code: 
  '''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       ,     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__=0.02       ,     )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       seq_length
  									lowercase__							 =       is_training
  									lowercase__							 =       use_input_mask
  									lowercase__							 =       use_token_type_ids
  									lowercase__							 =       use_labels
  									lowercase__							 =       vocab_size
  									lowercase__							 =       hidden_size
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       intermediate_size
  									lowercase__							 =       hidden_act
  									lowercase__							 =       hidden_dropout_prob
  									lowercase__							 =       attention_probs_dropout_prob
  									lowercase__							 =       max_position_embeddings
  									lowercase__							 =       initializer_range
  									lowercase__							 =       None
  									lowercase__							 =       vocab_size - 1
  									lowercase__							 =       vocab_size - 1
  									lowercase__							 =       vocab_size - 1
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )
  
  									lowercase__							 =       None
  									if self.use_input_mask:
  															lowercase__							 =       random_attention_mask([self.batch_size, self.seq_length]       )
  
  									lowercase__							 =       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       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									lowercase__ ,						lowercase__ ,						lowercase__							 =       config_and_inputs
  									lowercase__							 =       {"""input_ids""": input_ids, """attention_mask""": attention_mask}
  									return config, inputs_dict
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__ ,						lowercase__							 =       self.prepare_config_and_inputs()
  
  									lowercase__							 =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
  									lowercase__							 =       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__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       20
  									lowercase__							 =       model_class_name(lowerCamelCase__       )
  
  									lowercase__							 =       model.init_cache(input_ids.shape[0]       ,     lowerCamelCase__       )
  									lowercase__							 =       jnp.ones((input_ids.shape[0], max_decoder_length)       ,     dtype="""i4"""       )
  
  									lowercase__							 =       jnp.broadcast_to(
  									    jnp.arange(input_ids.shape[-1] - 1       )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )
  									lowercase__							 =       model(
  									    input_ids[:, :-1]       ,     attention_mask=lowerCamelCase__       ,     past_key_values=lowerCamelCase__       ,     position_ids=lowerCamelCase__       ,     )
  
  									lowercase__							 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="""i4"""       )
  									lowercase__							 =       model(
  									    input_ids[:, -1:]       ,     attention_mask=lowerCamelCase__       ,     past_key_values=outputs_cache.past_key_values       ,     position_ids=lowerCamelCase__       ,     )
  
  									lowercase__							 =       model(lowerCamelCase__       )
  
  									lowercase__							 =       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__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       20
  									lowercase__							 =       model_class_name(lowerCamelCase__       )
  
  									lowercase__							 =       jnp.concatenate(
  									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]       ,     axis=-1       ,     )
  
  									lowercase__							 =       model.init_cache(input_ids.shape[0]       ,     lowerCamelCase__       )
  									lowercase__							 =       jnp.broadcast_to(
  									    jnp.arange(input_ids.shape[-1] - 1       )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )
  
  									lowercase__							 =       model(
  									    input_ids[:, :-1]       ,     attention_mask=lowerCamelCase__       ,     past_key_values=lowerCamelCase__       ,     position_ids=lowerCamelCase__       ,     )
  									lowercase__							 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="""i4"""       )
  									lowercase__							 =       model(
  									    input_ids[:, -1:]       ,     past_key_values=outputs_cache.past_key_values       ,     attention_mask=lowerCamelCase__       ,     position_ids=lowerCamelCase__       ,     )
  
  									lowercase__							 =       model(lowerCamelCase__       ,     attention_mask=lowerCamelCase__       )
  
  									lowercase__							 =       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							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							int       		=  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  			lowerCamelCase							:							Optional[Any]       		=  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       FlaxGPTaModelTester(self       )
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_class_name in self.all_model_classes:
  															lowercase__ ,						lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  															self.model_tester.check_use_cache_forward(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_class_name in self.all_model_classes:
  															lowercase__ ,						lowercase__ ,						lowercase__							 =       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       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       GPTaTokenizer.from_pretrained("""gpt2"""       ,     pad_token="""</s>"""       ,     padding_side="""left"""       )
  									lowercase__							 =       tokenizer(["""Hello this is a long string""", """Hey"""]       ,     return_tensors="""np"""       ,     padding=lowerCamelCase__       ,     truncation=lowerCamelCase__       )
  
  									lowercase__							 =       FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""       )
  									lowercase__							 =       False
  									lowercase__							 =       model.config.eos_token_id
  
  									lowercase__							 =       jax.jit(model.generate       )
  
  									lowercase__							 =       jit_generate(inputs["""input_ids"""]       ,     attention_mask=inputs["""attention_mask"""]       ).sequences
  
  									lowercase__							 =       tokenizer.batch_decode(lowerCamelCase__       ,     skip_special_tokens=lowerCamelCase__       )
  
  									lowercase__							 =       [
  									    """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       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       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
  																					lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}
  
  																					# load corresponding PyTorch class
  																					lowercase__							 =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  
  																					lowercase__ ,						lowercase__							 =       pt_inputs["""input_ids"""].shape
  																					lowercase__							 =       np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)       )
  																					for batch_idx, start_index in enumerate(lowerCamelCase__       ):
  																											lowercase__							 =       0
  																											lowercase__							 =       1
  																											lowercase__							 =       0
  																											lowercase__							 =       1
  																					lowercase__							 =       pt_model_class(lowerCamelCase__       ).eval()
  																					lowercase__							 =       model_class(lowerCamelCase__       ,     dtype=jnp.floataa       )
  
  																					lowercase__							 =       convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,     lowerCamelCase__       )
  																					lowercase__							 =       fx_state
  
  																					with torch.no_grad():
  																											lowercase__							 =       pt_model(**lowerCamelCase__       ).to_tuple()
  
  																					lowercase__							 =       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__       )
  																											lowercase__							 =       model_class.from_pretrained(lowerCamelCase__       ,     from_pt=lowerCamelCase__       )
  
  																					lowercase__							 =       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       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       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
  																					lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}
  
  																					# load corresponding PyTorch class
  																					lowercase__							 =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  
  																					lowercase__							 =       pt_model_class(lowerCamelCase__       ).eval()
  																					lowercase__							 =       model_class(lowerCamelCase__       ,     dtype=jnp.floataa       )
  
  																					lowercase__							 =       load_flax_weights_in_pytorch_model(lowerCamelCase__       ,     fx_model.params       )
  																					lowercase__ ,						lowercase__							 =       pt_inputs["""input_ids"""].shape
  																					lowercase__							 =       np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)       )
  																					for batch_idx, start_index in enumerate(lowerCamelCase__       ):
  																											lowercase__							 =       0
  																											lowercase__							 =       1
  																											lowercase__							 =       0
  																											lowercase__							 =       1
  
  																					# make sure weights are tied in PyTorch
  																					pt_model.tie_weights()
  
  																					with torch.no_grad():
  																											lowercase__							 =       pt_model(**lowerCamelCase__       ).to_tuple()
  
  																					lowercase__							 =       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__       )
  																											lowercase__							 =       pt_model_class.from_pretrained(lowerCamelCase__       ,     from_flax=lowerCamelCase__       )
  
  																					with torch.no_grad():
  																											lowercase__							 =       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]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_class_name in self.all_model_classes:
  															lowercase__							 =       model_class_name.from_pretrained("""gpt2"""       ,     from_pt=lowerCamelCase__       )
  															lowercase__							 =       model(np.ones((1, 1)       )       )
  															self.assertIsNotNone(lowerCamelCase__       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  