

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      _SCREAMING_SNAKE_CASE							 =			'''platform'''

      import jax
      import jax.numpy as jnp

      from transformers import AutoTokenizer
      from transformers.models.mbart.modeling_flax_mbart import (
          FlaxMBartForConditionalGeneration,
          FlaxMBartForQuestionAnswering,
          FlaxMBartForSequenceClassification,
          FlaxMBartModel,
          shift_tokens_right,
      )





def  _snake_case		(snake_case_					:     Dict    ,       snake_case_					:     Dict    ,       snake_case_					:     Union[str, Any]=None    ,       snake_case_					:     Tuple=None    ,       snake_case_					:     Optional[Any]=None    ,       snake_case_					:     str=None    ,       snake_case_					:     List[Any]=None    ,       snake_case_					:     Any=None    ,       )							->		Tuple:
  if attention_mask is None:
    __UpperCAmelCase	:  Tuple		=     np.where(input_ids != config.pad_token_id    ,       1    ,       0  )
  if decoder_attention_mask is None:
    __UpperCAmelCase	:  List[Any]		=     np.where(decoder_input_ids != config.pad_token_id    ,       1    ,       0  )
  if head_mask is None:
    __UpperCAmelCase	:  int		=     np.ones((config.encoder_layers, config.encoder_attention_heads)  )
  if decoder_head_mask is None:
    __UpperCAmelCase	:  Optional[Any]		=     np.ones((config.decoder_layers, config.decoder_attention_heads)  )
  if cross_attn_head_mask is None:
    __UpperCAmelCase	:  Optional[Any]		=     np.ones((config.decoder_layers, config.decoder_attention_heads)  )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }


class      lowerCamelCase_ (			unittest.TestCase):




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=99  ,  _UpperCAmelCase=16  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=32  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=1  ,  _UpperCAmelCase=0  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=0.0_2  ,  ):
   __UpperCAmelCase	:  Any		=     parent
   __UpperCAmelCase	:  List[str]		=     batch_size
   __UpperCAmelCase	:  int		=     seq_length
   __UpperCAmelCase	:  Dict		=     is_training
   __UpperCAmelCase	:  List[Any]		=     use_labels
   __UpperCAmelCase	:  Union[str, Any]		=     vocab_size
   __UpperCAmelCase	:  Optional[Any]		=     hidden_size
   __UpperCAmelCase	:  Any		=     num_hidden_layers
   __UpperCAmelCase	:  Optional[int]		=     num_attention_heads
   __UpperCAmelCase	:  Optional[int]		=     intermediate_size
   __UpperCAmelCase	:  Optional[int]		=     hidden_act
   __UpperCAmelCase	:  Dict		=     hidden_dropout_prob
   __UpperCAmelCase	:  Optional[int]		=     attention_probs_dropout_prob
   __UpperCAmelCase	:  List[Any]		=     max_position_embeddings
   __UpperCAmelCase	:  List[str]		=     eos_token_id
   __UpperCAmelCase	:  Union[str, Any]		=     pad_token_id
   __UpperCAmelCase	:  str		=     bos_token_id
   __UpperCAmelCase	:  str		=     decoder_start_token_id
   __UpperCAmelCase	:  int		=     initializer_range




 def     a       (			self							):
   __UpperCAmelCase	:  Any		=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,  self.vocab_size							)  ,  3  ,  self.vocab_size							)
   __UpperCAmelCase	:  Tuple		=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,  dtype=np.intaa							))  ,  -1							)

   __UpperCAmelCase	:  List[Any]		=     shift_tokens_right(__lowercase  ,  1							)

   __UpperCAmelCase	:  int		=     MBartConfig(
       vocab_size=self.vocab_size  ,  d_model=self.hidden_size  ,  encoder_layers=self.num_hidden_layers  ,  decoder_layers=self.num_hidden_layers  ,  encoder_attention_heads=self.num_attention_heads  ,  decoder_attention_heads=self.num_attention_heads  ,  encoder_ffn_dim=self.intermediate_size  ,  decoder_ffn_dim=self.intermediate_size  ,  dropout=self.hidden_dropout_prob  ,  attention_dropout=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  eos_token_id=self.eos_token_id  ,  bos_token_id=self.bos_token_id  ,  pad_token_id=self.pad_token_id  ,  decoder_start_token_id=self.decoder_start_token_id  ,  initializer_range=self.initializer_range  ,  use_cache=__lowercase  ,  )
   __UpperCAmelCase	:  Tuple		=     prepare_mbart_inputs_dict(__lowercase  ,  __lowercase  ,  __lowercase							)
   return config, inputs_dict




