

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    __UpperCAmelCase	:



   def __init__(				self				:	str,						__A				:	List[str],						__A				:	List[Any]=1_4,						__A				:	Optional[Any]=7,						__A				:	Dict=True,						__A				:	Tuple=True,						__A				:	List[Any]=True,						__A				:	Union[str, Any]=9_9,						__A				:	Optional[int]=3_2,						__A				:	Optional[Any]=5,						__A				:	Dict=4,						__A				:	List[str]=3_7,						__A				:	List[str]="gelu",						__A				:	List[Any]=0.1,						__A				:	List[str]=0.1,						__A				:	Tuple=5_1_2,						__A				:	Union[str, Any]=0.0_2,						__A				:	Union[str, Any]=None,						):
     UpperCAmelCase			:				Tuple										=					parent
     UpperCAmelCase			:				str										=					batch_size
     UpperCAmelCase			:				str										=					seq_length
     UpperCAmelCase			:				Union[str, Any]										=					is_training
     UpperCAmelCase			:				Dict										=					use_input_mask
     UpperCAmelCase			:				List[Any]										=					use_labels
     UpperCAmelCase			:				str										=					vocab_size
     UpperCAmelCase			:				List[Any]										=					d_model
     UpperCAmelCase			:				Optional[Any]										=					num_hidden_layers
     UpperCAmelCase			:				int										=					num_attention_heads
     UpperCAmelCase			:				Any										=					ffn_dim
     UpperCAmelCase			:				Union[str, Any]										=					activation_function
     UpperCAmelCase			:				List[Any]										=					activation_dropout
     UpperCAmelCase			:				List[str]										=					attention_dropout
     UpperCAmelCase			:				Optional[int]										=					max_position_embeddings
     UpperCAmelCase			:				Any										=					initializer_range
     UpperCAmelCase			:				str										=					None
     UpperCAmelCase			:				int										=					0
     UpperCAmelCase			:				Any										=					2
     UpperCAmelCase			:				Dict										=					1



   def 			__magic_name__       (				self				:	Optional[Any]		):
     UpperCAmelCase			:				List[str]										=					np.clip(ids_tensor([self.batch_size, self.seq_length],						self.vocab_size		),						3,						self.vocab_size		)

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

     UpperCAmelCase			:				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 			__magic_name__       (				self				:	Any		):
     UpperCAmelCase			:				List[str]										=					self.prepare_config_and_inputs()
     UpperCAmelCase			:				Any										=					config_and_inputs
     UpperCAmelCase			:				Tuple										=					{"input_ids": input_ids, "attention_mask": attention_mask}
     return config, inputs_dict



   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				int										=					self.prepare_config_and_inputs()

     UpperCAmelCase			:				Tuple										=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
     UpperCAmelCase			:				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 			__magic_name__       (				self				:	Optional[int],						__A				:	List[str],						__A				:	Union[str, Any],						__A				:	Tuple,						__A				:	Any		):
     UpperCAmelCase			:				Optional[int]										=					2_0
     UpperCAmelCase			:				List[Any]										=					model_class_name(__A		)

     UpperCAmelCase			:				Optional[int]										=					model.init_cache(input_ids.shape[0],						__A		)
     UpperCAmelCase			:				Optional[Any]										=					jnp.ones((input_ids.shape[0], max_decoder_length),						dtype='''i4'''		)

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

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

     UpperCAmelCase			:				List[Any]										=					model(__A		)

     UpperCAmelCase			:				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 			__magic_name__       (				self				:	Tuple,						__A				:	Any,						__A				:	Union[str, Any],						__A				:	Dict,						__A				:	Any		):
     UpperCAmelCase			:				Tuple										=					2_0
     UpperCAmelCase			:				List[Any]										=					model_class_name(__A		)

