



"""simple docstring"""
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch
class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Tuple						,					snake_case:							Tuple						,					snake_case:							Tuple=14						,					snake_case:							Tuple=7						,					snake_case:							Optional[Any]=True						,					snake_case:							Tuple=True						,					snake_case:							int=False						,					snake_case:							Dict=True						,					snake_case:							Optional[int]=99						,					snake_case:							Union[str, Any]=32						,					snake_case:							Tuple=4						,					snake_case:							Tuple=4						,					snake_case:							Tuple=[[["global", "local"], 2]]						,					snake_case:							Optional[Any]=37						,					snake_case:							List[Any]="gelu"						,					snake_case:							Optional[int]=0.1						,					snake_case:							Optional[Any]=0.1						,					snake_case:							List[str]=512						,					snake_case:							Union[str, Any]=7						,					snake_case:							int=0.0_2						,					)       ->     Dict:
											snake_case_			:Optional[int]						=      parent
											snake_case_			:str						=      batch_size
											snake_case_			:Union[str, Any]						=      seq_length
											snake_case_			:int						=      is_training
											snake_case_			:Optional[Any]						=      use_input_mask
											snake_case_			:Union[str, Any]						=      use_token_type_ids
											snake_case_			:Tuple						=      use_labels
											snake_case_			:int						=      vocab_size
											snake_case_			:List[Any]						=      hidden_size
											snake_case_			:List[Any]						=      num_hidden_layers
											snake_case_			:Any						=      num_attention_heads
											snake_case_			:Optional[int]						=      attention_types
											snake_case_			:List[str]						=      intermediate_size
											snake_case_			:Optional[int]						=      hidden_act
											snake_case_			:Optional[Any]						=      hidden_dropout_prob
											snake_case_			:str						=      attention_probs_dropout_prob
											snake_case_			:Any						=      max_position_embeddings
											snake_case_			:Optional[int]						=      window_size
											snake_case_			:Union[str, Any]						=      initializer_range
											snake_case_			:Tuple						=      None
											snake_case_			:Any						=      vocab_size - 1
											snake_case_			:List[Any]						=      vocab_size - 1
											snake_case_			:str						=      vocab_size - 1






					def        lowerCAmelCase_    (	self:							Any						)       ->     str:
											snake_case_			:Union[str, Any]						=      ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size						)

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

											snake_case_			:List[Any]						=      GPTNeoConfig(
											    vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					num_layers=self.num_hidden_layers						,					num_heads=self.num_attention_heads						,					max_position_embeddings=self.max_position_embeddings						,					use_cache=snake_case						,					bos_token_id=self.bos_token_id						,					eos_token_id=self.eos_token_id						,					pad_token_id=self.pad_token_id						,					window_size=self.window_size						,					attention_types=self.attention_types						,					)

											return (config, input_ids, input_mask)






					def        lowerCAmelCase_    (	self:							Any						)       ->     Any:
											snake_case_			:Tuple						=      self.prepare_config_and_inputs()
											snake_case_,	snake_case_,	snake_case_			:int						=      config_and_inputs
											snake_case_			:Optional[Any]						=      {"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






					def        lowerCAmelCase_    (	self:							Tuple						,					snake_case:							Any						,					snake_case:							Optional[Any]						,					snake_case:							Union[str, Any]						,					snake_case:							Any						)       ->     List[Any]:
											snake_case_			:Dict						=      20
											snake_case_			:Optional[Any]						=      model_class_name(snake_case						)

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

											snake_case_			:List[str]						=      jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)
											snake_case_			:List[str]						=      model(
											    input_ids[:, :-1]						,					attention_mask=snake_case						,					past_key_values=snake_case						,					position_ids=snake_case						,					)

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

											snake_case_			:str						=      model(snake_case						)

											snake_case_			:List[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}"""						)






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							Tuple						,					snake_case:							List[Any]						,					snake_case:							int						,					snake_case:							Optional[int]						)       ->     Tuple:
											snake_case_			:Any						=      20
											snake_case_			:Union[str, Any]						=      model_class_name(snake_case						)

											snake_case_			:Optional[int]						=      jnp.concatenate(
											    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]						,					axis=-1						,					)

											snake_case_			:int						=      model.init_cache(input_ids.shape[0]						,					snake_case						)
											snake_case_			:Union[str, Any]						=      jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)

