


"""simple docstring"""







import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
       import numpy as np

       from transformers.modeling_flax_pytorch_utils import (
           convert_pytorch_state_dict_to_flax,
           load_flax_weights_in_pytorch_model,
       )
       from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
       import torch






@require_flax
class    __A       :






 """simple docstring"""




 def __init__(	self				,					__A				,					__A=14				,					__A=7				,					__A=True				,					__A=True				,					__A=True				,					__A=99				,					__A=32				,					__A=5				,					__A=4				,					__A=37				,					__A="gelu"				,					__A=0.1				,					__A=0.1				,					__A=512				,					__A=0.02				,					__A=None				,					)      ->  List[str]:
     a					=parent
     a					=batch_size
     a					=seq_length
     a					=is_training
     a					=use_input_mask
     a					=use_labels
     a					=vocab_size
     a					=d_model
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=ffn_dim
     a					=activation_function
     a					=activation_dropout
     a					=attention_dropout
     a					=max_position_embeddings
     a					=initializer_range
     a					=None
     a					=0
     a					=2
     a					=1


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     a					=np.clip(ids_tensor([self.batch_size, self.seq_length]				,					self.vocab_size		)				,					3				,					self.vocab_size		)

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

     a					=XGLMConfig(
         vocab_size=self.vocab_size				,					d_model=self.hidden_size				,					num_layers=self.num_hidden_layers				,					attention_heads=self.num_attention_heads				,					ffn_dim=self.ffn_dim				,					activation_function=self.activation_function				,					activation_dropout=self.activation_dropout				,					attention_dropout=self.attention_dropout				,					max_position_embeddings=self.max_position_embeddings				,					initializer_range=self.initializer_range				,					use_cache=__A				,					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    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     a					=self.prepare_config_and_inputs()
     a	,   a	,   a					=config_and_inputs
     a					={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
     a	,   a	,   a					=self.prepare_config_and_inputs()

     a					=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
     a					=ids_tensor([self.batch_size, self.seq_length]				,					vocab_size=2		)

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  int:
     a					=20
     a					=model_class_name(__A		)

     a					=model.init_cache(input_ids.shape[0]				,					__A		)
     a					=jnp.ones((input_ids.shape[0], max_decoder_length)				,					dtype='''i4'''		)

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

     a					=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype='''i4'''		)
     a					=model(
         input_ids[:, -1:]				,					attention_mask=__A				,					past_key_values=outputs_cache.past_key_values				,					position_ids=__A				,					)

     a					=model(__A		)

     a					=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				,					__A				,					__A				,					__A				,					__A		)      ->  Optional[int]:
     a					=20
     a					=model_class_name(__A		)

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

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

     a					=model(
         input_ids[:, :-1]				,					attention_mask=__A				,					past_key_values=__A				,					position_ids=__A				,					)
     a					=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype='''i4'''		)
     a					=model(
         input_ids[:, -1:]				,					past_key_values=outputs_cache.past_key_values				,					attention_mask=__A				,					position_ids=__A				,					)

     a					=model(__A				,					attention_mask=__A		)
     a					=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_sentencepiece
@require_flax
class    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					unittest.TestCase    ):






 """simple docstring"""




 __lowerCAmelCase				      =						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
 __lowerCAmelCase				      =						(FlaxXGLMForCausalLM,) if is_flax_available() else ()


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     a					=FlaxXGLMModelTester(self		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     for model_class_name in self.all_model_classes:
         a	,   a	,   a					=self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(__A				,					__A				,					__A				,					__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     for model_class_name in self.all_model_classes:
         a	,   a	,   a					=self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             __A				,					__A				,					__A				,					__A		)


 @slow
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     a					=XGLMTokenizer.from_pretrained('''XGLM'''				,					padding_side='''left'''		)
     a					=tokenizer(['''Hello this is a long string''', '''Hey''']				,					return_tensors='''np'''				,					padding=__A				,					truncation=__A		)

     a					=FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''		)
     a					=1
     a					=False

     a					=jax.jit(model.generate		)

     a					=jit_generate(inputs['''input_ids''']				,					attention_mask=inputs['''attention_mask''']		).sequences

     a					=tokenizer.batch_decode(__A				,					skip_special_tokens=__A		)

     a					=[
         '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
         '''Hey, I\'m a newbie to the forum and I\'''',
     ]

     self.assertListEqual(__A				,					__A		)


 @is_pt_flax_cross_test
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
     a	,   a					=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
             a					=self._prepare_for_class(__A				,					__A		)
             a					={k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

             a	,   a					=pt_inputs['''input_ids'''].shape
             a					=np.random.randint(0				,					seq_length - 1				,					size=(batch_size,)		)
             for batch_idx, start_index in enumerate(__A		):
                 a					=0
                 a					=1
                 a					=0
                 a					=1
             a					=pt_model_class(__A		).eval()
             # Flax models don't use the `use_cache` option and cache is not returned as a default.
             # So we disable `use_cache` here for PyTorch model.
             a					=False
             a					=model_class(__A				,					dtype=jnp.floataa		)
             a					=convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,					__A		)
             a					=fx_state

             with torch.no_grad():
                 a					=pt_model(**__A		).to_tuple()

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

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

             a					=fx_model_loaded(**__A		).to_tuple()
             self.assertEqual(
                 len(__A		)				,					len(__A		)				,					'''Output lengths differ between Flax and PyTorch'''		)
             for fx_output_loaded, pt_output in zip(__A				,					__A		):
                 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[str]:
     a	,   a					=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
             a					=self._prepare_for_class(__A				,					__A		)
             a					={k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

             a					=pt_model_class(__A		).eval()
             a					=False
             a					=model_class(__A				,					dtype=jnp.floataa		)

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

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

             with torch.no_grad():
                 a					=pt_model(**__A		).to_tuple()

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

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

             with torch.no_grad():
                 a					=pt_model_loaded(**__A		).to_tuple()

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





 @slow
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
     for model_class_name in self.all_model_classes:
         a					=model_class_name.from_pretrained('''facebook/xglm-564M'''		)
         a					=model(np.ones((1, 1)		)		)
         self.assertIsNotNone(__A		)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_     :     Any    		=						{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_     :     List[str]    		=						[
           """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DinatForImageClassification""",
           """DinatModel""",
           """DinatPreTrainedModel""",
           """DinatBackbone""",
       ]

if TYPE_CHECKING:
       from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_     :     List[Any]    		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)