




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        __snake_case  :



				def __init__(      self      :		str		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		Any=14		,	_UpperCAmelCase      :		Optional[Any]=7		,	_UpperCAmelCase      :		Optional[int]=True		,	_UpperCAmelCase      :		List[Any]=True		,	_UpperCAmelCase      :		int=False		,	_UpperCAmelCase      :		str=True		,	_UpperCAmelCase      :		Optional[int]=99		,	_UpperCAmelCase      :		Union[str, Any]=32		,	_UpperCAmelCase      :		Union[str, Any]=4		,	_UpperCAmelCase      :		List[Any]=4		,	_UpperCAmelCase      :		int=4		,	_UpperCAmelCase      :		List[str]=37		,	_UpperCAmelCase      :		int="gelu"		,	_UpperCAmelCase      :		List[Any]=0.1		,	_UpperCAmelCase      :		List[str]=0.1		,	_UpperCAmelCase      :		Optional[Any]=512		,	_UpperCAmelCase      :		int=0.02		,	)  ->    List[Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size      )

								_lowerCAmelCase    :    int										=      None
								if self.use_input_mask:
												_lowerCAmelCase    :    List[Any]										=      random_attention_mask([self.batch_size, self.seq_length]      )

								_lowerCAmelCase    :    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=__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       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.prepare_config_and_inputs()
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    int										=      config_and_inputs
								_lowerCAmelCase    :    int										=      {"""input_ids""": input_ids, """attention_mask""": attention_mask}
								return config, inputs_dict



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      20
								_lowerCAmelCase    :    Tuple										=      model_class_name(__A      )

								_lowerCAmelCase    :    str										=      model.init_cache(input_ids.shape[0]		,	__A      )
								_lowerCAmelCase    :    str										=      jnp.ones((input_ids.shape[0], max_decoder_length)		,	dtype="""i4"""      )

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

								_lowerCAmelCase    :    Optional[int]										=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,	dtype="""i4"""      )
								_lowerCAmelCase    :    Dict										=      model(
								    input_ids[:, -1:]		,	attention_mask=__A		,	past_key_values=outputs_cache.past_key_values		,	position_ids=__A		,	)

								_lowerCAmelCase    :    Optional[Any]										=      model(__A      )

								_lowerCAmelCase    :    List[Any]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      20
								_lowerCAmelCase    :    int										=      model_class_name(__A      )

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

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

								_lowerCAmelCase    :    Tuple										=      model(
								    input_ids[:, :-1]		,	attention_mask=__A		,	past_key_values=__A		,	position_ids=__A		,	)
								_lowerCAmelCase    :    List[Any]										=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,	dtype="""i4"""      )
								_lowerCAmelCase    :    Tuple										=      model(
								    input_ids[:, -1:]		,	past_key_values=outputs_cache.past_key_values		,	attention_mask=__A		,	position_ids=__A		,	)

								_lowerCAmelCase    :    Any										=      model(__A		,	attention_mask=__A      )

								_lowerCAmelCase    :    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        __snake_case  (UpperCamelCase__						,	UpperCamelCase__						,	unittest.TestCase		):
				lowerCAmelCase__				=     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
				lowerCAmelCase__				=     (FlaxGPTJForCausalLM,) if is_flax_available() else ()



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





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      FlaxGPTJModelTester(self      )



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





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(__A		,	__A		,	__A		,	__A      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    __A		,	__A		,	__A		,	__A      )



				@tooslow
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    int:





								'''simple docstring'''




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

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

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

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

								_lowerCAmelCase    :    Any										=      tokenizer.batch_decode(__A		,	skip_special_tokens=__A      )

								_lowerCAmelCase    :    int										=      [
								    """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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    str:





								'''simple docstring'''




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

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

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

																_lowerCAmelCase    :    List[str]										=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,	__A      )
																_lowerCAmelCase    :    Union[str, Any]										=      fx_state

																with torch.no_grad():
																				_lowerCAmelCase    :    Optional[int]										=      pt_model(**__A      ).to_tuple()

																_lowerCAmelCase    :    Union[str, Any]										=      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      )
																				_lowerCAmelCase    :    Tuple										=      model_class.from_pretrained(__A		,	from_pt=__A      )

																_lowerCAmelCase    :    Any										=      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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Dict:





								'''simple docstring'''




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

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

																_lowerCAmelCase    :    Tuple										=      pt_model_class(__A      ).eval()
																_lowerCAmelCase    :    Tuple										=      model_class(__A		,	dtype=jnp.floataa      )

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

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

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

																_lowerCAmelCase    :    Tuple										=      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      )
																				_lowerCAmelCase    :    List[str]										=      pt_model_class.from_pretrained(__A		,	from_flax=__A      )

																with torch.no_grad():
																				_lowerCAmelCase    :    List[str]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Tuple:





