






'''simple docstring'''





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 					lowercase							:


    '''simple docstring'''

    def __init__(       self  :  Dict					,  __lowerCamelCase  :  List[str]					,  __lowerCamelCase  :  int=14					,  __lowerCamelCase  :  Optional[Any]=7					,  __lowerCamelCase  :  Tuple=True					,  __lowerCamelCase  :  int=True					,  __lowerCamelCase  :  List[Any]=False					,  __lowerCamelCase  :  Optional[Any]=True					,  __lowerCamelCase  :  Any=99					,  __lowerCamelCase  :  Any=32					,  __lowerCamelCase  :  Dict=4					,  __lowerCamelCase  :  Optional[Any]=4					,  __lowerCamelCase  :  Optional[int]=[[["global", "local"], 2]]					,  __lowerCamelCase  :  Any=37					,  __lowerCamelCase  :  Dict="gelu"					,  __lowerCamelCase  :  str=0.1					,  __lowerCamelCase  :  Optional[Any]=0.1					,  __lowerCamelCase  :  Dict=512					,  __lowerCamelCase  :  Union[str, Any]=7					,  __lowerCamelCase  :  List[Any]=0.0_2					,  )				->				Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__	=  parent
     lowerCamelCase__	=  batch_size
     lowerCamelCase__	=  seq_length
     lowerCamelCase__	=  is_training
     lowerCamelCase__	=  use_input_mask
     lowerCamelCase__	=  use_token_type_ids
     lowerCamelCase__	=  use_labels
     lowerCamelCase__	=  vocab_size
     lowerCamelCase__	=  hidden_size
     lowerCamelCase__	=  num_hidden_layers
     lowerCamelCase__	=  num_attention_heads
     lowerCamelCase__	=  attention_types
     lowerCamelCase__	=  intermediate_size
     lowerCamelCase__	=  hidden_act
     lowerCamelCase__	=  hidden_dropout_prob
     lowerCamelCase__	=  attention_probs_dropout_prob
     lowerCamelCase__	=  max_position_embeddings
     lowerCamelCase__	=  window_size
     lowerCamelCase__	=  initializer_range
     lowerCamelCase__	=  None
     lowerCamelCase__	=  vocab_size - 1
     lowerCamelCase__	=  vocab_size - 1
     lowerCamelCase__	=  vocab_size - 1

    def  a__					(       self  :  Dict  )				->				List[Any]:

     '''simple docstring'''




     lowerCamelCase__	=  ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size  )

     lowerCamelCase__	=  None
     if self.use_input_mask:
      lowerCamelCase__	=  random_attention_mask([self.batch_size, self.seq_length]  )

     lowerCamelCase__	=  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=__lowerCamelCase					,  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  :  Optional[int]  )				->				int:

     '''simple docstring'''




     lowerCamelCase__	=  self.prepare_config_and_inputs()
     lowerCamelCase__	,						lowerCamelCase__	,						lowerCamelCase__	=  config_and_inputs
     lowerCamelCase__	=  {"input_ids": input_ids, "attention_mask": attention_mask}
     return config, inputs_dict

    def  a__					(       self  :  Dict					,  __lowerCamelCase  :  Tuple					,  __lowerCamelCase  :  Any					,  __lowerCamelCase  :  List[str]					,  __lowerCamelCase  :  str  )				->				int:

     '''simple docstring'''




     lowerCamelCase__	=  20
     lowerCamelCase__	=  model_class_name(__lowerCamelCase  )

     lowerCamelCase__	=  model.init_cache(input_ids.shape[0]					,  __lowerCamelCase  )
     lowerCamelCase__	=  jnp.ones((input_ids.shape[0], max_decoder_length)					,  dtype="i4"  )

     lowerCamelCase__	=  jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1  )[None, :]					,  (input_ids.shape[0], input_ids.shape[-1] - 1)  )
     lowerCamelCase__	=  model(
         input_ids[:, :-1]					,  attention_mask=__lowerCamelCase					,  past_key_values=__lowerCamelCase					,  position_ids=__lowerCamelCase					,  )

     lowerCamelCase__	=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,  dtype="i4"  )
     lowerCamelCase__	=  model(
         input_ids[:, -1:]					,  attention_mask=__lowerCamelCase					,  past_key_values=outputs_cache.past_key_values					,  position_ids=__lowerCamelCase					,  )

     lowerCamelCase__	=  model(__lowerCamelCase  )

     lowerCamelCase__	=  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  :  Optional[int]					,  __lowerCamelCase  :  Dict					,  __lowerCamelCase  :  Union[str, Any]					,  __lowerCamelCase  :  Optional[int]					,  __lowerCamelCase  :  str  )				->				List[Any]:

