



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        __snake_case		:




							def __init__( self  : int       ,       A_  : str       ,       A_  : Optional[Any]=1_4       ,       A_  : int=7       ,       A_  : List[Any]=True       ,       A_  : str=True       ,       A_  : Union[str, Any]=True       ,       A_  : List[Any]=9_9       ,       A_  : int=3_2       ,       A_  : Optional[int]=5       ,       A_  : Optional[Any]=4       ,       A_  : Optional[Any]=3_7       ,       A_  : Union[str, Any]="gelu"       ,       A_  : List[str]=0.1       ,       A_  : int=0.1       ,       A_  : Optional[int]=5_1_2       ,       A_  : List[str]=0.02       ,       A_  : Any=None       ,       ):
									lowerCAmelCase_    :  int             =				parent
									lowerCAmelCase_    :  List[Any]             =				batch_size
									lowerCAmelCase_    :  Optional[Any]             =				seq_length
									lowerCAmelCase_    :  Union[str, Any]             =				is_training
									lowerCAmelCase_    :  Tuple             =				use_input_mask
									lowerCAmelCase_    :  List[Any]             =				use_labels
									lowerCAmelCase_    :  Optional[int]             =				vocab_size
									lowerCAmelCase_    :  Tuple             =				d_model
									lowerCAmelCase_    :  str             =				num_hidden_layers
									lowerCAmelCase_    :  Tuple             =				num_attention_heads
									lowerCAmelCase_    :  Dict             =				ffn_dim
									lowerCAmelCase_    :  int             =				activation_function
									lowerCAmelCase_    :  Optional[Any]             =				activation_dropout
									lowerCAmelCase_    :  str             =				attention_dropout
									lowerCAmelCase_    :  Optional[int]             =				max_position_embeddings
									lowerCAmelCase_    :  Optional[Any]             =				initializer_range
									lowerCAmelCase_    :  Union[str, Any]             =				None
									lowerCAmelCase_    :  Optional[int]             =				0
									lowerCAmelCase_    :  Optional[Any]             =				2
									lowerCAmelCase_    :  str             =				1




							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  str             =				np.clip(ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size)       ,       3       ,       self.vocab_size)

									lowerCAmelCase_    :  str             =				None
									if self.use_input_mask:
											lowerCAmelCase_    :  Tuple             =				random_attention_mask([self.batch_size, self.seq_length])

									lowerCAmelCase_    :  Optional[int]             =				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      UpperCAmelCase__    ( self  : Dict):
									lowerCAmelCase_    :  Any             =				self.prepare_config_and_inputs()
									lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				config_and_inputs
									lowerCAmelCase_    :  Optional[int]             =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[int]             =				self.prepare_config_and_inputs()

									lowerCAmelCase_    :  Any             =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
									lowerCAmelCase_    :  Union[str, Any]             =				ids_tensor([self.batch_size, self.seq_length]       ,       vocab_size=2)

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




							def      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : str       ,       A_  : List[str]       ,       A_  : Optional[int]       ,       A_  : Optional[Any]):
									lowerCAmelCase_    :  Optional[int]             =				2_0
									lowerCAmelCase_    :  Any             =				model_class_name(A_)

									lowerCAmelCase_    :  Tuple             =				model.init_cache(input_ids.shape[0]       ,       A_)
									lowerCAmelCase_    :  int             =				jnp.ones((input_ids.shape[0], max_decoder_length)       ,       dtype='''i4''')

									lowerCAmelCase_    :  List[str]             =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,       (input_ids.shape[0], input_ids.shape[-1] - 1))
									lowerCAmelCase_    :  Any             =				model(
									    input_ids[:, :-1]       ,       attention_mask=A_       ,       past_key_values=A_       ,       position_ids=A_       ,       )

									lowerCAmelCase_    :  str             =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,       dtype='''i4''')
									lowerCAmelCase_    :  List[Any]             =				model(
									    input_ids[:, -1:]       ,       attention_mask=A_       ,       past_key_values=outputs_cache.past_key_values       ,       position_ids=A_       ,       )

									lowerCAmelCase_    :  Any             =				model(A_)

									lowerCAmelCase_    :  Dict             =				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      UpperCAmelCase__    ( self  : Any       ,       A_  : Dict       ,       A_  : Optional[Any]       ,       A_  : Union[str, Any]       ,       A_  : Optional[Any]):
									lowerCAmelCase_    :  Dict             =				2_0
									lowerCAmelCase_    :  List[Any]             =				model_class_name(A_)

