
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							:



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=14    ,       lowerCamelCase__=7    ,       lowerCamelCase__=True    ,       lowerCamelCase__=True    ,       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    ,       lowerCamelCase__=None    ,       )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    parent
									lowercase__								=    batch_size
									lowercase__								=    seq_length
									lowercase__								=    is_training
									lowercase__								=    use_input_mask
									lowercase__								=    use_labels
									lowercase__								=    vocab_size
									lowercase__								=    d_model
									lowercase__								=    num_hidden_layers
									lowercase__								=    num_attention_heads
									lowercase__								=    ffn_dim
									lowercase__								=    activation_function
									lowercase__								=    activation_dropout
									lowercase__								=    attention_dropout
									lowercase__								=    max_position_embeddings
									lowercase__								=    initializer_range
									lowercase__								=    None
									lowercase__								=    0
									lowercase__								=    2
									lowercase__								=    1



			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    np.clip(ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size    )    ,       3    ,       self.vocab_size    )

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

									lowercase__								=    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=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[int]:




									'''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    )     ->						int:




									'''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__    )     ->						Union[str, Any]:




									'''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_sentencepiece
@require_flax
class      A							(    _UpperCAmelCase						,  _UpperCAmelCase						,  unittest.TestCase			):
			lowerCamelCase							:							Union[str, Any]    						=					(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			lowerCamelCase							:							str    						=					(FlaxXGLMForCausalLM,) if is_flax_available() else ()



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    FlaxXGLMModelTester(self    )



			def A__	(   self    )     ->						List[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(lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    )



			def A__	(   self    )     ->						List[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_with_attn_mask(
															    lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    )



			@slow
			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    XGLMTokenizer.from_pretrained("""XGLM"""    ,       padding_side="""left"""    )
									lowercase__								=    tokenizer(["""Hello this is a long string""", """Hey"""]    ,       return_tensors="""np"""    ,       padding=lowerCAmelCase__    ,       truncation=lowerCAmelCase__    )

									lowercase__								=    FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""    )
									lowercase__								=    1
									lowercase__								=    False

									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 questions, but I\'m not sure if I\'m""",
									    """Hey, I\'m a newbie to the forum and I\'""",
									]

									self.assertListEqual(lowerCAmelCase__    ,       lowerCAmelCase__    )



			@is_pt_flax_cross_test
			def A__	(   self    )     ->						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__	,  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()
																					# 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.
																					lowercase__								=    False
																					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    )     ->						Optional[Any]:




									'''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__								=    False
																					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    )     ->						Optional[int]:




									'''simple docstring'''



									for model_class_name in self.all_model_classes:
															lowercase__								=    model_class_name.from_pretrained("""facebook/xglm-564M"""    )
															lowercase__								=    model(np.ones((1, 1)    )    )
															self.assertIsNotNone(lowerCAmelCase__    )








'''simple docstring'''



def 	_A						(							lowercase__		):
						return " ".join(sorted(set(sentence.split()		)		)		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






