





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					:  str				,						a					:  List[str]				,						a					:  List[Any]=14				,						a					:  Optional[Any]=7				,						a					:  Dict=True				,						a					:  Tuple=True				,						a					:  List[Any]=True				,						a					:  Union[str, Any]=99				,						a					:  Optional[int]=32				,						a					:  Optional[Any]=5				,						a					:  Dict=4				,						a					:  List[str]=37				,						a					:  List[str]="gelu"				,						a					:  List[Any]=0.1				,						a					:  List[str]=0.1				,						a					:  Tuple=5_12				,						a					:  Union[str, Any]=0.02				,						a					:  Union[str, Any]=None				,						)						->Dict:
													SCREAMING_SNAKE_CASE__  :					Tuple =       parent
													SCREAMING_SNAKE_CASE__  :					str =       batch_size
													SCREAMING_SNAKE_CASE__  :					str =       seq_length
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       is_training
													SCREAMING_SNAKE_CASE__  :					Dict =       use_input_mask
													SCREAMING_SNAKE_CASE__  :					List[Any] =       use_labels
													SCREAMING_SNAKE_CASE__  :					str =       vocab_size
													SCREAMING_SNAKE_CASE__  :					List[Any] =       d_model
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       num_hidden_layers
													SCREAMING_SNAKE_CASE__  :					int =       num_attention_heads
													SCREAMING_SNAKE_CASE__  :					Any =       ffn_dim
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       activation_function
													SCREAMING_SNAKE_CASE__  :					List[Any] =       activation_dropout
													SCREAMING_SNAKE_CASE__  :					List[str] =       attention_dropout
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       max_position_embeddings
													SCREAMING_SNAKE_CASE__  :					Any =       initializer_range
													SCREAMING_SNAKE_CASE__  :					str =       None
													SCREAMING_SNAKE_CASE__  :					int =       0
													SCREAMING_SNAKE_CASE__  :					Any =       2
													SCREAMING_SNAKE_CASE__  :					Dict =       1

							def   A_	(   self					:  Optional[Any]     )						->Optional[Any]:
													SCREAMING_SNAKE_CASE__  :					List[str] =       np.clip(ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size     )				,						3				,						self.vocab_size     )

													SCREAMING_SNAKE_CASE__  :					int =       None
													if self.use_input_mask:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       random_attention_mask([self.batch_size, self.seq_length]     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       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   A_	(   self					:  Any     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					List[str] =       self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Any =       config_and_inputs
													SCREAMING_SNAKE_CASE__  :					Tuple =       {"input_ids": input_ids, "attention_mask": attention_mask}
													return config, inputs_dict

							def   A_	(   self					:  str     )						->List[str]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       self.prepare_config_and_inputs()

													SCREAMING_SNAKE_CASE__  :					Tuple =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       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					:  Optional[int]				,						a					:  List[str]				,						a					:  Union[str, Any]				,						a					:  Tuple				,						a					:  Any     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       20
													SCREAMING_SNAKE_CASE__  :					List[Any] =       model_class_name(a     )

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       model.init_cache(input_ids.shape[0]				,						a     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       jnp.ones((input_ids.shape[0], max_decoder_length)				,						dtype="i4"     )

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

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

													SCREAMING_SNAKE_CASE__  :					List[Any] =       model(a     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       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					:  Tuple				,						a					:  Any				,						a					:  Union[str, Any]				,						a					:  Dict				,						a					:  Any     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					Tuple =       20
													SCREAMING_SNAKE_CASE__  :					List[Any] =       model_class_name(a     )

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

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

													SCREAMING_SNAKE_CASE__  :					Tuple =       model(
													    input_ids[:, :-1]				,						attention_mask=a				,						past_key_values=a				,						position_ids=a				,						)
													SCREAMING_SNAKE_CASE__  :					str =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,						dtype="i4"     )
													SCREAMING_SNAKE_CASE__  :					Any =       model(
													    input_ids[:, -1:]				,						past_key_values=outputs_cache.past_key_values				,						attention_mask=a				,						position_ids=a				,						)

													SCREAMING_SNAKE_CASE__  :					Any =       model(a				,						attention_mask=a     )
													SCREAMING_SNAKE_CASE__  :					str =       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  (					lowercase__       ,				lowercase__       ,				unittest.TestCase				):






							"""simple docstring"""

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

							def   A_	(   self					:  List[Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					int =       FlaxXGLMModelTester(self     )

							def   A_	(   self					:  int     )						->Dict:
													for model_class_name in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(a				,						a				,						a				,						a     )

							def   A_	(   self					:  Dict     )						->List[str]:
													for model_class_name in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    a				,						a				,						a				,						a     )

							@slow
							def   A_	(   self					:  str     )						->str:
													SCREAMING_SNAKE_CASE__  :					Dict =       XGLMTokenizer.from_pretrained("XGLM"				,						padding_side="left"     )
													SCREAMING_SNAKE_CASE__  :					Any =       tokenizer(["Hello this is a long string", "Hey"]				,						return_tensors="np"				,						padding=a				,						truncation=a     )

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )
													SCREAMING_SNAKE_CASE__  :					Dict =       1
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       False

