






"""simple docstring"""


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
   import torch






class __a   :



  def __init__(self  :		List[str]				,			lowercase_  :		Optional[int]				,			lowercase_  :		Optional[int]=14				,			lowercase_  :		List[Any]=7				,			lowercase_  :		Dict=True				,			lowercase_  :		Tuple=True				,			lowercase_  :		Any=False				,			lowercase_  :		List[str]=True				,			lowercase_  :		List[Any]=99				,			lowercase_  :		Any=32				,			lowercase_  :		Tuple=5				,			lowercase_  :		str=4				,			lowercase_  :		Optional[Any]=37				,			lowercase_  :		str="gelu"				,			lowercase_  :		Dict=0.1				,			lowercase_  :		Any=0.1				,			lowercase_  :		List[str]=512				,			lowercase_  :		Union[str, Any]=0.02				,			)		->							Any:
   __A	:       Tuple       =	parent
   __A	:       Union[str, Any]       =	batch_size
   __A	:       Union[str, Any]       =	seq_length
   __A	:       List[str]       =	is_training
   __A	:       Dict       =	use_input_mask
   __A	:       List[str]       =	use_token_type_ids
   __A	:       List[Any]       =	use_labels
   __A	:       int       =	vocab_size
   __A	:       Union[str, Any]       =	hidden_size
   __A	:       Union[str, Any]       =	num_hidden_layers
   __A	:       int       =	num_attention_heads
   __A	:       Optional[Any]       =	intermediate_size
   __A	:       Optional[Any]       =	hidden_act
   __A	:       Any       =	hidden_dropout_prob
   __A	:       Any       =	attention_probs_dropout_prob
   __A	:       int       =	max_position_embeddings
   __A	:       Union[str, Any]       =	initializer_range
   __A	:       List[Any]       =	None
   __A	:       Tuple       =	vocab_size - 1
   __A	:       Any       =	vocab_size - 1
   __A	:       str       =	vocab_size - 1



  def 						A_				(self  :		Dict   )		->							Tuple:
   __A	:       Tuple       =	ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

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

   __A	:       Any       =	GPTaConfig(
       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=lowercase_				,			bos_token_id=self.bos_token_id				,			eos_token_id=self.eos_token_id				,			pad_token_id=self.pad_token_id				,			)

   return (config, input_ids, input_mask)



  def 						A_				(self  :		int   )		->							Any:
   __A	:       Any       =	self.prepare_config_and_inputs()
   __A	:       Union[str, Any]       =	config_and_inputs
   __A	:       Any       =	{'input_ids': input_ids, 'attention_mask': attention_mask}
   return config, inputs_dict



  def 						A_				(self  :		Union[str, Any]   )		->							Tuple:
   __A	:       List[Any]       =	self.prepare_config_and_inputs()

   __A	:       List[Any]       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
   __A	:       Union[str, Any]       =	ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2   )

   return (
       config,
       input_ids,
       attention_mask,
       encoder_hidden_states,
       encoder_attention_mask,
   )



  def 						A_				(self  :		Dict				,			lowercase_  :		List[Any]				,			lowercase_  :		str				,			lowercase_  :		List[Any]				,			lowercase_  :		Tuple   )		->							Optional[Any]:
   __A	:       str       =	20
   __A	:       str       =	model_class_name(lowercase_   )

   __A	:       List[str]       =	model.init_cache(input_ids.shape[0]				,			lowercase_   )
   __A	:       Any       =	jnp.ones((input_ids.shape[0], max_decoder_length)				,			dtype='i4'   )

   __A	:       Union[str, Any]       =	jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1   )[None, :]				,			(input_ids.shape[0], input_ids.shape[-1] - 1)   )
   __A	:       Optional[int]       =	model(
       input_ids[:, :-1]				,			attention_mask=lowercase_				,			past_key_values=lowercase_				,			position_ids=lowercase_				,			)

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

   __A	:       Union[str, Any]       =	model(lowercase_   )

   __A	:       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}"   )



  def 						A_				(self  :		str				,			lowercase_  :		str				,			lowercase_  :		Optional[int]				,			lowercase_  :		int				,			lowercase_  :		List[Any]   )		->							List[str]:
   __A	:       Dict       =	20
   __A	:       Union[str, Any]       =	model_class_name(lowercase_   )

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

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

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

   __A	:       str       =	model(lowercase_				,			attention_mask=lowercase_   )

   __A	:       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 __a   (   snake_case_ ,snake_case_ ,unittest.TestCase				):
  __lowercase    :    Union[str, Any]						=					(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  __lowercase    :    Dict						=					(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



  def 						A_				(self  :		Optional[Any]   )		->							Optional[int]:
   __A	:       str       =	FlaxGPTaModelTester(self   )



  def 						A_				(self  :		Optional[Any]   )		->							Dict:
   for model_class_name in self.all_model_classes:
    __A	:       Any       =	self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )



  def 						A_				(self  :		str   )		->							Union[str, Any]:
   for model_class_name in self.all_model_classes:
    __A	:       List[str]       =	self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward_with_attn_mask(
        lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )



