






'''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 				__magic_name__  :







	"""simple docstring"""



	def __init__(				self	,   _lowercase	,   _lowercase=14	,   _lowercase=7	,   _lowercase=True	,   _lowercase=True	,   _lowercase=True	,   _lowercase=99	,   _lowercase=32	,   _lowercase=5	,   _lowercase=4	,   _lowercase=37	,   _lowercase="gelu"	,   _lowercase=0.1	,   _lowercase=0.1	,   _lowercase=512	,   _lowercase=0.02	,   _lowercase=None	,   )    ->	List[str]:
							lowercase_   : Tuple   	=      parent
							lowercase_   : Any   	=      batch_size
							lowercase_   : str   	=      seq_length
							lowercase_   : int   	=      is_training
							lowercase_   : Optional[Any]   	=      use_input_mask
							lowercase_   : Any   	=      use_labels
							lowercase_   : List[str]   	=      vocab_size
							lowercase_   : List[str]   	=      d_model
							lowercase_   : Optional[Any]   	=      num_hidden_layers
							lowercase_   : Tuple   	=      num_attention_heads
							lowercase_   : List[str]   	=      ffn_dim
							lowercase_   : str   	=      activation_function
							lowercase_   : Any   	=      activation_dropout
							lowercase_   : Optional[Any]   	=      attention_dropout
							lowercase_   : Union[str, Any]   	=      max_position_embeddings
							lowercase_   : Optional[Any]   	=      initializer_range
							lowercase_   : Optional[Any]   	=      None
							lowercase_   : Any   	=      0
							lowercase_   : Dict   	=      2
							lowercase_   : Optional[int]   	=      1
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : Tuple   	=      np.clip(ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size							)	,   3	,   self.vocab_size							)

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

							lowercase_   : 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=_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 							lowerCamelCase__		(				self							)    ->	str:
							lowercase_   : Optional[int]   	=      self.prepare_config_and_inputs()
							lowercase_	,   lowercase_	,   lowercase_   : str   	=      config_and_inputs
							lowercase_   : Dict   	=      {'input_ids': input_ids, 'attention_mask': attention_mask}
							return config, inputs_dict
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							lowercase_	,   lowercase_	,   lowercase_   : Dict   	=      self.prepare_config_and_inputs()

							lowercase_   : Dict   	=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
							lowercase_   : 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 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : List[str]   	=      20
							lowercase_   : int   	=      model_class_name(_lowerCAmelCase							)

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

							lowercase_   : Tuple   	=      jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1							)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1)							)
							lowercase_   : Union[str, Any]   	=      model(
							    input_ids[:, :-1]	,   attention_mask=_lowerCAmelCase	,   past_key_values=_lowerCAmelCase	,   position_ids=_lowerCAmelCase	,   )

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

							lowercase_   : Any   	=      model(_lowerCAmelCase							)

							lowercase_   : int   	=      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 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Any   	=      20
							lowercase_   : Dict   	=      model_class_name(_lowerCAmelCase							)

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

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

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

							lowercase_   : str   	=      model(_lowerCAmelCase	,   attention_mask=_lowerCAmelCase							)
							lowercase_   : Optional[int]   	=      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 				__magic_name__  (						UpperCAmelCase__,    UpperCAmelCase__,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   Tuple   =	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
	SCREAMING_SNAKE_CASE_       :   List[Any]   =	(FlaxXGLMForCausalLM,) if is_flax_available() else ()
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : str   	=      FlaxXGLMModelTester(self							)
	def 							lowerCamelCase__		(				self							)    ->	Any:
							for model_class_name in self.all_model_classes:
													lowercase_	,   lowercase_	,   lowercase_   : Tuple   	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(_lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase							)
	def 							lowerCamelCase__		(				self							)    ->	int:
							for model_class_name in self.all_model_classes:
													lowercase_	,   lowercase_	,   lowercase_   : int   	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase							)
	@slow
	def 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							lowercase_   : int   	=      XGLMTokenizer.from_pretrained('XGLM'	,   padding_side='left'							)
							lowercase_   : Tuple   	=      tokenizer(['Hello this is a long string', 'Hey']	,   return_tensors='np'	,   padding=_lowerCAmelCase	,   truncation=_lowerCAmelCase							)

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

							lowercase_   : Dict   	=      jax.jit(model.generate							)

							lowercase_   : Dict   	=      jit_generate(inputs['input_ids']	,   attention_mask=inputs['attention_mask']							).sequences

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

							lowercase_   : Tuple   	=      [
							    '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 							lowerCamelCase__		(				self							)    ->	List[str]:
							lowercase_	,   lowercase_   : Dict   	=      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_   : List[str]   	=      self._prepare_for_class(_lowerCAmelCase	,   _lowerCAmelCase							)
																			lowercase_   : Tuple   	=      {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_   : List[Any]   	=      getattr(_lowerCAmelCase	,   _lowerCAmelCase							)

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

																			with torch.no_grad():
																									lowercase_   : Union[str, Any]   	=      pt_model(**_lowerCAmelCase							).to_tuple()

																			lowercase_   : List[Any]   	=      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_   : Any   	=      model_class.from_pretrained(_lowerCAmelCase	,   from_pt=_lowerCAmelCase							)

																			lowercase_   : Optional[Any]   	=      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 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_	,   lowercase_   : Union[str, 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
																			lowercase_   : int   	=      self._prepare_for_class(_lowerCAmelCase	,   _lowerCAmelCase							)
																			lowercase_   : int   	=      {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

																			lowercase_   : List[Any]   	=      pt_model_class(_lowerCAmelCase							).eval()
																			lowercase_   : Dict   	=      False
																			lowercase_   : Dict   	=      model_class(_lowerCAmelCase	,   dtype=jnp.floataa							)

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

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

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

																			lowercase_   : Optional[Any]   	=      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_   : Optional[Any]   	=      pt_model_class.from_pretrained(_lowerCAmelCase	,   from_flax=_lowerCAmelCase							)

																			with torch.no_grad():
																									lowercase_   : int   	=      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 							lowerCamelCase__		(				self							)    ->	Dict:
							for model_class_name in self.all_model_classes:
													lowercase_   : Optional[Any]   	=      model_class_name.from_pretrained('facebook/xglm-564M'							)
													lowercase_   : Union[str, Any]   	=      model(np.ones((1, 1)							)							)
													self.assertIsNotNone(_lowerCAmelCase							)






from __future__ import annotations

from typing import Any


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):  # noqa: A002
            a			   =item
            a			   =next



class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self	):
            a			   =None
            a			   =0
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            a			   =Node(_lowerCAmelCase ,    self.head	)
            self.size += 1
     def        lowerCAmelCase__  (				self	):
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   a			   =self.head.item
                   a			   =self.head.next
                   self.size -= 1
                   return item
     def        lowerCAmelCase__  (				self	):
            return self.head is None
     def __str__(				self	):
            if self.is_empty():
                   return ""
            else:
                   a			   =self.head
                   a			   =""""""
                   a			   =[]
                   while iterate:
                          item_list.append(str(iterate.item	)	)
                          a			   =iterate.next

                   a			   =""" --> """.join(_lowerCAmelCase	)

                   return item_str






     def __len__(				self	):
            return self.size
