code: 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     :			int					    =						logging.get_logger(__name__)
  
  a     :			str					    =						{
      """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						(    UpperCAmelCase__   ,     UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						'convnext'
  
  
  
  
      def __init__(						self					,	lowerCamelCase__=3					,	lowerCamelCase__=4					,	lowerCamelCase__=4					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.0_2					,	lowerCamelCase__=1E-12					,	lowerCamelCase__=1E-6					,	lowerCamelCase__=0.0					,	lowerCamelCase__=224					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	**lowerCamelCase__					,	):
         super().__init__(**lowerCamelCase__   )
  
         lowerCAmelCase_:       str         =  num_channels
         lowerCAmelCase_:       Optional[int]         =  patch_size
         lowerCAmelCase_:       Dict         =  num_stages
         lowerCAmelCase_:       Optional[Any]         =  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
         lowerCAmelCase_:       List[str]         =  [3, 3, 9, 3] if depths is None else depths
         lowerCAmelCase_:       str         =  hidden_act
         lowerCAmelCase_:       int         =  initializer_range
         lowerCAmelCase_:       Union[str, Any]         =  layer_norm_eps
         lowerCAmelCase_:       int         =  layer_scale_init_value
         lowerCAmelCase_:       Optional[int]         =  drop_path_rate
         lowerCAmelCase_:       List[str]         =  image_size
         lowerCAmelCase_:       Union[str, Any]         =  ["stem"] + [F'''stage{idx}''' for idx in range(1					,	len(self.depths   ) + 1   )]
         lowerCAmelCase_:       str         =  get_aligned_output_features_output_indices(
             out_features=lowerCamelCase__					,	out_indices=lowerCamelCase__					,	stage_names=self.stage_names   )
  
  
  
  
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							int        =						version.parse('1.11' )
  
  
  
  
      @property
      def     _a						(						self   ):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]   )
  
  
  
  
      @property
      def     _a						(						self   ):
         return 1E-5
style_context: 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 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=14					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	):
         lowerCAmelCase_:       int         =  parent
         lowerCAmelCase_:       Tuple         =  batch_size
         lowerCAmelCase_:       int         =  seq_length
         lowerCAmelCase_:       Any         =  is_training
         lowerCAmelCase_:       Any         =  use_input_mask
         lowerCAmelCase_:       Optional[Any]         =  use_token_type_ids
         lowerCAmelCase_:       str         =  use_labels
         lowerCAmelCase_:       Tuple         =  vocab_size
         lowerCAmelCase_:       List[Any]         =  hidden_size
         lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
         lowerCAmelCase_:       List[str]         =  num_attention_heads
         lowerCAmelCase_:       int         =  intermediate_size
         lowerCAmelCase_:       Tuple         =  hidden_act
         lowerCAmelCase_:       Dict         =  hidden_dropout_prob
         lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
         lowerCAmelCase_:       Any         =  max_position_embeddings
         lowerCAmelCase_:       Dict         =  initializer_range
         lowerCAmelCase_:       List[Any]         =  None
         lowerCAmelCase_:       List[Any]         =  vocab_size - 1
         lowerCAmelCase_:       Union[str, Any]         =  vocab_size - 1
         lowerCAmelCase_:       int         =  vocab_size - 1
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )
  
         lowerCAmelCase_:       Any         =  None
         if self.use_input_mask:
            lowerCAmelCase_:       Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]   )
  
         lowerCAmelCase_:       Union[str, 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=lowerCamelCase__					,	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   ):
         lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  config_and_inputs
         lowerCAmelCase_:       List[str]         =  {"input_ids": input_ids, "attention_mask": attention_mask}
         return config, inputs_dict
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
  
         lowerCAmelCase_:       List[str]         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
         lowerCAmelCase_:       Tuple         =  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					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[Any]         =  20
         lowerCAmelCase_:       Union[str, Any]         =  model_class_name(lowerCamelCase__   )
  
         lowerCAmelCase_:       List[str]         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
         lowerCAmelCase_:       Any         =  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"   )
  
         lowerCAmelCase_:       Union[str, Any]         =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
         lowerCAmelCase_:       Optional[Any]         =  model(
             input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       Any         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       Optional[int]         =  model(
             input_ids[:, -1:]					,	attention_mask=lowerCamelCase__					,	past_key_values=outputs_cache.past_key_values					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       List[Any]         =  model(lowerCamelCase__   )
  