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

											snake_case_			:Dict						=      model(snake_case						,					attention_mask=snake_case						)

											snake_case_			: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_flax
class     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Optional[Any]						=					(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					_A		:      Optional[Any]						=					(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()






					def        lowerCAmelCase_    (	self:							int						)       ->     Union[str, Any]:
											snake_case_			:Any						=      FlaxGPTNeoModelTester(self						)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Tuple:
											for model_class_name in self.all_model_classes:
																	snake_case_,	snake_case_,	snake_case_			:Dict						=      self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(snake_case						,					snake_case						,					snake_case						,					snake_case						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     Any:
											for model_class_name in self.all_model_classes:
																	snake_case_,	snake_case_,	snake_case_			:Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    snake_case						,					snake_case						,					snake_case						,					snake_case						)






					@slow
					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											snake_case_			:Tuple						=      GPTaTokenizer.from_pretrained("""gpt2"""						,					pad_token="""<|endoftext|>"""						,					padding_side="""left"""						)
											snake_case_			:Optional[Any]						=      tokenizer(["""Hello this is a long string""", """Hey"""]						,					return_tensors="""np"""						,					padding=snake_case						,					truncation=snake_case						)

											snake_case_			:int						=      FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""						)
											snake_case_			:List[str]						=      False
											snake_case_			:Union[str, Any]						=      model.config.eos_token_id

											snake_case_			:List[str]						=      jax.jit(model.generate						)

											snake_case_			:Tuple						=      jit_generate(
											    inputs["""input_ids"""]						,					attention_mask=inputs["""attention_mask"""]						,					pad_token_id=tokenizer.pad_token_id						).sequences

											snake_case_			:Union[str, Any]						=      tokenizer.batch_decode(snake_case						,					skip_special_tokens=snake_case						)

											snake_case_			:Dict						=      [
											    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
											    """Hey, I'm a little late to the party. I'm going to""",
											]

											self.assertListEqual(snake_case						,					snake_case						)






					@is_pt_flax_cross_test
					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     Optional[int]:
											snake_case_,	snake_case_			:Optional[int]						=      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
																							snake_case_			:Optional[Any]						=      self._prepare_for_class(snake_case						,					snake_case						)
																							snake_case_			:List[str]						=      {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

																							snake_case_,	snake_case_			:List[str]						=      pt_inputs["""input_ids"""].shape
																							snake_case_			:str						=      np.random.randint(0						,					seq_length - 1						,					size=(batch_size,)						)
																							for batch_idx, start_index in enumerate(snake_case						):
																													snake_case_			:int						=      0
																													snake_case_			:List[str]						=      1
																													snake_case_			:int						=      0
																													snake_case_			:List[str]						=      1
																							snake_case_			:Tuple						=      pt_model_class(snake_case						).eval()
																							snake_case_			:Union[str, Any]						=      model_class(snake_case						,					dtype=jnp.floataa						)

																							snake_case_			:Union[str, Any]						=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,					snake_case						)
																							snake_case_			:Dict						=      fx_state

																							with torch.no_grad():
																													snake_case_			:List[str]						=      pt_model(**snake_case						).to_tuple()

																							snake_case_			:int						=      fx_model(**snake_case						).to_tuple()
																							self.assertEqual(len(snake_case						)						,					len(snake_case						)						,					"""Output lengths differ between Flax and PyTorch"""						)
																							for fx_output, pt_output in zip(snake_case						,					snake_case						):
																													self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4E-2						)

																							with tempfile.TemporaryDirectory() as tmpdirname:
																													pt_model.save_pretrained(snake_case						)
																													snake_case_			:Dict						=      model_class.from_pretrained(snake_case						,					from_pt=snake_case						)

																							snake_case_			:Any						=      fx_model_loaded(**snake_case						).to_tuple()
																							self.assertEqual(
																							    len(snake_case						)						,					len(snake_case						)						,					"""Output lengths differ between Flax and PyTorch"""						)
																							for fx_output_loaded, pt_output in zip(snake_case						,					snake_case						):
																													self.assert_almost_equals(fx_output_loaded[:, -1]						,					pt_output[:, -1].numpy()						,					4E-2						)






					@is_pt_flax_cross_test
					def        lowerCAmelCase_    (	self:							str						)       ->     Any:
											snake_case_,	snake_case_			:Tuple						=      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
																							snake_case_			:int						=      self._prepare_for_class(snake_case						,					snake_case						)
																							snake_case_			:Optional[int]						=      {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

																							snake_case_			:Dict						=      pt_model_class(snake_case						).eval()
																							snake_case_			:Tuple						=      model_class(snake_case						,					dtype=jnp.floataa						)