  @slow
  def 						A_				(self  :		Optional[Any]   )		->							str:
   __A	:       Union[str, Any]       =	GPTaTokenizer.from_pretrained('gpt2'				,			pad_token='</s>'				,			padding_side='left'   )
   __A	:       List[str]       =	tokenizer(['Hello this is a long string', 'Hey']				,			return_tensors='np'				,			padding=lowercase_				,			truncation=lowercase_   )

   __A	:       Dict       =	FlaxGPTaLMHeadModel.from_pretrained('gpt2'   )
   __A	:       List[Any]       =	False
   __A	:       Optional[int]       =	model.config.eos_token_id

   __A	:       List[str]       =	jax.jit(model.generate   )

   __A	:       Tuple       =	jit_generate(inputs['input_ids']				,			attention_mask=inputs['attention_mask']   ).sequences

   __A	:       Union[str, Any]       =	tokenizer.batch_decode(lowercase_				,			skip_special_tokens=lowercase_   )

   __A	:       Optional[Any]       =	[
       'Hello this is a long string of words. I\'m going to start with the first one.\n',
       'Hey, I\'m not sure if I\'m going to be able to do',
   ]

   self.assertListEqual(lowercase_				,			lowercase_   )



  @is_pt_flax_cross_test
  def 						A_				(self  :		List[str]   )		->							str:
   __A	:       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
     __A	:       Union[str, Any]       =	self._prepare_for_class(lowercase_				,			lowercase_   )
     __A	:       Union[str, Any]       =	{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

     __A	:       Optional[Any]       =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,			lowercase_   )
     __A	:       List[Any]       =	fx_state

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

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

     with tempfile.TemporaryDirectory() as tmpdirname:
      pt_model.save_pretrained(lowercase_   )
      __A	:       List[str]       =	model_class.from_pretrained(lowercase_				,			from_pt=lowercase_   )

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



  @is_pt_flax_cross_test
  def 						A_				(self  :		str   )		->							List[Any]:
   __A	:       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
     __A	:       Dict       =	self._prepare_for_class(lowercase_				,			lowercase_   )
     __A	:       str       =	{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

     __A	:       Union[str, Any]       =	fx_model(**lowercase_   ).to_tuple()
     self.assertEqual(len(lowercase_   )				,			len(lowercase_   )				,			'Output lengths differ between Flax and PyTorch'   )
     for fx_output, pt_output in zip(lowercase_				,			lowercase_   ):
      self.assert_almost_equals(fx_output[:, -1]				,			pt_output[:, -1].numpy()				,			4E-2   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      fx_model.save_pretrained(lowercase_   )
      __A	:       int       =	pt_model_class.from_pretrained(lowercase_				,			from_flax=lowercase_   )

     with torch.no_grad():
      __A	:       Optional[Any]       =	pt_model_loaded(**lowercase_   ).to_tuple()

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



  @slow
  def 						A_				(self  :		List[Any]   )		->							Tuple:
   for model_class_name in self.all_model_classes:
    __A	:       Optional[int]       =	model_class_name.from_pretrained('gpt2'				,			from_pt=lowercase_   )
    __A	:       Tuple       =	model(np.ones((1, 1)   )   )
    self.assertIsNotNone(lowercase_   )













"""simple docstring"""


from __future__ import annotations

from typing import Any






class __a   :



  def __init__(self  :		Optional[Any]				,			lowercase_  :		Any				,			lowercase_  :		Any   )		->							None:  # noqa: A002
   __A	:       Optional[Any]       =	item
   __A	:       Union[str, Any]       =	next






class __a   :



  def __init__(self  :		Tuple   )		->							None:
   __A	:       Node | None       =	None
   __A	:       Dict       =	0



  def 						A_				(self  :		str				,			lowercase_  :		Any   )		->							None:
   __A	:       Dict       =	Node(lowercase_				,			self.head   )
   self.size += 1



  def 						A_				(self  :		Optional[int]   )		->							Any:
   # Switched 'self.is_empty()' to 'self.head is None'
   # because mypy was considering the possibility that 'self.head'
   # can be None in below else part and giving error
   if self.head is None:
    return None
   else:
    __A	:       Dict       =	self.head.item
    __A	:       List[str]       =	self.head.next
    self.size -= 1
    return item



  def 						A_				(self  :		List[str]   )		->							bool:
   return self.head is None



  def __str__(self  :		Any   )		->							str:
   if self.is_empty():
    return ""
   else:
    __A	:       List[str]       =	self.head
    __A	:       Union[str, Any]       =	''
    __A	:       list[str]       =	[]
    while iterate:
     item_list.append(str(iterate.item   )   )
     __A	:       List[str]       =	iterate.next

    __A	:       str       =	' --> '.join(lowercase_   )

    return item_str



  def __len__(self  :		Union[str, Any]   )		->							int:
   return self.size





