'''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		:       Dict		,      A		:       int		,      A		:       str=14		,      A		:       str=7		,      A		:       Union[str, Any]=True		,      A		:       List[Any]=True		,      A		:       Any=False		,      A		:       Dict=True		,      A		:       Optional[int]=99		,      A		:       List[str]=32		,      A		:       Optional[Any]=4		,      A		:       Union[str, Any]=4		,      A		:       int=[[["global", "local"], 2]]		,      A		:       Any=37		,      A		:       Tuple="gelu"		,      A		:       Any=0.1		,      A		:       str=0.1		,      A		:       Dict=512		,      A		:       Union[str, Any]=7		,      A		:       Optional[int]=0.02		,      ):
             _UpperCAmelCase  :						List[Any]							   =					parent
             _UpperCAmelCase  :						str							   =					batch_size
             _UpperCAmelCase  :						Dict							   =					seq_length
             _UpperCAmelCase  :						str							   =					is_training
             _UpperCAmelCase  :						Optional[Any]							   =					use_input_mask
             _UpperCAmelCase  :						Any							   =					use_token_type_ids
             _UpperCAmelCase  :						Any							   =					use_labels
             _UpperCAmelCase  :						Dict							   =					vocab_size
             _UpperCAmelCase  :						Optional[Any]							   =					hidden_size
             _UpperCAmelCase  :						Any							   =					num_hidden_layers
             _UpperCAmelCase  :						List[str]							   =					num_attention_heads
             _UpperCAmelCase  :						Optional[int]							   =					attention_types
             _UpperCAmelCase  :						Tuple							   =					intermediate_size
             _UpperCAmelCase  :						Dict							   =					hidden_act
             _UpperCAmelCase  :						List[Any]							   =					hidden_dropout_prob
             _UpperCAmelCase  :						Optional[int]							   =					attention_probs_dropout_prob
             _UpperCAmelCase  :						str							   =					max_position_embeddings
             _UpperCAmelCase  :						List[Any]							   =					window_size
             _UpperCAmelCase  :						Union[str, Any]							   =					initializer_range
             _UpperCAmelCase  :						str							   =					None
             _UpperCAmelCase  :						Tuple							   =					vocab_size - 1
             _UpperCAmelCase  :						Union[str, Any]							   =					vocab_size - 1
             _UpperCAmelCase  :						str							   =					vocab_size - 1




      def   _A			( self		:       int ):
             _UpperCAmelCase  :						Optional[int]							   =					ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size )

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

             _UpperCAmelCase  :						Optional[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=A		,      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   _A			( self		:       str ):
             _UpperCAmelCase  :						Dict							   =					self.prepare_config_and_inputs()
             _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					config_and_inputs
             _UpperCAmelCase  :						Dict							   =					{"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict




      def   _A			( self		:       Dict		,      A		:       int		,      A		:       List[str]		,      A		:       Union[str, Any]		,      A		:       Union[str, Any] ):
             _UpperCAmelCase  :						Dict							   =					20
             _UpperCAmelCase  :						List[Any]							   =					model_class_name(A )

             _UpperCAmelCase  :						Tuple							   =					model.init_cache(input_ids.shape[0]		,      A )
             _UpperCAmelCase  :						Optional[int]							   =					jnp.ones((input_ids.shape[0], max_decoder_length)		,      dtype="i4" )

             _UpperCAmelCase  :						Optional[int]							   =					jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,      (input_ids.shape[0], input_ids.shape[-1] - 1) )
             _UpperCAmelCase  :						Optional[int]							   =					model(
                 input_ids[:, :-1]		,      attention_mask=A		,      past_key_values=A		,      position_ids=A		,      )

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

             _UpperCAmelCase  :						List[str]							   =					model(A )

             _UpperCAmelCase  :						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   _A			( self		:       List[Any]		,      A		:       Optional[int]		,      A		:       Dict		,      A		:       List[str]		,      A		:       Union[str, Any] ):
             _UpperCAmelCase  :						Union[str, Any]							   =					20
             _UpperCAmelCase  :						Dict							   =					model_class_name(A )

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

             _UpperCAmelCase  :						Tuple							   =					model.init_cache(input_ids.shape[0]		,      A )
             _UpperCAmelCase  :						Dict							   =					jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,      (input_ids.shape[0], input_ids.shape[-1] - 1) )

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

             _UpperCAmelCase  :						Dict							   =					model(A		,      attention_mask=A )

