[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  2_0
								snake_case__						:       int       						=  model_class_name(snake_case_  )

								snake_case__						:       Optional[Any]       						=  model.init_cache(input_ids.shape[0]			,			snake_case_  )
								snake_case__						:       Dict       						=  jnp.ones((input_ids.shape[0], max_decoder_length)			,			dtype='''i4'''  )

								snake_case__						:       Union[str, Any]       						=  jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)  )
								snake_case__						:       List[Any]       						=  model(
								    input_ids[:, :-1]			,			attention_mask=snake_case_			,			past_key_values=snake_case_			,			position_ids=snake_case_			,			)

								snake_case__						:       str       						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,			dtype='''i4'''  )
								snake_case__						:       Union[str, Any]       						=  model(
								    input_ids[:, -1:]			,			attention_mask=snake_case_			,			past_key_values=outputs_cache.past_key_values			,			position_ids=snake_case_			,			)

								snake_case__						:       int       						=  model(snake_case_  )

								snake_case__						:       int       						=  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       __magic_name__							(							self					:      str			,			snake_case_					:      List[str]			,			snake_case_					:      List[str]			,			snake_case_					:      int			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  2_0
								snake_case__						:       str       						=  model_class_name(snake_case_  )

								snake_case__						:       Optional[Any]       						=  jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]			,			axis=-1			,			)

								snake_case__						:       Union[str, Any]       						=  model.init_cache(input_ids.shape[0]			,			snake_case_  )
								snake_case__						:       Optional[Any]       						=  jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)  )

								snake_case__						:       Optional[int]       						=  model(
								    input_ids[:, :-1]			,			attention_mask=snake_case_			,			past_key_values=snake_case_			,			position_ids=snake_case_			,			)
								snake_case__						:       Union[str, Any]       						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,			dtype='''i4'''  )
								snake_case__						:       Dict       						=  model(
								    input_ids[:, -1:]			,			past_key_values=outputs_cache.past_key_values			,			attention_mask=snake_case_			,			position_ids=snake_case_			,			)

								snake_case__						:       List[Any]       						=  model(snake_case_			,			attention_mask=snake_case_  )

								snake_case__						:       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}"""  )





@require_flax
class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




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







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  FlaxGPTJModelTester(self  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								for model_class_name in self.all_model_classes:
										snake_case__						,       snake_case__						,       snake_case__						:       Any       						=  self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward(snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								for model_class_name in self.all_model_classes:
										snake_case__						,       snake_case__						,       snake_case__						:       Any       						=  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_  )







						@tooslow
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



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

								snake_case__						:       List[Any]       						=  FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''  )
								snake_case__						:       Union[str, Any]       						=  False
								snake_case__						:       List[str]       						=  model.config.eos_token_id

								snake_case__						:       Tuple       						=  jax.jit(model.generate  )

								snake_case__						:       List[str]       						=  jit_generate(
								    inputs['''input_ids''']			,			attention_mask=inputs['''attention_mask''']			,			pad_token_id=tokenizer.pad_token_id  ).sequences

								snake_case__						:       Union[str, Any]       						=  tokenizer.batch_decode(snake_case_			,			skip_special_tokens=snake_case_  )

								snake_case__						:       Any       						=  [
								    '''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       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       int       						=  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
												snake_case__						:       Optional[int]       						=  self._prepare_for_class(snake_case_			,			snake_case_  )
												snake_case__						:       List[str]       						=  {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

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

												snake_case__						:       Any       						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,			snake_case_  )
												snake_case__						:       Union[str, Any]       						=  fx_state

												with torch.no_grad():
														snake_case__						:       Union[str, Any]       						=  pt_model(**snake_case_  ).to_tuple()

												snake_case__						:       Union[str, 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:
														pt_model.save_pretrained(snake_case_  )
														snake_case__						:       List[str]       						=  model_class.from_pretrained(snake_case_			,			from_pt=snake_case_  )

												snake_case__						:       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       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



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

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

												snake_case__						:       Any       						=  pt_model_class(snake_case_  ).eval()
												snake_case__						:       Dict       						=  model_class(snake_case_			,			dtype=jnp.floataa  )

												snake_case__						:       Dict       						=  load_flax_weights_in_pytorch_model(snake_case_			,			fx_model.params  )
												snake_case__						,       snake_case__						:       Union[str, Any]       						=  pt_inputs['''input_ids'''].shape
												snake_case__						:       List[Any]       						=  np.random.randint(0			,			seq_length - 1			,			size=(batch_size,)  )
												for batch_idx, start_index in enumerate(snake_case_  ):
														snake_case__						:       Tuple       						=  0
														snake_case__						:       List[Any]       						=  1
														snake_case__						:       Tuple       						=  0
														snake_case__						:       Tuple       						=  1

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

												with torch.no_grad():
														snake_case__						:       Union[str, Any]       						=  pt_model(**snake_case_  ).to_tuple()

												snake_case__						:       Dict       						=  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_  )
														snake_case__						:       Optional[int]       						=  pt_model_class.from_pretrained(snake_case_			,			from_flax=snake_case_  )

												with torch.no_grad():
														snake_case__						:       str       						=  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  )







						@tooslow
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								for model_class_name in self.all_model_classes:
										snake_case__						:       List[str]       						=  model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''  )
										snake_case__						:       Optional[Any]       						=  model(np.ones((1, 1)  )  )
										self.assertIsNotNone(snake_case_  )






'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    None





class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    2







						@register_to_config
						def __init__(							self					:      int			,			snake_case_					:      float = 0.0_2			,			snake_case_					:      float = 1_0_0			,			snake_case_					:      float = 1.0_0_7			,			snake_case_					:      float = 8_0			,			snake_case_					:      float = 0.0_5			,			snake_case_					:      float = 5_0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  sigma_max

								# setable values
								snake_case__						:       int       						=  None
								snake_case__						:       np.IntTensor       						=  None
								snake_case__						:       torch.FloatTensor       						=  None  # sigma(t_i)







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      int			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  num_inference_steps
								snake_case__						:       Tuple       						=  np.arange(0			,			self.num_inference_steps  )[::-1].copy()
								snake_case__						:       int       						=  torch.from_numpy(snake_case_  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								snake_case__						:       Union[str, Any]       						=  torch.tensor(snake_case_			,			dtype=torch.floataa			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      Optional[torch.Generator] = None  ):



								'''simple docstring'''



								if self.config.s_min <= sigma <= self.config.s_max:
										snake_case__						:       List[Any]       						=  min(self.config.s_churn / self.num_inference_steps			,			2**0.5 - 1  )
								else:
										snake_case__						:       List[Any]       						=  0

								# sample eps ~ N(0, S_noise^2 * I)
								snake_case__						:       Any       						=  self.config.s_noise * randn_tensor(sample.shape			,			generator=snake_case_  ).to(sample.device  )
								snake_case__						:       Tuple       						=  sigma + gamma * sigma
								snake_case__						:       Dict       						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  sample_hat + sigma_hat * model_output
								snake_case__						:       List[Any]       						=  (sample_hat - pred_original_sample) / sigma_hat
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  sample_prev + sigma_prev * model_output
								snake_case__						:       List[Any]       						=  (sample_prev - pred_original_sample) / sigma_prev
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								raise NotImplementedError()