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     self.prepare_config_and_inputs()
   return config, inputs_dict




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     20
   __UpperCAmelCase	:  Dict		=     model_class_name(__lowercase							)

   __UpperCAmelCase	:  Tuple		=     model.encode(inputs_dict['input_ids']							)

   __UpperCAmelCase	:  Union[str, Any]		=     (
       inputs_dict['''decoder_input_ids'''],
       inputs_dict['''decoder_attention_mask'''],
   )

   __UpperCAmelCase	:  List[str]		=     model.init_cache(decoder_input_ids.shape[0]  ,  __lowercase  ,  __lowercase							)
   __UpperCAmelCase	:  List[str]		=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,  dtype='i4'							)

   __UpperCAmelCase	:  Union[str, Any]		=     jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]  ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,  )
   __UpperCAmelCase	:  Optional[int]		=     model.decode(
       decoder_input_ids[:, :-1]  ,  __lowercase  ,  decoder_attention_mask=__lowercase  ,  past_key_values=__lowercase  ,  decoder_position_ids=__lowercase  ,  )

   __UpperCAmelCase	:  str		=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,  dtype='i4'							)
   __UpperCAmelCase	:  int		=     model.decode(
       decoder_input_ids[:, -1:]  ,  __lowercase  ,  decoder_attention_mask=__lowercase  ,  past_key_values=outputs_cache.past_key_values  ,  decoder_position_ids=__lowercase  ,  )

   __UpperCAmelCase	:  List[str]		=     model.decode(__lowercase  ,  __lowercase							)

   __UpperCAmelCase	:  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  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Optional[Any]		=     20
   __UpperCAmelCase	:  Union[str, Any]		=     model_class_name(__lowercase							)

   __UpperCAmelCase	:  Union[str, Any]		=     model.encode(inputs_dict['input_ids']							)

   __UpperCAmelCase	:  str		=     (
       inputs_dict['''decoder_input_ids'''],
       inputs_dict['''decoder_attention_mask'''],
   )

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

   __UpperCAmelCase	:  int		=     model.init_cache(decoder_input_ids.shape[0]  ,  __lowercase  ,  __lowercase							)
   __UpperCAmelCase	:  Dict		=     jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]  ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,  )

   __UpperCAmelCase	:  int		=     model.decode(
       decoder_input_ids[:, :-1]  ,  __lowercase  ,  decoder_attention_mask=__lowercase  ,  past_key_values=__lowercase  ,  decoder_position_ids=__lowercase  ,  )
   __UpperCAmelCase	:  Optional[int]		=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,  dtype='i4'							)
   __UpperCAmelCase	:  Optional[int]		=     model.decode(
       decoder_input_ids[:, -1:]  ,  __lowercase  ,  past_key_values=outputs_cache.past_key_values  ,  decoder_attention_mask=__lowercase  ,  decoder_position_ids=__lowercase  ,  )

   __UpperCAmelCase	:  List[str]		=     model.decode(__lowercase  ,  __lowercase  ,  decoder_attention_mask=__lowercase							)

   __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}"							)



@require_flax
class      lowerCamelCase_ (			unittest.TestCase):
 _lowerCAmelCase									=	99




 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     np.array(
       [
           [71, 82, 18, 33, 46, 91, 2],
           [68, 34, 26, 58, 30, 82, 2],
           [5, 97, 17, 39, 94, 40, 2],
           [76, 83, 94, 25, 70, 78, 2],
           [87, 59, 41, 35, 48, 66, 2],
           [55, 13, 16, 58, 5, 2, 1],  # note padding
           [64, 27, 31, 51, 12, 75, 2],
           [52, 64, 86, 17, 83, 39, 2],
           [48, 61, 9, 24, 71, 82, 2],
           [26, 1, 60, 48, 22, 13, 2],
           [21, 5, 62, 28, 14, 76, 2],
           [45, 98, 37, 86, 59, 48, 2],
           [70, 70, 50, 9, 28, 0, 2],
       ]  ,  dtype=np.intaa  ,  )

   __UpperCAmelCase	:  Optional[int]		=     input_ids.shape[0]
   __UpperCAmelCase	:  Optional[int]		=     MBartConfig(
       vocab_size=self.vocab_size  ,  d_model=24  ,  encoder_layers=2  ,  decoder_layers=2  ,  encoder_attention_heads=2  ,  decoder_attention_heads=2  ,  encoder_ffn_dim=32  ,  decoder_ffn_dim=32  ,  max_position_embeddings=48  ,  eos_token_id=2  ,  pad_token_id=1  ,  bos_token_id=0  ,  )
   return config, input_ids, batch_size