         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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Union[str, Any]         =  20
         lowerCAmelCase_:       List[str]         =  model_class_name(lowerCamelCase__   )
  
         lowerCAmelCase_:       Any         =  jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]					,	axis=-1					,	)
  
         lowerCAmelCase_:       str         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
         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=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
         lowerCAmelCase_:       Dict         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       Dict         =  model(
             input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       Union[str, Any]         =  model(lowerCamelCase__					,	attention_mask=lowerCamelCase__   )
  
         lowerCAmelCase_:       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 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Optional[int]        =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Any         =  FlaxGPTaModelTester(self   )
  
  
  
  
      def     _a						(						self   ):
         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(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  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   ):
         lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained("gpt2"					,	pad_token="</s>"					,	padding_side="left"   )
         lowerCAmelCase_:       Optional[int]         =  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=lowerCamelCase__					,	truncation=lowerCamelCase__   )
  
         lowerCAmelCase_:       List[Any]         =  FlaxGPTaLMHeadModel.from_pretrained("gpt2"   )
         lowerCAmelCase_:       List[str]         =  False
         lowerCAmelCase_:       int         =  model.config.eos_token_id
  
         lowerCAmelCase_:       Optional[int]         =  jax.jit(model.generate   )
  
         lowerCAmelCase_:       Any         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences
  
         lowerCAmelCase_:       Any         =  tokenizer.batch_decode(lowerCamelCase__					,	skip_special_tokens=lowerCamelCase__   )
  
         lowerCAmelCase_:       Dict         =  [
             "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(lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      @is_pt_flax_cross_test
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, 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_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
               lowerCAmelCase_:       str         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               lowerCAmelCase_:       Dict         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
               lowerCAmelCase_:       Union[str, Any]         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )
  
               lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  pt_inputs["input_ids"].shape
               lowerCAmelCase_:       str         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
               for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                  lowerCAmelCase_:       List[str]         =  0
                  lowerCAmelCase_:       int         =  1
                  lowerCAmelCase_:       List[Any]         =  0
                  lowerCAmelCase_:       List[Any]         =  1
               lowerCAmelCase_:       Tuple         =  pt_model_class(lowerCamelCase__   ).eval()
               lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )
  
               lowerCAmelCase_:       int         =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	lowerCamelCase__   )
               lowerCAmelCase_:       Optional[int]         =  fx_state
  
               with torch.no_grad():
                  lowerCAmelCase_:       Union[str, Any]         =  pt_model(**lowerCamelCase__   ).to_tuple()
  
               lowerCAmelCase_:       List[str]         =  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_:       Dict         =  model_class.from_pretrained(lowerCamelCase__					,	from_pt=lowerCamelCase__   )
  
               lowerCAmelCase_:       List[Any]         =  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   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  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_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
               lowerCAmelCase_:       int         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               lowerCAmelCase_:       List[str]         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
               lowerCAmelCase_:       Dict         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )
  
               lowerCAmelCase_:       Dict         =  pt_model_class(lowerCamelCase__   ).eval()
               lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )
  
               lowerCAmelCase_:       Optional[Any]         =  load_flax_weights_in_pytorch_model(lowerCamelCase__					,	fx_model.params   )
               lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  pt_inputs["input_ids"].shape
               lowerCAmelCase_:       Optional[Any]         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
               for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                  lowerCAmelCase_:       Dict         =  0
                  lowerCAmelCase_:       List[Any]         =  1
                  lowerCAmelCase_:       Dict         =  0
                  lowerCAmelCase_:       Optional[int]         =  1
  
               # make sure weights are tied in PyTorch
               pt_model.tie_weights()
  
               with torch.no_grad():
                  lowerCAmelCase_:       Any         =  pt_model(**lowerCamelCase__   ).to_tuple()
  
               lowerCAmelCase_:       Union[str, Any]         =  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_:       str         =  pt_model_class.from_pretrained(lowerCamelCase__					,	from_flax=lowerCamelCase__   )
  
               with torch.no_grad():
                  lowerCAmelCase_:       int         =  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   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("gpt2"					,	from_pt=lowerCamelCase__   )
            lowerCAmelCase_:       int         =  model(np.ones((1, 1)   )   )
            self.assertIsNotNone(lowerCamelCase__   )