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

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

																							with torch.no_grad():
																													snake_case_			:Tuple						=      pt_model(**snake_case						).to_tuple()

																							snake_case_			:Any						=      fx_model(**snake_case						).to_tuple()
																							self.assertEqual(len(snake_case						)						,					len(snake_case						)						,					"""Output lengths differ between Flax and PyTorch"""						)
																							for fx_output, pt_output in zip(snake_case						,					snake_case						):
																													self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4E-2						)

																							with tempfile.TemporaryDirectory() as tmpdirname:
																													fx_model.save_pretrained(snake_case						)
																													snake_case_			:Optional[int]						=      pt_model_class.from_pretrained(snake_case						,					from_flax=snake_case						)

																							with torch.no_grad():
																													snake_case_			:Any						=      pt_model_loaded(**snake_case						).to_tuple()

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






					@slow
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[Any]:
											for model_class_name in self.all_model_classes:
																	snake_case_			:List[Any]						=      model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""						)
																	snake_case_			:Optional[int]						=      model(np.ones((1, 1)						)						)
																	self.assertIsNotNone(snake_case						)





"""simple docstring"""
from typing import Mapping

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


__a     =    logging.get_logger(__name__)

__a     =    {
    "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     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[Any]						=					"""longt5"""
					_A		:      Dict						=					["""past_key_values"""]
					_A		:      int						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






					def __init__(	self:							str						,					snake_case:							List[Any]=32_128						,					snake_case:							Optional[int]=512						,					snake_case:							Union[str, Any]=64						,					snake_case:							str=2_048						,					snake_case:							Any=6						,					snake_case:							Optional[Any]=None						,					snake_case:							str=8						,					snake_case:							Tuple=127						,					snake_case:							Any=16						,					snake_case:							int=32						,					snake_case:							int=128						,					snake_case:							int=0.1						,					snake_case:							Optional[int]=1E-6						,					snake_case:							List[Any]=1.0						,					snake_case:							Optional[int]="relu"						,					snake_case:							str=True						,					snake_case:							Optional[int]="local"						,					snake_case:							Any=True						,					snake_case:							Tuple=0						,					snake_case:							Any=1						,					**snake_case:							Any						,					)       ->     List[str]:
											snake_case_			:Any						=      vocab_size
											snake_case_			:List[Any]						=      d_model
											snake_case_			:Any						=      d_kv
											snake_case_			:int						=      d_ff
											snake_case_			:Tuple						=      num_layers
											# default = symmetry
											snake_case_			:str						=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											snake_case_			:Any						=      num_heads
											snake_case_			:str						=      local_radius
											snake_case_			:Optional[int]						=      global_block_size
											snake_case_			:Union[str, Any]						=      relative_attention_num_buckets
											snake_case_			:List[str]						=      relative_attention_max_distance
											snake_case_			:Dict						=      dropout_rate
											snake_case_			:str						=      layer_norm_epsilon
											snake_case_			:str						=      initializer_factor
											snake_case_			:Any						=      feed_forward_proj
											snake_case_			:List[str]						=      encoder_attention_type
											snake_case_			:List[str]						=      use_cache

											snake_case_			:Tuple						=      self.feed_forward_proj.split("""-"""						)
											snake_case_			:Any						=      act_info[-1]
											snake_case_			:Union[str, Any]						=      act_info[0] == """gated"""

											if len(snake_case						) > 1 and act_info[0] != "gated" or len(snake_case						) > 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":
																	snake_case_			:Optional[int]						=      """gelu_new"""

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

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					@property
					def        lowerCAmelCase_    (	self:							Dict						)       ->     Mapping[str, Mapping[int, str]]:
											snake_case_			:Dict						=      {
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
																	snake_case_			:List[Any]						=      """past_encoder_sequence + sequence"""
																	snake_case_			:List[Any]						=      {0: """batch"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																	snake_case_			:List[str]						=      {0: """batch""", 1: """decoder_sequence"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
																	self.fill_with_past_key_values_(snake_case						,					direction="""inputs"""						)

											return common_inputs






					@property
					def        lowerCAmelCase_    (	self:							List[str]						)       ->     int:
											return 13
