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






   def  __UpperCamelCase	( self							:	int    )   ->		Any:
       lowercase__					:  int          =							np.clip(ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size    )      ,		3      ,		self.vocab_size    )

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

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






   def  __UpperCamelCase	( self							:	List[str]    )   ->		Optional[Any]:
       lowercase__   ,  lowercase__   ,  lowercase__					:  Optional[Any]          =							self.prepare_config_and_inputs()

       lowercase__					:  Tuple          =							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  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Dict      ,		lowercase_							:	Tuple      ,		lowercase_							:	List[Any]      ,		lowercase_							:	List[Any]    )   ->		str:
       lowercase__					:  int          =							20
       lowercase__					:  Tuple          =							model_class_name(UpperCAmelCase_    )

       lowercase__					:  Union[str, Any]          =							model.init_cache(input_ids.shape[0]      ,		UpperCAmelCase_    )
       lowercase__					:  Any          =							jnp.ones((input_ids.shape[0], max_decoder_length)      ,		dtype="i4"    )

       lowercase__					:  List[str]          =							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=UpperCAmelCase_      ,		past_key_values=UpperCAmelCase_      ,		position_ids=UpperCAmelCase_      ,		)

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

       lowercase__					:  Dict          =							model(UpperCAmelCase_    )

       lowercase__					:  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  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Any      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Tuple    )   ->		Optional[int]:
       lowercase__					:  int          =							20
       lowercase__					:  Dict          =							model_class_name(UpperCAmelCase_    )

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

       lowercase__					:  Any          =							model.init_cache(input_ids.shape[0]      ,		UpperCAmelCase_    )
       lowercase__					:  Optional[Any]          =							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=UpperCAmelCase_      ,		past_key_values=UpperCAmelCase_      ,		position_ids=UpperCAmelCase_      ,		)
       lowercase__					:  Any          =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,		dtype="i4"    )
       lowercase__					:  Any          =							model(
           input_ids[:, -1:]      ,		past_key_values=outputs_cache.past_key_values      ,		attention_mask=UpperCAmelCase_      ,		position_ids=UpperCAmelCase_      ,		)

       lowercase__					:  Any          =							model(UpperCAmelCase_      ,		attention_mask=UpperCAmelCase_    )
       lowercase__					:  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     snake_case_			(						__A    ,__A    ,unittest.TestCase			):
   __A      :						Union[str, Any]   =							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   __A      :						Optional[int]   =							(FlaxXGLMForCausalLM,) if is_flax_available() else ()






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		str:
       lowercase__					:  Tuple          =							FlaxXGLMModelTester(self    )






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






   def  __UpperCamelCase	( self							:	int    )   ->		int:
       for model_class_name in self.all_model_classes:
           lowercase__   ,  lowercase__   ,  lowercase__					:  Dict          =							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							:	List[Any]    )   ->		Dict:
       lowercase__					:  Tuple          =							XGLMTokenizer.from_pretrained("XGLM"      ,		padding_side="left"    )
       lowercase__					:  Optional[int]          =							tokenizer(["Hello this is a long string", "Hey"]      ,		return_tensors="np"      ,		padding=UpperCAmelCase_      ,		truncation=UpperCAmelCase_    )

       lowercase__					:  List[str]          =							FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"    )
       lowercase__					:  Optional[Any]          =							1
       lowercase__					:  str          =							False

       lowercase__					:  str          =							jax.jit(model.generate    )

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

       lowercase__					:  str          =							tokenizer.batch_decode(UpperCAmelCase_      ,		skip_special_tokens=UpperCAmelCase_    )

       lowercase__					:  Dict          =							[
           "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							:	str    )   ->		Any:
       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__					:  str          =							self._prepare_for_class(UpperCAmelCase_      ,		UpperCAmelCase_    )
               lowercase__					:  List[Any]          =							{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(UpperCAmelCase_      ,		UpperCAmelCase_    )

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

               with torch.no_grad():
                   lowercase__					:  Optional[int]          =							pt_model(**UpperCAmelCase_    ).to_tuple()

               lowercase__					:  Optional[int]          =							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__					:  Optional[int]          =							model_class.from_pretrained(UpperCAmelCase_      ,		from_pt=UpperCAmelCase_    )

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

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

               lowercase__					:  Dict          =							pt_model_class(UpperCAmelCase_    ).eval()
               lowercase__					:  Any          =							False
               lowercase__					:  Union[str, Any]          =							model_class(UpperCAmelCase_      ,		dtype=jnp.floataa    )

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

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

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

               lowercase__					:  Optional[int]          =							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__					:  Union[str, Any]          =							pt_model_class.from_pretrained(UpperCAmelCase_      ,		from_flax=UpperCAmelCase_    )

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






"""simple docstring"""

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a__			:						Dict							=			logging.get_logger(__name__)

a__			:						Any							=			{
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}


class       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  'longt5'
						__snake_case			      =  ['past_key_values']
						__snake_case			      =  {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(self  ,				UpperCAmelCase_=3_2_1_2_8  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=6_4  ,				UpperCAmelCase_=2_0_4_8  ,				UpperCAmelCase_=6  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=8  ,				UpperCAmelCase_=1_2_7  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=1_2_8  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=1e-6  ,				UpperCAmelCase_=1.0  ,				UpperCAmelCase_="relu"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_="local"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=1  ,				**UpperCAmelCase_  ,				)      ->    int:
											__lowercase        =      vocab_size
											__lowercase        =      d_model
											__lowercase        =      d_kv
											__lowercase        =      d_ff
											__lowercase        =      num_layers
											# default = symmetry
											__lowercase        =      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__lowercase        =      num_heads
											__lowercase        =      local_radius
											__lowercase        =      global_block_size
											__lowercase        =      relative_attention_num_buckets
											__lowercase        =      relative_attention_max_distance
											__lowercase        =      dropout_rate
											__lowercase        =      layer_norm_epsilon
											__lowercase        =      initializer_factor
											__lowercase        =      feed_forward_proj
											__lowercase        =      encoder_attention_type
											__lowercase        =      use_cache

											__lowercase        =      self.feed_forward_proj.split('''-'''				)
											__lowercase        =      act_info[-1]
											__lowercase        =      act_info[0] == '''gated'''

											if len(UpperCAmelCase_				) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_				) > 2:
																raise ValueError(
																    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																    '''\'gated-gelu\' or \'relu\''''				)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																__lowercase        =      '''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_  ,				eos_token_id=UpperCAmelCase_  ,				is_encoder_decoder=UpperCAmelCase_  ,				**UpperCAmelCase_  ,				)



class       _snake_case  (    UpperCamelCase							):







						@property
						def 							_UpperCamelCase     (self				)      ->    Mapping[str, Mapping[int, str]]:
											__lowercase        =      {
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																__lowercase        =      '''past_encoder_sequence + sequence'''
																__lowercase        =      {0: '''batch'''}
																__lowercase        =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
																self.fill_with_past_key_values_(UpperCAmelCase_  ,				direction='''inputs'''				)

											return common_inputs







						@property
						def 							_UpperCamelCase     (self				)      ->    int:
											return 1_3


