


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      snake_case__	:
				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=[[["global", "local"], 2]]  ,   _UpperCAmelCase=37  ,   _UpperCAmelCase="gelu"  ,   _UpperCAmelCase=0.1  ,   _UpperCAmelCase=0.1  ,   _UpperCAmelCase=512  ,   _UpperCAmelCase=7  ,   _UpperCAmelCase=0.02  ,   ):




										"""simple docstring"""




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




										"""simple docstring"""




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

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

										__magic_name__					:				Optional[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=_UpperCAmelCase  ,   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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   self.prepare_config_and_inputs()
										__magic_name__		,			__magic_name__		,			__magic_name__					:				Dict				=   config_and_inputs
										__magic_name__					:				Union[str, Any]				=   {'input_ids': input_ids, 'attention_mask': attention_mask}
										return config, inputs_dict
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   20
										__magic_name__					:				Dict				=   model_class_name(_UpperCAmelCase   )

										__magic_name__					:				List[Any]				=   model.init_cache(input_ids.shape[0]  ,   _UpperCAmelCase   )
										__magic_name__					:				Dict				=   jnp.ones((input_ids.shape[0], max_decoder_length)  ,   dtype='i4'   )

										__magic_name__					:				List[str]				=   jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1   )[None, :]  ,   (input_ids.shape[0], input_ids.shape[-1] - 1)   )
										__magic_name__					:				Tuple				=   model(
										    input_ids[:, :-1]  ,   attention_mask=_UpperCAmelCase  ,   past_key_values=_UpperCAmelCase  ,   position_ids=_UpperCAmelCase  ,   )

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

										__magic_name__					:				Any				=   model(_UpperCAmelCase   )

										__magic_name__					:				Union[str, 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 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   20
										__magic_name__					:				Union[str, Any]				=   model_class_name(_UpperCAmelCase   )

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

										__magic_name__					:				str				=   model.init_cache(input_ids.shape[0]  ,   _UpperCAmelCase   )
										__magic_name__					:				List[Any]				=   jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1   )[None, :]  ,   (input_ids.shape[0], input_ids.shape[-1] - 1)   )

										__magic_name__					:				int				=   model(
										    input_ids[:, :-1]  ,   attention_mask=_UpperCAmelCase  ,   past_key_values=_UpperCAmelCase  ,   position_ids=_UpperCAmelCase  ,   )
										__magic_name__					:				Any				=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,   dtype='i4'   )
										__magic_name__					:				List[str]				=   model(
										    input_ids[:, -1:]  ,   past_key_values=outputs_cache.past_key_values  ,   attention_mask=_UpperCAmelCase  ,   position_ids=_UpperCAmelCase  ,   )

										__magic_name__					:				Any				=   model(_UpperCAmelCase  ,   attention_mask=_UpperCAmelCase   )

										__magic_name__					:				List[str]				=   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__	(   _A    ,							_A    ,							unittest.TestCase	):
				__lowerCAmelCase		=   (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
				__lowerCAmelCase		=   (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   FlaxGPTNeoModelTester(self   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for model_class_name in self.all_model_classes:
																__magic_name__		,			__magic_name__		,			__magic_name__					:				Optional[Any]				=   self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for model_class_name in self.all_model_classes:
																__magic_name__		,			__magic_name__		,			__magic_name__					:				Any				=   self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward_with_attn_mask(
																    _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )
				@slow
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   GPTaTokenizer.from_pretrained('gpt2'  ,   pad_token='<|endoftext|>'  ,   padding_side='left'   )
										__magic_name__					:				Tuple				=   tokenizer(['Hello this is a long string', 'Hey']  ,   return_tensors='np'  ,   padding=_UpperCAmelCase  ,   truncation=_UpperCAmelCase   )

										__magic_name__					:				Union[str, Any]				=   FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'   )
										__magic_name__					:				Any				=   False
										__magic_name__					:				Dict				=   model.config.eos_token_id

										__magic_name__					:				int				=   jax.jit(model.generate   )

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

										__magic_name__					:				Dict				=   tokenizer.batch_decode(_UpperCAmelCase  ,   skip_special_tokens=_UpperCAmelCase   )

										__magic_name__					:				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(_UpperCAmelCase  ,   _UpperCAmelCase   )
				@is_pt_flax_cross_test
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				List[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
																						__magic_name__					:				Tuple				=   self._prepare_for_class(_UpperCAmelCase  ,   _UpperCAmelCase   )
																						__magic_name__					:				List[str]				=   {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

																						__magic_name__					:				int				=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,   _UpperCAmelCase   )
																						__magic_name__					:				int				=   fx_state

																						with torch.no_grad():
																												__magic_name__					:				Optional[Any]				=   pt_model(**_UpperCAmelCase   ).to_tuple()

																						__magic_name__					:				Tuple				=   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   )
																												__magic_name__					:				Dict				=   model_class.from_pretrained(_UpperCAmelCase  ,   from_pt=_UpperCAmelCase   )

																						__magic_name__					:				List[Any]				=   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   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				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
																						__magic_name__					:				Union[str, Any]				=   self._prepare_for_class(_UpperCAmelCase  ,   _UpperCAmelCase   )
																						__magic_name__					:				Dict				=   {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

																						__magic_name__					:				List[str]				=   pt_model_class(_UpperCAmelCase   ).eval()
																						__magic_name__					:				Optional[int]				=   model_class(_UpperCAmelCase  ,   dtype=jnp.floataa   )

