
'''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__       )








'''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__)