									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]       ,       A_)
									lowerCAmelCase_    :  Tuple             =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,       (input_ids.shape[0], input_ids.shape[-1] - 1))

									lowerCAmelCase_    :  Union[str, Any]             =				model(
									    input_ids[:, :-1]       ,       attention_mask=A_       ,       past_key_values=A_       ,       position_ids=A_       ,       )
									lowerCAmelCase_    :  List[str]             =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,       dtype='''i4''')
									lowerCAmelCase_    :  List[Any]             =				model(
									    input_ids[:, -1:]       ,       past_key_values=outputs_cache.past_key_values       ,       attention_mask=A_       ,       position_ids=A_       ,       )

									lowerCAmelCase_    :  Optional[int]             =				model(A_       ,       attention_mask=A_)
									lowerCAmelCase_    :  Dict             =				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        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		,unittest.TestCase		):
							_a       =				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
							_a       =				(FlaxXGLMForCausalLM,) if is_flax_available() else ()




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  Optional[int]             =				FlaxXGLMModelTester(self)




							def      UpperCAmelCase__    ( self  : str):
									for model_class_name in self.all_model_classes:
											lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  List[str]             =				self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(A_       ,       A_       ,       A_       ,       A_)




							def      UpperCAmelCase__    ( self  : List[str]):
									for model_class_name in self.all_model_classes:
											lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  Union[str, Any]             =				self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    A_       ,       A_       ,       A_       ,       A_)




							@slow
							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  int             =				XGLMTokenizer.from_pretrained('''XGLM'''       ,       padding_side='''left''')
									lowerCAmelCase_    :  int             =				tokenizer(['''Hello this is a long string''', '''Hey''']       ,       return_tensors='''np'''       ,       padding=A_       ,       truncation=A_)

									lowerCAmelCase_    :  Union[str, Any]             =				FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
									lowerCAmelCase_    :  List[str]             =				1
									lowerCAmelCase_    :  Any             =				False

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

									lowerCAmelCase_    :  Union[str, Any]             =				jit_generate(inputs['''input_ids''']       ,       attention_mask=inputs['''attention_mask''']).sequences

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

									lowerCAmelCase_    :  Union[str, Any]             =				[
									    '''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      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  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_    :  Tuple             =				self._prepare_for_class(A_       ,       A_)
													lowerCAmelCase_    :  Any             =				{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

													lowerCAmelCase_      ,						lowerCAmelCase_    :  Union[str, Any]             =				pt_inputs['''input_ids'''].shape
													lowerCAmelCase_    :  Any             =				np.random.randint(0       ,       seq_length - 1       ,       size=(batch_size,))
													for batch_idx, start_index in enumerate(A_):
															lowerCAmelCase_    :  List[Any]             =				0
															lowerCAmelCase_    :  List[Any]             =				1
															lowerCAmelCase_    :  Optional[Any]             =				0
															lowerCAmelCase_    :  List[Any]             =				1
													lowerCAmelCase_    :  int             =				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.
													lowerCAmelCase_    :  Optional[Any]             =				False
													lowerCAmelCase_    :  List[str]             =				model_class(A_       ,       dtype=jnp.floataa)
													lowerCAmelCase_    :  Union[str, Any]             =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,       A_)
													lowerCAmelCase_    :  int             =				fx_state

													with torch.no_grad():
															lowerCAmelCase_    :  Optional[int]             =				pt_model(**A_).to_tuple()

													lowerCAmelCase_    :  Union[str, Any]             =				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_)
															lowerCAmelCase_    :  Any             =				model_class.from_pretrained(A_       ,       from_pt=A_)

													lowerCAmelCase_    :  List[Any]             =				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      UpperCAmelCase__    ( self  : Dict):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  List[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_    :  str             =				self._prepare_for_class(A_       ,       A_)
													lowerCAmelCase_    :  List[str]             =				{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

													lowerCAmelCase_    :  List[Any]             =				pt_model_class(A_).eval()
													lowerCAmelCase_    :  int             =				False
													lowerCAmelCase_    :  List[str]             =				model_class(A_       ,       dtype=jnp.floataa)

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

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

													with torch.no_grad():
															lowerCAmelCase_    :  Dict             =				pt_model(**A_).to_tuple()

													lowerCAmelCase_    :  Optional[int]             =				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_)
															lowerCAmelCase_    :  Optional[int]             =				pt_model_class.from_pretrained(A_       ,       from_flax=A_)

													with torch.no_grad():
															lowerCAmelCase_    :  Dict             =				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      UpperCAmelCase__    ( self  : Union[str, Any]):
									for model_class_name in self.all_model_classes:
											lowerCAmelCase_    :  Union[str, Any]             =				model_class_name.from_pretrained('''facebook/xglm-564M''')
											lowerCAmelCase_    :  List[Any]             =				model(np.ones((1, 1)))
											self.assertIsNotNone(A_)










from typing import TYPE_CHECKING

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


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Any          =      [
						    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SegformerDecodeHead''',
						    '''SegformerForImageClassification''',
						    '''SegformerForSemanticSegmentation''',
						    '''SegformerLayer''',
						    '''SegformerModel''',
						    '''SegformerPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFSegformerDecodeHead''',
						    '''TFSegformerForImageClassification''',
						    '''TFSegformerForSemanticSegmentation''',
						    '''TFSegformerModel''',
						    '''TFSegformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						A__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