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     self._get_config_and_data()
   __UpperCAmelCase	:  Union[str, Any]		=     FlaxMBartForSequenceClassification(__lowercase							)
   __UpperCAmelCase	:  int		=     model(input_ids=__lowercase  ,  decoder_input_ids=__lowercase							)
   __UpperCAmelCase	:  Dict		=     (batch_size, config.num_labels)
   self.assertEqual(outputs['logits'].shape  ,  __lowercase							)




 def     a       (			self							):
   __UpperCAmelCase	:  Any		=     self._get_config_and_data()
   __UpperCAmelCase	:  List[Any]		=     FlaxMBartForQuestionAnswering(__lowercase							)
   __UpperCAmelCase	:  Union[str, Any]		=     model(input_ids=__lowercase							)

   self.assertEqual(outputs['start_logits'].shape  ,  input_ids.shape							)
   self.assertEqual(outputs['end_logits'].shape  ,  input_ids.shape							)




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     self._get_config_and_data()
   __UpperCAmelCase	:  Any		=     FlaxMBartForConditionalGeneration(__lowercase							)
   __UpperCAmelCase	:  Any		=     lm_model(input_ids=__lowercase							)
   __UpperCAmelCase	:  List[str]		=     (batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs['logits'].shape  ,  __lowercase							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     MBartConfig(
       vocab_size=self.vocab_size  ,  d_model=14  ,  encoder_layers=2  ,  decoder_layers=2  ,  encoder_attention_heads=2  ,  decoder_attention_heads=2  ,  encoder_ffn_dim=8  ,  decoder_ffn_dim=8  ,  max_position_embeddings=48  ,  )
   __UpperCAmelCase	:  int		=     FlaxMBartForConditionalGeneration(__lowercase							)
   __UpperCAmelCase	:  str		=     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,  dtype=np.intaa							)
   __UpperCAmelCase	:  str		=     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,  dtype=np.intaa							)
   __UpperCAmelCase	:  Any		=     lm_model(input_ids=__lowercase  ,  decoder_input_ids=__lowercase							)
   __UpperCAmelCase	:  List[str]		=     (*summary.shape, config.vocab_size)
   self.assertEqual(outputs['logits'].shape  ,  __lowercase							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,  dtype=np.intaa							)
   __UpperCAmelCase	:  List[str]		=     shift_tokens_right(__lowercase  ,  1							)
   __UpperCAmelCase	:  str		=     np.equal(__lowercase  ,  1							).astype(np.floataa							).sum()
   __UpperCAmelCase	:  List[str]		=     np.equal(__lowercase  ,  1							).astype(np.floataa							).sum()
   self.assertEqual(shifted.shape  ,  input_ids.shape							)
   self.assertEqual(__lowercase  ,  n_pad_before - 1							)
   self.assertTrue(np.equal(shifted[:, 0]  ,  2							).all()							)



@require_flax
class      lowerCamelCase_ (			_A	,				unittest.TestCase	,				_A):
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	(
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 _lowerCAmelCase									=	(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     FlaxMBartModelTester(self							)




 def     a       (			self							):
   __UpperCAmelCase	:  Any		=     self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(__lowercase  ,  __lowercase  ,  __lowercase							)




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(__lowercase  ,  __lowercase  ,  __lowercase							)




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__							):
       __UpperCAmelCase	:  Tuple		=     self._prepare_for_class(__lowercase  ,  __lowercase							)
       __UpperCAmelCase	:  Any		=     model_class(__lowercase							)

       @jax.jit
       def encode_jitted(_UpperCAmelCase  ,  _UpperCAmelCase=None  ,  **_UpperCAmelCase							):
         return model.encode(input_ids=__lowercase  ,  attention_mask=__lowercase							)

       with self.subTest('JIT Enabled'							):
         __UpperCAmelCase	:  Dict		=     encode_jitted(**__lowercase							).to_tuple()

       with self.subTest('JIT Disabled'							):
         with jax.disable_jit():
           __UpperCAmelCase	:  str		=     encode_jitted(**__lowercase							).to_tuple()

       self.assertEqual(len(__lowercase							)  ,  len(__lowercase							)							)
       for jitted_output, output in zip(__lowercase  ,  __lowercase							):
         self.assertEqual(jitted_output.shape  ,  output.shape							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__							):
       __UpperCAmelCase	:  int		=     model_class(__lowercase							)
       __UpperCAmelCase	:  Dict		=     model.encode(inputs_dict['input_ids']  ,  inputs_dict['attention_mask']							)

       __UpperCAmelCase	:  str		=     {
           '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
           '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
           '''encoder_outputs''': encoder_outputs,
       }

