

"""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       _snake_case  :







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=1_4  ,				UpperCAmelCase_=7  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=9_9  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=5  ,				UpperCAmelCase_=4  ,				UpperCAmelCase_=3_7  ,				UpperCAmelCase_="gelu"  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=0.0_2  ,				UpperCAmelCase_=None  ,				)      ->    Optional[int]:
											__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 							_UpperCamelCase     (self				)      ->    Any:
											__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=UpperCAmelCase_  ,				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				)      ->    List[Any]:
											__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 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__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 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    str:
											__lowercase        =      2_0
											__lowercase        =      model_class_name(UpperCAmelCase_				)

											__lowercase        =      model.init_cache(input_ids.shape[0]  ,				UpperCAmelCase_				)
											__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=UpperCAmelCase_  ,				past_key_values=UpperCAmelCase_  ,				position_ids=UpperCAmelCase_  ,				)

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

											__lowercase        =      model(UpperCAmelCase_				)

											__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 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Optional[int]:
											__lowercase        =      2_0
											__lowercase        =      model_class_name(UpperCAmelCase_				)

											__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]  ,				UpperCAmelCase_				)
											__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=UpperCAmelCase_  ,				past_key_values=UpperCAmelCase_  ,				position_ids=UpperCAmelCase_  ,				)
											__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=UpperCAmelCase_  ,				position_ids=UpperCAmelCase_  ,				)

											__lowercase        =      model(UpperCAmelCase_  ,				attention_mask=UpperCAmelCase_				)
											__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       _snake_case  (    UpperCamelCase    ,      UpperCamelCase    ,      unittest.TestCase							):
						__snake_case			      =  (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
						__snake_case			      =  (FlaxXGLMForCausalLM,) if is_flax_available() else ()







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      FlaxXGLMModelTester(self				)







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											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(UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    int:
											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(
																    UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)







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

											__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(UpperCAmelCase_  ,				skip_special_tokens=UpperCAmelCase_				)

											__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(UpperCAmelCase_  ,				UpperCAmelCase_				)







						@is_pt_flax_cross_test
						def 							_UpperCamelCase     (self				)      ->    Any:
											__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(UpperCAmelCase_  ,				UpperCAmelCase_				)
																					__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(UpperCAmelCase_  ,				UpperCAmelCase_				)

																					__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(UpperCAmelCase_				):
																										__lowercase        =      0
																										__lowercase        =      1
																										__lowercase        =      0
																										__lowercase        =      1
																					__lowercase        =      pt_model_class(UpperCAmelCase_				).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(UpperCAmelCase_  ,				dtype=jnp.floataa				)
																					__lowercase        =      convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,				UpperCAmelCase_				)
																					__lowercase        =      fx_state

																					with torch.no_grad():
																										__lowercase        =      pt_model(**UpperCAmelCase_				).to_tuple()

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

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

																					__lowercase        =      fx_model_loaded(**UpperCAmelCase_				).to_tuple()
																					self.assertEqual(
																					    len(UpperCAmelCase_				)  ,				len(UpperCAmelCase_				)  ,				'''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output_loaded, pt_output in zip(UpperCAmelCase_  ,				UpperCAmelCase_				):
																										self.assert_almost_equals(fx_output_loaded[:, -1]  ,				pt_output[:, -1].numpy()  ,				4e-2				)







						@is_pt_flax_cross_test
						def 							_UpperCamelCase     (self				)      ->    Dict:
											__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(UpperCAmelCase_  ,				UpperCAmelCase_				)
																					__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(UpperCAmelCase_  ,				UpperCAmelCase_				)

																					__lowercase        =      pt_model_class(UpperCAmelCase_				).eval()
																					__lowercase        =      False
																					__lowercase        =      model_class(UpperCAmelCase_  ,				dtype=jnp.floataa				)

																					__lowercase        =      load_flax_weights_in_pytorch_model(UpperCAmelCase_  ,				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(UpperCAmelCase_				):
																										__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(**UpperCAmelCase_				).to_tuple()

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

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

																					with torch.no_grad():
																										__lowercase        =      pt_model_loaded(**UpperCAmelCase_				).to_tuple()

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







						@slow
						def 							_UpperCamelCase     (self				)      ->    List[str]:
											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(UpperCAmelCase_				)





"""simple docstring"""

import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


a__			:						Optional[int]							=			set()
a__			:						str							=			get_logger(__name__)





def 					__UpperCamelCase (     a__:			Optional[str] = None      )					->      Dict:




					"""simple docstring"""



					def decorator(a__:			Callable      ):
										global _emitted_deprecation_warnings

										if inspect.isclass(a__      ):
															__lowercase        =      deprecated_class_or_function.__init__
															__lowercase        =      deprecated_class_or_function.__name__
										else:
															__lowercase        =      deprecated_class_or_function
															__lowercase        =      deprecated_function.__name__
															# Support deprecating __init__ class method: class name instead
															__lowercase        =      name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''      )[-2]

										__lowercase        =      (
										    f'''{name} is deprecated and will be removed in the next major version of datasets.''' + f''' {help_message}'''
										    if help_message
										    else ''''''
										)

										@wraps(a__      )
										def wrapper(*a__:			int			,						**a__:			int      ):
															__lowercase        =      hash(a__      )
															if func_hash not in _emitted_deprecation_warnings:
																				warnings.warn(a__			,						category=a__			,						stacklevel=2      )
																				_emitted_deprecation_warnings.add(a__      )
															return deprecated_function(*a__			,						**a__      )

										__lowercase        =      '''deprecated'''

										if inspect.isclass(a__      ):
															__lowercase        =      wrapper
															return deprecated_class_or_function
										else:
															return wrapper

					return decorator


class       _snake_case  (    enum.EnumMeta							):







						def __getattribute__(cls  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      super().__getattribute__(UpperCAmelCase_				)
											if isinstance(UpperCAmelCase_  ,				enum.Enum				) and obj._on_access:
																obj._on_access()
											return obj







						def __getitem__(cls  ,				UpperCAmelCase_				)      ->    Union[str, Any]:
											__lowercase        =      super().__getitem__(UpperCAmelCase_				)
											if member._on_access:
																member._on_access()
											return member







						def __call__(cls  ,				UpperCAmelCase_  ,				UpperCAmelCase_=None  ,				*, UpperCAmelCase_=None  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=1				)      ->    List[Any]:
											__lowercase        =      super().__call__(UpperCAmelCase_  ,				UpperCAmelCase_  ,				module=UpperCAmelCase_  ,				qualname=UpperCAmelCase_  ,				type=UpperCAmelCase_  ,				start=UpperCAmelCase_				)
											if isinstance(UpperCAmelCase_  ,				enum.Enum				) and obj._on_access:
																obj._on_access()
											return obj



class       _snake_case  (    enum.Enum    ,      metaclass=UpperCamelCase							):







						def __new__(cls  ,				UpperCAmelCase_				)      ->    Optional[int]:
											__lowercase        =      object.__new__(cls				)
											__lowercase        =      value
											__lowercase        =      member.deprecate
											return member







						@property
						def 							_UpperCamelCase     (self				)      ->    Dict:
											return ""







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      f''' {self.help_message}''' if self.help_message else ''''''
											warnings.warn(
											    f'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
											    + help_message  ,				UpperCAmelCase_  ,				stacklevel=3  ,				)