     '''simple docstring'''




     lowerCamelCase__	=  20
     lowerCamelCase__	=  model_class_name(__lowerCamelCase  )

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

     lowerCamelCase__	=  model.init_cache(input_ids.shape[0]					,  __lowerCamelCase  )
     lowerCamelCase__	=  jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1  )[None, :]					,  (input_ids.shape[0], input_ids.shape[-1] - 1)  )

     lowerCamelCase__	=  model(
         input_ids[:, :-1]					,  attention_mask=__lowerCamelCase					,  past_key_values=__lowerCamelCase					,  position_ids=__lowerCamelCase					,  )
     lowerCamelCase__	=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,  dtype="i4"  )
     lowerCamelCase__	=  model(
         input_ids[:, -1:]					,  past_key_values=outputs_cache.past_key_values					,  attention_mask=__lowerCamelCase					,  position_ids=__lowerCamelCase					,  )

     lowerCamelCase__	=  model(__lowerCamelCase					,  attention_mask=__lowerCamelCase  )

     lowerCamelCase__	=  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 					lowercase							(				_lowerCamelCase			,							_lowerCamelCase			,							unittest.TestCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
    lowerCAmelCase__   					=				(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

    def  a__					(       self  :  Any  )				->				Tuple:

     '''simple docstring'''




     lowerCamelCase__	=  FlaxGPTNeoModelTester(self  )

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

     '''simple docstring'''




     for model_class_name in self.all_model_classes:
      lowerCamelCase__	,						lowerCamelCase__	,						lowerCamelCase__	=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  )

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

     '''simple docstring'''




     for model_class_name in self.all_model_classes:
      lowerCamelCase__	,						lowerCamelCase__	,						lowerCamelCase__	=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  )

    @slow
    def  a__					(       self  :  Optional[int]  )				->				Tuple:

     '''simple docstring'''




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

     lowerCamelCase__	=  FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"  )
     lowerCamelCase__	=  False
     lowerCamelCase__	=  model.config.eos_token_id

     lowerCamelCase__	=  jax.jit(model.generate  )

     lowerCamelCase__	=  jit_generate(
         inputs["input_ids"]					,  attention_mask=inputs["attention_mask"]					,  pad_token_id=tokenizer.pad_token_id  ).sequences

     lowerCamelCase__	=  tokenizer.batch_decode(__lowerCamelCase					,  skip_special_tokens=__lowerCamelCase  )

     lowerCamelCase__	=  [
         "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(__lowerCamelCase					,  __lowerCamelCase  )

    @is_pt_flax_cross_test
    def  a__					(       self  :  int  )				->				Optional[int]:

     '''simple docstring'''




     lowerCamelCase__	,						lowerCamelCase__	=  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__	=  self._prepare_for_class(__lowerCamelCase					,  __lowerCamelCase  )
       lowerCamelCase__	=  {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       lowerCamelCase__	=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
       lowerCamelCase__	=  getattr(__lowerCamelCase					,  __lowerCamelCase  )

       lowerCamelCase__	,						lowerCamelCase__	=  pt_inputs["input_ids"].shape
       lowerCamelCase__	=  np.random.randint(0					,  seq_length - 1					,  size=(batch_size,)  )
       for batch_idx, start_index in enumerate(__lowerCamelCase  ):
        lowerCamelCase__	=  0
        lowerCamelCase__	=  1
        lowerCamelCase__	=  0
        lowerCamelCase__	=  1
       lowerCamelCase__	=  pt_model_class(__lowerCamelCase  ).eval()
       lowerCamelCase__	=  model_class(__lowerCamelCase					,  dtype=jnp.floataa  )

       lowerCamelCase__	=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,  __lowerCamelCase  )
       lowerCamelCase__	=  fx_state

       with torch.no_grad():
        lowerCamelCase__	=  pt_model(**__lowerCamelCase  ).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
        pt_model.save_pretrained(__lowerCamelCase  )
        lowerCamelCase__	=  model_class.from_pretrained(__lowerCamelCase					,  from_pt=__lowerCamelCase  )