       @jax.jit
       def decode_jitted(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
         return model.decode(
             decoder_input_ids=__lowercase  ,  decoder_attention_mask=__lowercase  ,  encoder_outputs=__lowercase  ,  )

       with self.subTest('JIT Enabled'							):
         __UpperCAmelCase	:  Dict		=     decode_jitted(**__lowercase							).to_tuple()

       with self.subTest('JIT Disabled'							):
         with jax.disable_jit():
           __UpperCAmelCase	:  List[Any]		=     decode_jitted(**__lowercase							).to_tuple()

       self.assertEqual(len(__lowercase							)  ,  len(__lowercase							)							)
       for jitted_output, output in zip(__lowercase  ,  __lowercase							):
         self.assertEqual(jitted_output.shape  ,  output.shape							)




 @slow
 def     a       (			self							):
   for model_class_name in self.all_model_classes:
     __UpperCAmelCase	:  Dict		=     model_class_name.from_pretrained('facebook/mbart-large-cc25'  ,  from_pt=__lowercase							)
     # FlaxMBartForSequenceClassification expects eos token in input_ids
     __UpperCAmelCase	:  Optional[int]		=     np.ones((1, 1)							) * model.config.eos_token_id
     __UpperCAmelCase	:  Optional[int]		=     model(__lowercase							)
     self.assertIsNotNone(__lowercase							)



@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCamelCase_ (			unittest.TestCase):
 _lowerCAmelCase									=	[
     " UN Chief Says There Is No Military Solution in Syria",
 ]
 _lowerCAmelCase									=	[
     "Şeful ONU declară că nu există o soluţie militară în Siria",
 ]
 _lowerCAmelCase									=	"facebook/mbart-large-en-ro"




 @cached_property
 def     a       (			self							):
   return AutoTokenizer.from_pretrained(self.model_name							)




 @cached_property
 def     a       (			self							):
   __UpperCAmelCase	:  Dict		=     FlaxMBartForConditionalGeneration.from_pretrained(self.model_name  ,  from_pt=__lowercase							)
   return model




 def     a       (			self  ,  **_UpperCAmelCase							):
   __UpperCAmelCase	:  str		=     self.translate_src_text(**__lowercase							)
   self.assertListEqual(self.expected_text  ,  __lowercase							)




 def     a       (			self  ,  **_UpperCAmelCase							):
   __UpperCAmelCase	:  int		=     self.tokenizer(self.src_text  ,  **__lowercase  ,  return_tensors='np'							)
   __UpperCAmelCase	:  str		=     self.model.generate(
       model_inputs.input_ids  ,  attention_mask=model_inputs.attention_mask  ,  decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']  ,  early_stopping=__lowercase  ,  num_beams=2  ,  ).sequences
   __UpperCAmelCase	:  int		=     self.tokenizer.batch_decode(__lowercase  ,  skip_special_tokens=__lowercase							)
   return generated_words




 @slow
 def     a       (			self							):
   self._assert_generated_batch_equal_expected()












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 							snake_case_		:







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_4      ,  __lowercase=7      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=False      ,  __lowercase=True      ,  __lowercase=9_9      ,  __lowercase=3_2      ,  __lowercase=4      ,  __lowercase=4      ,  __lowercase=[[["global", "local"], 2]]      ,  __lowercase=3_7      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=7      ,  __lowercase=0.0_2      ,  )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=parent
				lowerCamelCase :			Optional[int]    		=batch_size
				lowerCamelCase :			Any    		=seq_length
				lowerCamelCase :			Optional[Any]    		=is_training
				lowerCamelCase :			Any    		=use_input_mask
				lowerCamelCase :			int    		=use_token_type_ids
				lowerCamelCase :			int    		=use_labels
				lowerCamelCase :			str    		=vocab_size
				lowerCamelCase :			Optional[int]    		=hidden_size
				lowerCamelCase :			int    		=num_hidden_layers
				lowerCamelCase :			List[Any]    		=num_attention_heads
				lowerCamelCase :			Any    		=attention_types
				lowerCamelCase :			List[str]    		=intermediate_size
				lowerCamelCase :			Dict    		=hidden_act
				lowerCamelCase :			Union[str, Any]    		=hidden_dropout_prob
				lowerCamelCase :			Optional[int]    		=attention_probs_dropout_prob
				lowerCamelCase :			Any    		=max_position_embeddings
				lowerCamelCase :			int    		=window_size
				lowerCamelCase :			List[Any]    		=initializer_range
				lowerCamelCase :			Any    		=None
				lowerCamelCase :			List[Any]    		=vocab_size - 1
				lowerCamelCase :			str    		=vocab_size - 1
				lowerCamelCase :			int    		=vocab_size - 1