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

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

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

     UpperCAmelCase			:				Any										=					model(__A,						attention_mask=__A		)
     UpperCAmelCase			:				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    __UpperCAmelCase	(			lowercase__     ,    lowercase__     ,    unittest.TestCase		):
   UpperCamelCase  	=	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   UpperCamelCase  	=	(FlaxXGLMForCausalLM,) if is_flax_available() else ()



   def 			__magic_name__       (				self				:	List[Any]		):
     UpperCAmelCase			:				int										=					FlaxXGLMModelTester(self		)



   def 			__magic_name__       (				self				:	int		):
     for model_class_name in self.all_model_classes:
       UpperCAmelCase			:				Union[str, Any]										=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(__A,						__A,						__A,						__A		)



   def 			__magic_name__       (				self				:	Dict		):
     for model_class_name in self.all_model_classes:
       UpperCAmelCase			:				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 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Dict										=					XGLMTokenizer.from_pretrained('''XGLM''',						padding_side='''left'''		)
     UpperCAmelCase			:				Any										=					tokenizer(['''Hello this is a long string''', '''Hey'''],						return_tensors='''np''',						padding=__A,						truncation=__A		)

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

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

     UpperCAmelCase			:				List[Any]										=					jit_generate(inputs['''input_ids'''],						attention_mask=inputs['''attention_mask''']		).sequences

     UpperCAmelCase			:				int										=					tokenizer.batch_decode(__A,						skip_special_tokens=__A		)

     UpperCAmelCase			:				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 			__magic_name__       (				self				:	int		):
     UpperCAmelCase			:				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
         UpperCAmelCase			:				Union[str, Any]										=					self._prepare_for_class(__A,						__A		)
         UpperCAmelCase			:				List[Any]										=					{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

         with torch.no_grad():
           UpperCAmelCase			:				Optional[Any]										=					pt_model(**__A		).to_tuple()

         UpperCAmelCase			:				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		)
           UpperCAmelCase			:				List[Any]										=					model_class.from_pretrained(__A,						from_pt=__A		)

         UpperCAmelCase			:				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 			__magic_name__       (				self				:	List[Any]		):
     UpperCAmelCase			:				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
         UpperCAmelCase			:				int										=					self._prepare_for_class(__A,						__A		)
         UpperCAmelCase			:				Tuple										=					{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

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

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

         with torch.no_grad():
           UpperCAmelCase			:				Tuple										=					pt_model(**__A		).to_tuple()

         UpperCAmelCase			:				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		)
           UpperCAmelCase			:				Optional[Any]										=					pt_model_class.from_pretrained(__A,						from_flax=__A		)

         with torch.no_grad():
           UpperCAmelCase			:				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 			__magic_name__       (				self				:	Optional[int]		):
     for model_class_name in self.all_model_classes:
       UpperCAmelCase			:				Tuple										=					model_class_name.from_pretrained('''facebook/xglm-564M'''		)
       UpperCAmelCase			:				str										=					model(np.ones((1, 1)		)		)
       self.assertIsNotNone(__A		)












from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase							:List[Any]							=	TypeVar("T", bound="Enum")



@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    80
							snake_case_							=    70
							snake_case_							=    60
							snake_case_							=    50
							snake_case_							=    40
							snake_case_							=    30
							snake_case_							=    20
							snake_case_							=    10







@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    24
							snake_case_							=    21
							snake_case_							=    18
							snake_case_							=    15
							snake_case_							=    12
							snake_case_							=    9
							snake_case_							=    6
							snake_case_							=    3
							snake_case_							=    2
							snake_case_							=    1
							snake_case_							=    -1
							snake_case_							=    -2
							snake_case_							=    -3
							snake_case_							=    -6
							snake_case_							=    -9
							snake_case_							=    -12
							snake_case_							=    -15
							snake_case_							=    -18
							snake_case_							=    -21
							snake_case_							=    -24

							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value > 0}






							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value < 0}







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												SCREAMING_SNAKE_CASE__  :					str =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {name_prefix}"""
						return str(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						for prefix in BinaryUnit:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {prefix.name}"""
						return str(_lowerCamelCase  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()