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

    @is_pt_flax_cross_test
    def  a__					(       self  :  str  )				->				Any:

     '''simple docstring'''




     lowerCamelCase__	,						lowerCamelCase__	=  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__	=  self._prepare_for_class(__lowerCamelCase					,  __lowerCamelCase  )
       lowerCamelCase__	=  {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       lowerCamelCase__	=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
       lowerCamelCase__	=  getattr(__lowerCamelCase					,  __lowerCamelCase  )

       lowerCamelCase__	=  pt_model_class(__lowerCamelCase  ).eval()
       lowerCamelCase__	=  model_class(__lowerCamelCase					,  dtype=jnp.floataa  )

       lowerCamelCase__	=  load_flax_weights_in_pytorch_model(__lowerCamelCase					,  fx_model.params  )
       lowerCamelCase__	,						lowerCamelCase__	=  pt_inputs["input_ids"].shape
       lowerCamelCase__	=  np.random.randint(0					,  seq_length - 1					,  size=(batch_size,)  )
       for batch_idx, start_index in enumerate(__lowerCamelCase  ):
        lowerCamelCase__	=  0
        lowerCamelCase__	=  1
        lowerCamelCase__	=  0
        lowerCamelCase__	=  1

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

       with torch.no_grad():
        lowerCamelCase__	=  pt_model(**__lowerCamelCase  ).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
        fx_model.save_pretrained(__lowerCamelCase  )
        lowerCamelCase__	=  pt_model_class.from_pretrained(__lowerCamelCase					,  from_flax=__lowerCamelCase  )

       with torch.no_grad():
        lowerCamelCase__	=  pt_model_loaded(**__lowerCamelCase  ).to_tuple()

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




    @slow
    def  a__					(       self  :  List[Any]  )				->				Optional[Any]:

     '''simple docstring'''




     for model_class_name in self.all_model_classes:
      lowerCamelCase__	=  model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"  )
      lowerCamelCase__	=  model(np.ones((1, 1)  )  )
      self.assertIsNotNone(__lowerCamelCase  )









'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A				:				Optional[int]					=		logging.get_logger(__name__)

__A				:				Tuple					=		{
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class 					lowercase							(				_lowerCamelCase			,							_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				"convnext"

    def __init__(       self  :  Any					,  __lowerCamelCase  :  Dict=3					,  __lowerCamelCase  :  int=4					,  __lowerCamelCase  :  Any=4					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  str=None					,  __lowerCamelCase  :  Optional[int]="gelu"					,  __lowerCamelCase  :  List[str]=0.0_2					,  __lowerCamelCase  :  int=1E-12					,  __lowerCamelCase  :  Optional[Any]=1E-6					,  __lowerCamelCase  :  Any=0.0					,  __lowerCamelCase  :  Union[str, Any]=224					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  List[str]=None					,  **__lowerCamelCase  :  Tuple					,  )				->				Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     lowerCamelCase__	=  num_channels
     lowerCamelCase__	=  patch_size
     lowerCamelCase__	=  num_stages
     lowerCamelCase__	=  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     lowerCamelCase__	=  [3, 3, 9, 3] if depths is None else depths
     lowerCamelCase__	=  hidden_act
     lowerCamelCase__	=  initializer_range
     lowerCamelCase__	=  layer_norm_eps
     lowerCamelCase__	=  layer_scale_init_value
     lowerCamelCase__	=  drop_path_rate
     lowerCamelCase__	=  image_size
     lowerCamelCase__	=  ["stem"] + [f'''stage{idx}''' for idx in range(1					,  len(self.depths  ) + 1  )]
     lowerCamelCase__	,						lowerCamelCase__	=  get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase					,  out_indices=__lowerCamelCase					,  stage_names=self.stage_names  )
class 					lowercase							(				_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				version.parse("1.11"   )

    @property
    def  a__					(       self  :  List[Any]  )				->				Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]  )




    @property
    def  a__					(       self  :  Tuple  )				->				float:

     '''simple docstring'''




     return 1E-5