													SCREAMING_SNAKE_CASE__  :					int =       jax.jit(model.generate     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       jit_generate(inputs["input_ids"]				,						attention_mask=inputs["attention_mask"]     ).sequences

													SCREAMING_SNAKE_CASE__  :					int =       tokenizer.batch_decode(a				,						skip_special_tokens=a     )

													SCREAMING_SNAKE_CASE__  :					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(a				,						a     )

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

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

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

																									with torch.no_grad():
																															SCREAMING_SNAKE_CASE__  :					Optional[Any] =       pt_model(**a     ).to_tuple()

																									SCREAMING_SNAKE_CASE__  :					List[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     )
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       model_class.from_pretrained(a				,						from_pt=a     )

																									SCREAMING_SNAKE_CASE__  :					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   A_	(   self					:  List[Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[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
																									SCREAMING_SNAKE_CASE__  :					int =       self._prepare_for_class(a				,						a     )
																									SCREAMING_SNAKE_CASE__  :					Tuple =       {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

																									SCREAMING_SNAKE_CASE__  :					Any =       pt_model_class(a     ).eval()
																									SCREAMING_SNAKE_CASE__  :					Dict =       False
																									SCREAMING_SNAKE_CASE__  :					Tuple =       model_class(a				,						dtype=jnp.floataa     )

																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       load_flax_weights_in_pytorch_model(a				,						fx_model.params     )
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       pt_inputs["input_ids"].shape
																									SCREAMING_SNAKE_CASE__  :					Dict =       np.random.randint(0				,						seq_length - 1				,						size=(batch_size,)     )
																									for batch_idx, start_index in enumerate(a     ):
																															SCREAMING_SNAKE_CASE__  :					Any =       0
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       1
																															SCREAMING_SNAKE_CASE__  :					Any =       0
																															SCREAMING_SNAKE_CASE__  :					Optional[Any] =       1

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

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

																									SCREAMING_SNAKE_CASE__  :					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     )
																															SCREAMING_SNAKE_CASE__  :					Optional[Any] =       pt_model_class.from_pretrained(a				,						from_flax=a     )

																									with torch.no_grad():
																															SCREAMING_SNAKE_CASE__  :					str =       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   A_	(   self					:  Optional[int]     )						->Tuple:
													for model_class_name in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       model_class_name.from_pretrained("facebook/xglm-564M"     )
																			SCREAMING_SNAKE_CASE__  :					str =       model(np.ones((1, 1)     )     )
																			self.assertIsNotNone(a     )






from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class  _a  :






							"""simple docstring"""

							snake_case_							=    ("_front", "_back", "_len")



							@dataclass
							class  _a  :






														"""simple docstring"""

														snake_case_							=    None
														snake_case_							=    None
														snake_case_							=    None







							class  _a  :






														"""simple docstring"""

														snake_case_							=    ("_cur",)

														def __init__(   self					:  Tuple				,						a					:  Deque._Node | None     )						->None:
																				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       cur

														def __iter__(   self					:  Dict     )						->Deque._Iterator:
																				return self






														def   A_	(   self					:  int     )						->Any:
																				if self._cur is None:
																										# finished iterating
																										raise StopIteration
																				SCREAMING_SNAKE_CASE__  :					Tuple =       self._cur.val
																				SCREAMING_SNAKE_CASE__  :					int =       self._cur.next_node

																				return val







							def __init__(   self					:  str				,						a					:  Iterable[Any] | None = None     )						->None:
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					int =       0

													if iterable is not None:
																			# append every value to the deque
																			for val in iterable:
																									self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					int =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       node
																			SCREAMING_SNAKE_CASE__  :					Any =       self._back
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node  # assign new back to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  List[str]				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					str =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Any =       self._front
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node
																			SCREAMING_SNAKE_CASE__  :					int =       node  # assign new front to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  Dict				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.appendleft(a     )

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._back
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self._back.prev_node  # set new back
													# drop the last node - python will deallocate memory automatically
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					Any =       self._front
													SCREAMING_SNAKE_CASE__  :					Dict =       self._front.next_node  # set new front and drop the first node
													SCREAMING_SNAKE_CASE__  :					Dict =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  str     )						->bool:
													return self._front is None

							def __len__(   self					:  List[str]     )						->int:
													return self._len

							def __eq__(   self					:  List[Any]				,						a					:  object     )						->bool:

													if not isinstance(a				,						a     ):
																			return NotImplemented

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._front
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       other._front

													# if the length of the dequeues are not the same, they are not equal
													if len(self     ) != len(a     ):
																			return False

													while me is not None and oth is not None:
																			# compare every value
																			if me.val != oth.val:
																									return False
																			SCREAMING_SNAKE_CASE__  :					Any =       me.next_node
																			SCREAMING_SNAKE_CASE__  :					Any =       oth.next_node

													return True

							def __iter__(   self					:  Any     )						->Deque._Iterator:
													return Deque._Iterator(self._front     )






							def __repr__(   self					:  Any     )						->str:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
													SCREAMING_SNAKE_CASE__  :					int =       self._front
													while aux is not None:
																			# append the values in a list to display
																			values_list.append(aux.val     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       aux.next_node

													return f"""[{', '.join(repr(a     ) for val in values_list     )}]"""


if __name__ == "__main__":
				import doctest

				doctest.testmod()