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase    :    Optional[Any]										=      model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""      )
												_lowerCAmelCase    :    Dict										=      model(np.ones((1, 1)      )      )
												self.assertIsNotNone(__A      )







from __future__ import annotations

from decimal import Decimal

from numpy import array




def 							_UpperCAmelCase  (UpperCamelCase_ : list[list[float]]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      Decimal

				# Check if the provided matrix has 2 rows and 2 columns
				# since this implementation only works for 2x2 matrices
				if len(UpperCamelCase_       ) == 2 and len(matrix[0]       ) == 2 and len(matrix[1]       ) == 2:
								# Calculate the determinant of the matrix
								_lowerCAmelCase    :    Optional[Any]										=      float(
								    d(matrix[0][0]       ) * d(matrix[1][1]       ) - d(matrix[1][0]       ) * d(matrix[0][1]       )       )
								if determinant == 0:
												raise ValueError("""This matrix has no inverse."""       )

								# Creates a copy of the matrix with swapped positions of the elements
								_lowerCAmelCase    :    Union[str, Any]										=      [[0.0, 0.0], [0.0, 0.0]]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      matrix[1][1], matrix[0][0]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      -matrix[1][0], -matrix[0][1]

								# Calculate the inverse of the matrix
								return [
								    [(float(d(UpperCamelCase_       )       ) / determinant) or 0.0 for n in row] for row in swapped_matrix
								]
				elif (
				    len(UpperCamelCase_       ) == 3
				    and len(matrix[0]       ) == 3
				    and len(matrix[1]       ) == 3
				    and len(matrix[2]       ) == 3
				):
								# Calculate the determinant of the matrix using Sarrus rule
								_lowerCAmelCase    :    List[str]										=      float(
								    (
								        (d(matrix[0][0]       ) * d(matrix[1][1]       ) * d(matrix[2][2]       ))
								        + (d(matrix[0][1]       ) * d(matrix[1][2]       ) * d(matrix[2][0]       ))
								        + (d(matrix[0][2]       ) * d(matrix[1][0]       ) * d(matrix[2][1]       ))
								    )
								    - (
								        (d(matrix[0][2]       ) * d(matrix[1][1]       ) * d(matrix[2][0]       ))
								        + (d(matrix[0][1]       ) * d(matrix[1][0]       ) * d(matrix[2][2]       ))
								        + (d(matrix[0][0]       ) * d(matrix[1][2]       ) * d(matrix[2][1]       ))
								    )       )
								if determinant == 0:
												raise ValueError("""This matrix has no inverse."""       )

								# Creating cofactor matrix
								_lowerCAmelCase    :    Optional[Any]										=      [
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								]
								_lowerCAmelCase    :    Optional[Any]										=      (d(matrix[1][1]       ) * d(matrix[2][2]       )) - (
								    d(matrix[1][2]       ) * d(matrix[2][1]       )
								)
								_lowerCAmelCase    :    str										=      -(
								    (d(matrix[1][0]       ) * d(matrix[2][2]       )) - (d(matrix[1][2]       ) * d(matrix[2][0]       ))
								)
								_lowerCAmelCase    :    Any										=      (d(matrix[1][0]       ) * d(matrix[2][1]       )) - (
								    d(matrix[1][1]       ) * d(matrix[2][0]       )
								)
								_lowerCAmelCase    :    List[str]										=      -(
								    (d(matrix[0][1]       ) * d(matrix[2][2]       )) - (d(matrix[0][2]       ) * d(matrix[2][1]       ))
								)
								_lowerCAmelCase    :    Tuple										=      (d(matrix[0][0]       ) * d(matrix[2][2]       )) - (
								    d(matrix[0][2]       ) * d(matrix[2][0]       )
								)
								_lowerCAmelCase    :    Any										=      -(
								    (d(matrix[0][0]       ) * d(matrix[2][1]       )) - (d(matrix[0][1]       ) * d(matrix[2][0]       ))
								)
								_lowerCAmelCase    :    int										=      (d(matrix[0][1]       ) * d(matrix[1][2]       )) - (
								    d(matrix[0][2]       ) * d(matrix[1][1]       )
								)
								_lowerCAmelCase    :    Optional[Any]										=      -(
								    (d(matrix[0][0]       ) * d(matrix[1][2]       )) - (d(matrix[0][2]       ) * d(matrix[1][0]       ))
								)
								_lowerCAmelCase    :    Tuple										=      (d(matrix[0][0]       ) * d(matrix[1][1]       )) - (
								    d(matrix[0][1]       ) * d(matrix[1][0]       )
								)

								# Transpose the cofactor matrix (Adjoint matrix)
								_lowerCAmelCase    :    List[str]										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																_lowerCAmelCase    :    List[Any]										=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								_lowerCAmelCase    :    Tuple										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																inverse_matrix[i][j] /= d(UpperCamelCase_       )

        # Calculate the inverse of the matrix
								return [[float(d(UpperCamelCase_       )       ) or 0.0 for n in row] for row in inverse_matrix]
				raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""       )