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

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

																						with torch.no_grad():
																												__magic_name__					:				Tuple				=   pt_model(**_UpperCAmelCase   ).to_tuple()

																						__magic_name__					:				Optional[int]				=   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   )
																												__magic_name__					:				Tuple				=   pt_model_class.from_pretrained(_UpperCAmelCase  ,   from_flax=_UpperCAmelCase   )

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





				@slow
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for model_class_name in self.all_model_classes:
																__magic_name__					:				List[str]				=   model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'   )
																__magic_name__					:				Any				=   model(np.ones((1, 1)   )   )
																self.assertIsNotNone(_UpperCAmelCase   )






from __future__ import annotations

from typing import Any
def 			__SCREAMING_SNAKE_CASE							(					*lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if all(_check_not_integer(lowercase__					) for m in matrix_s					):
												for i in matrix_s[1:]:
																		_verify_matrix_sizes(matrix_s[0]   ,    lowercase__					)
												return [[sum(lowercase__					) for t in zip(*lowercase__					)] for m in zip(*lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if (
						    _check_not_integer(lowercase__					)
						    and _check_not_integer(lowercase__					)
						    and _verify_matrix_sizes(lowercase__   ,    lowercase__					)
						):
												return [[i - j for i, j in zip(*lowercase__					)] for m in zip(lowercase__   ,    lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int | float					)							->	list[list[float]]:
						return [[x * n for x in row] for row in matrix]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if _check_not_integer(lowercase__					) and _check_not_integer(lowercase__					):
												__magic_name__		,			__magic_name__					:				str				=   _verify_matrix_sizes(lowercase__   ,    lowercase__					)

						if cols[0] != rows[1]:
												__magic_name__					:				Union[str, Any]				=   (
												    'Cannot multiply matrix of dimensions '
												    f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
												)
												raise ValueError(lowercase__					)
						return [
						    [sum(m * n for m, n in zip(lowercase__   ,    lowercase__					)					) for j in zip(*lowercase__					)] for i in matrix_a
						]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				Tuple				=   int(lowercase__					)
						return [[int(row == column					) for column in range(lowercase__					)] for row in range(lowercase__					)]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:bool = True					)							->	list[list[int]] | map[list[int]]:
						if _check_not_integer(lowercase__					):
												if return_map:
																		return map(lowercase__   ,    zip(*lowercase__					)					)
												else:
																		return list(map(lowercase__   ,    zip(*lowercase__					)					)					)
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int   ,    lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				int				=   matrix[:row] + matrix[row + 1 :]
						return [row[:column] + row[column + 1 :] for row in minor]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	Any:
						if len(lowercase__					) == 1:
												return matrix[0][0]

						return sum(
						    x * determinant(minor(lowercase__   ,    0   ,    lowercase__					)					) * (-1) ** i
						    for i, x in enumerate(matrix[0]					)					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	list[list[float]] | None:
						__magic_name__					:				Tuple				=   determinant(lowercase__					)
						if det == 0:
												return None

						__magic_name__					:				Optional[int]				=   [
						    [determinant(minor(lowercase__   ,    lowercase__   ,    lowercase__					)					) for j in range(len(lowercase__					)					)]
						    for i in range(len(lowercase__					)					)
						]

						__magic_name__					:				Optional[Any]				=   [
						    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]					)]
						    for row in range(len(lowercase__					)					)
						]
						__magic_name__					:				Optional[Any]				=   list(transpose(lowercase__					)					)
						return scalar_multiply(lowercase__   ,    1 / det					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	bool:
						return not isinstance(lowercase__   ,    lowercase__					) and not isinstance(matrix[0]   ,    lowercase__					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	tuple[int, int]:
						return len(lowercase__					), len(matrix[0]					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	tuple[tuple[int, int], tuple[int, int]]:
						__magic_name__					:				str				=   _shape(lowercase__					) + _shape(lowercase__					)
						if shape[0] != shape[3] or shape[1] != shape[2]:
												__magic_name__					:				Optional[Any]				=   (
												    'operands could not be broadcast together with shape '
												    f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
												)
												raise ValueError(lowercase__					)
						return (shape[0], shape[2]), (shape[1], shape[3])
def 			__SCREAMING_SNAKE_CASE							(					)							->	None:
						__magic_name__					:				Dict				=   [[1_2, 1_0], [3, 9]]
						__magic_name__					:				Optional[int]				=   [[3, 4], [7, 4]]
						__magic_name__					:				Optional[Any]				=   [[1_1, 1_2, 1_3, 1_4], [2_1, 2_2, 2_3, 2_4], [3_1, 3_2, 3_3, 3_4], [4_1, 4_2, 4_3, 4_4]]
						__magic_name__					:				List[str]				=   [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
						print(f"Add Operation, {add(lowercase__   ,    lowercase__					) = } \n"					)
						print(
						    f"Multiply Operation, {multiply(lowercase__   ,    lowercase__					) = } \n"   ,    )
						print(f"Identity: {identity(5					)}\n"					)
						print(f"Minor of {matrix_c} = {minor(lowercase__   ,    1   ,    2					)} \n"					)
						print(f"Determinant of {matrix_b} = {determinant(lowercase__					)} \n"					)
						print(f"Inverse of {matrix_d} = {inverse(lowercase__					)}\n"					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()