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			List[Any]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

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

				lowerCamelCase :			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=__lowercase      ,  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       __lowercase (		self )					-> Any:
				lowerCamelCase :			List[Any]    		=self.prepare_config_and_inputs()
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[Any]    		=config_and_inputs
				lowerCamelCase :			List[str]    		={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> List[Any]:
				lowerCamelCase :			Tuple    		=2_0
				lowerCamelCase :			Union[str, Any]    		=model_class_name(__lowercase )

				lowerCamelCase :			List[Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			str    		=jnp.ones((input_ids.shape[0], max_decoder_length)      ,  dtype='''i4''' )

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

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

				lowerCamelCase :			Any    		=model(__lowercase )

				lowerCamelCase :			Dict    		=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       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> Dict:
				lowerCamelCase :			str    		=2_0
				lowerCamelCase :			Optional[int]    		=model_class_name(__lowercase )

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

				lowerCamelCase :			Union[str, Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			List[Any]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

				lowerCamelCase :			Optional[Any]    		=model(
				    input_ids[:, :-1]      ,  attention_mask=__lowercase      ,  past_key_values=__lowercase      ,  position_ids=__lowercase      ,  )
				lowerCamelCase :			List[Any]    		=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='''i4''' )
				lowerCamelCase :			Union[str, Any]    		=model(
				    input_ids[:, -1:]      ,  past_key_values=outputs_cache.past_key_values      ,  attention_mask=__lowercase      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			Union[str, Any]    		=model(__lowercase      ,  attention_mask=__lowercase )

				lowerCamelCase :			Dict    		=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 							snake_case_		(      _A    ,       _A    ,       unittest.TestCase):
		lowerCamelCase     :List[Any]							=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		lowerCamelCase     :Union[str, Any]							=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			str    		=FlaxGPTNeoModelTester(self )







		def       __lowercase (		self )					-> Dict:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Dict    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward(__lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		def       __lowercase (		self )					-> Tuple:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[int]    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward_with_attn_mask(
						    __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		@slow
		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Tuple    		=GPTaTokenizer.from_pretrained('''gpt2'''      ,  pad_token='''<|endoftext|>'''      ,  padding_side='''left''' )
				lowerCamelCase :			Any    		=tokenizer(['''Hello this is a long string''', '''Hey''']      ,  return_tensors='''np'''      ,  padding=__lowercase      ,  truncation=__lowercase )

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

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

				lowerCamelCase :			Optional[int]    		=jit_generate(
				    inputs['''input_ids''']      ,  attention_mask=inputs['''attention_mask''']      ,  pad_token_id=tokenizer.pad_token_id ).sequences

				lowerCamelCase :			Any    		=tokenizer.batch_decode(__lowercase      ,  skip_special_tokens=__lowercase )

				lowerCamelCase :			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(__lowercase      ,  __lowercase )







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

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

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

								lowerCamelCase :			Union[str, Any]    		=convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,  __lowercase )
								lowerCamelCase :			Optional[int]    		=fx_state

								with torch.no_grad():
										lowerCamelCase :			List[Any]    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			List[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

								with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(__lowercase )
										lowerCamelCase :			List[str]    		=model_class.from_pretrained(__lowercase      ,  from_pt=__lowercase )

								lowerCamelCase :			List[str]    		=fx_model_loaded(**__lowercase ).to_tuple()
								self.assertEqual(
								    len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output_loaded, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output_loaded[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )







		@is_pt_flax_cross_test
		def       __lowercase (		self )					-> str:
				lowerCamelCase		, lowerCamelCase :			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
								lowerCamelCase :			Tuple    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Any    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

								lowerCamelCase :			List[str]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Union[str, Any]    		=model_class(__lowercase      ,  dtype=jnp.floataa )

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

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

								with torch.no_grad():
										lowerCamelCase :			Tuple    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			Optional[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

								with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(__lowercase )
										lowerCamelCase :			Union[str, Any]    		=pt_model_class.from_pretrained(__lowercase      ,  from_flax=__lowercase )

								with torch.no_grad():
										lowerCamelCase :			str    		=pt_model_loaded(**__lowercase ).to_tuple()

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







		@slow
		def       __lowercase (		self )					-> Any:
				for model_class_name in self.all_model_classes:
						lowerCamelCase :			Optional[Any]    		=model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
						lowerCamelCase :			Union[str, Any]    		=model(np.ones((1, 1) ) )
						self.assertIsNotNone(__lowercase )

