'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
      import torch





class 					__magic_name__		:







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







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
          lowercase_      :    Optional[int]       		= ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size					)

          lowercase_      :    List[Any]       		= None
          if self.use_input_mask:
                 lowercase_      :    int       		= random_attention_mask([self.batch_size, self.seq_length]					)

          lowercase_      :    Dict       		= GPTJConfig(
              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       ,    rotary_dim=self.rotary_dim       ,    )

          return (config, input_ids, input_mask)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
          lowercase_      :    Union[str, Any]       		= self.prepare_config_and_inputs()
          lowercase_      :    Tuple       		= config_and_inputs
          lowercase_      :    Tuple       		= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
          return config, inputs_dict







   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Tuple       ,    lowercase_       :						int       ,    lowercase_       :						Tuple       ,    lowercase_       :						Optional[int]					):
          lowercase_      :    str       		= 20
          lowercase_      :    Optional[Any]       		= model_class_name(lowercase_					)

          lowercase_      :    int       		= model.init_cache(input_ids.shape[0]       ,    lowercase_					)
          lowercase_      :    Union[str, Any]       		= jnp.ones((input_ids.shape[0], max_decoder_length)       ,    dtype="""i4"""					)

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

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

          lowercase_      :    int       		= model(lowercase_					)

          lowercase_      :    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}'''					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						str       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						List[str]       ,    lowercase_       :						Optional[Any]					):
          lowercase_      :    int       		= 20
          lowercase_      :    List[Any]       		= model_class_name(lowercase_					)

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

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

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

          lowercase_      :    Tuple       		= model(lowercase_       ,    attention_mask=lowercase_					)

          lowercase_      :    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 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase,  unittest.TestCase):
   UpperCamelCase__       = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   UpperCamelCase__       = (FlaxGPTJForCausalLM,) if is_flax_available() else ()







   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
          lowercase_      :    Union[str, Any]       		= FlaxGPTJModelTester(self					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
          for model_class_name in self.all_model_classes:
                 lowercase_      :    str       		= self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
          for model_class_name in self.all_model_classes:
                 lowercase_      :    str       		= self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)







   @tooslow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
          lowercase_      :    Tuple       		= GPTaTokenizer.from_pretrained("""gpt2"""       ,    pad_token="""<|endoftext|>"""       ,    padding_side="""left"""					)
          lowercase_      :    List[str]       		= tokenizer(["""Hello this is a long string""", """Hey"""]       ,    return_tensors="""np"""       ,    padding=lowercase_       ,    truncation=lowercase_					)

          lowercase_      :    List[Any]       		= FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""					)
          lowercase_      :    Optional[Any]       		= False
          lowercase_      :    Optional[int]       		= model.config.eos_token_id

          lowercase_      :    Union[str, Any]       		= jax.jit(model.generate					)

          lowercase_      :    Any       		= jit_generate(
              inputs["""input_ids"""]       ,    attention_mask=inputs["""attention_mask"""]       ,    pad_token_id=tokenizer.pad_token_id					).sequences

          lowercase_      :    List[Any]       		= tokenizer.batch_decode(lowercase_       ,    skip_special_tokens=lowercase_					)

          lowercase_      :    List[str]       		= [
              """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(lowercase_       ,    lowercase_					)







   @is_pt_flax_cross_test
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
          lowercase_      :    Optional[int]       		= 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_      :    Dict       		= self._prepare_for_class(lowercase_       ,    lowercase_					)
                        lowercase_      :    List[Any]       		= {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

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

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

                        with torch.no_grad():
                               lowercase_      :    List[Any]       		= pt_model(**lowercase_					).to_tuple()

                        lowercase_      :    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:
                               pt_model.save_pretrained(lowercase_					)
                               lowercase_      :    Dict       		= model_class.from_pretrained(lowercase_       ,    from_pt=lowercase_					)

                        lowercase_      :    Any       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
          lowercase_      :    Optional[int]       		= 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_      :    Tuple       		= self._prepare_for_class(lowercase_       ,    lowercase_					)
                        lowercase_      :    Union[str, Any]       		= {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

                        lowercase_      :    str       		= 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_					)
                               lowercase_      :    Dict       		= pt_model_class.from_pretrained(lowercase_       ,    from_flax=lowercase_					)

                        with torch.no_grad():
                               lowercase_      :    str       		= 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					)







   @tooslow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
          for model_class_name in self.all_model_classes:
                 lowercase_      :    Any       		= model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""					)
                 lowercase_      :    Dict       		= model(np.ones((1, 1)					)					)
                 self.assertIsNotNone(lowercase_					)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