             _UpperCAmelCase  :						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_			(snake_case__							,				snake_case__							,				unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						str      			=		(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      __UpperCamelCase:						Any      			=		(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						Any							   =					FlaxGPTNeoModelTester(self )




      def   _A			( self		:       Union[str, Any] ):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(A		,      A		,      A		,      A )




      def   _A			( self		:       Tuple ):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						int							   =					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		:       Any ):
             _UpperCAmelCase  :						Optional[Any]							   =					GPTaTokenizer.from_pretrained("gpt2"		,      pad_token="<|endoftext|>"		,      padding_side="left" )
             _UpperCAmelCase  :						Union[str, Any]							   =					tokenizer(["Hello this is a long string", "Hey"]		,      return_tensors="np"		,      padding=A		,      truncation=A )

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

             _UpperCAmelCase  :						Optional[int]							   =					jax.jit(model.generate )

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

             _UpperCAmelCase  :						str							   =					tokenizer.batch_decode(A		,      skip_special_tokens=A )

             _UpperCAmelCase  :						List[str]							   =					[
                 "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(A		,      A )




      @is_pt_flax_cross_test
      def   _A			( self		:       Any ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						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
                           _UpperCAmelCase  :						List[Any]							   =					self._prepare_for_class(A		,      A )
                           _UpperCAmelCase  :						Optional[Any]							   =					{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  :						Optional[int]							   =					getattr(A		,      A )

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

                           _UpperCAmelCase  :						List[str]							   =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,      A )
                           _UpperCAmelCase  :						Union[str, Any]							   =					fx_state

                           with torch.no_grad():
                                  _UpperCAmelCase  :						str							   =					pt_model(**A ).to_tuple()

                           _UpperCAmelCase  :						Union[str, 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  :						Optional[Any]							   =					model_class.from_pretrained(A		,      from_pt=A )

                           _UpperCAmelCase  :						int							   =					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		:       int ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						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
                           _UpperCAmelCase  :						Any							   =					self._prepare_for_class(A		,      A )
                           _UpperCAmelCase  :						int							   =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                           _UpperCAmelCase  :						str							   =					pt_model_class(A ).eval()
                           _UpperCAmelCase  :						Optional[Any]							   =					model_class(A		,      dtype=jnp.floataa )

                           _UpperCAmelCase  :						Tuple							   =					load_flax_weights_in_pytorch_model(A		,      fx_model.params )
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						Tuple							   =					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  :						int							   =					0
                                  _UpperCAmelCase  :						Union[str, 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  :						Any							   =					pt_model(**A ).to_tuple()

                           _UpperCAmelCase  :						Tuple							   =					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  :						List[str]							   =					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   _A			( self		:       str ):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase  :						Union[str, Any]							   =					model_class_name.from_pretrained("EleutherAI/gpt-neo-125M" )
                    _UpperCAmelCase  :						Dict							   =					model(np.ones((1, 1) ) )
                    self.assertIsNotNone(A )


'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		"speech-encoder-decoder"
      __UpperCamelCase:						Optional[int]      			=		True




      def __init__( self		:       Optional[int]		,      **A		:       Tuple ):
             super().__init__(**A )
             if "encoder" not in kwargs or "decoder" not in kwargs:
                    raise ValueError(
                        F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
                        F""" `decoder` sub-configurations are passed, but only {kwargs}""" )

             _UpperCAmelCase  :						Dict							   =					kwargs.pop("encoder" )
             _UpperCAmelCase  :						Any							   =					encoder_config.pop("model_type" )
             _UpperCAmelCase  :						str							   =					kwargs.pop("decoder" )
             _UpperCAmelCase  :						int							   =					decoder_config.pop("model_type" )

             _UpperCAmelCase  :						List[Any]							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						str							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						Optional[int]							   =					True




      @classmethod
      def   _A			( cls		:       int		,      A		:       PretrainedConfig		,      A		:       PretrainedConfig		,      **A		:       Dict ):
             logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
             _UpperCAmelCase  :						Any							   =					True
             _UpperCAmelCase  :						Union[str, Any]							   =					True

             return cls(encoder=encoder_config.to_dict()		,      decoder=decoder_config.to_dict()		,      **A )





      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[str]							   =					copy.deepcopy(self.__dict__ )
             _UpperCAmelCase  :						Any							   =					self.encoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.decoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.__class__.model_type
             return output

