
import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowerCamelCase__     :		Dict        = logging.getLogger(__name__)



class    _snake_case (						UpperCAmelCase_				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: int     		=   self.layer[current_layer](SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	head_mask[current_layer])

   lowercase__	: List[Any]     		=   layer_outputs[0]

   return hidden_states



@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'	,    UpperCAmelCase_	,    )
class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   BertEncoderWithPabee(SCREAMING_SNAKE_CASE_)

   self.init_weights()
   lowercase__	: List[Any]     		=   0
   lowercase__	: Optional[int]     		=   0
   lowercase__	: Optional[Any]     		=   0

   lowercase__	: Optional[int]     		=   0


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   threshold


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   patience


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   0
   lowercase__	: Tuple     		=   0


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   self.inference_layers_num / self.inference_instances_num
   lowercase__	: Optional[Any]     		=   (
       f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
       f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
   )
   print(SCREAMING_SNAKE_CASE_)



 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_)
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=False       ,	):


   '''simple docstring'''




   if input_ids is not None and inputs_embeds is not None:
     raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
   elif input_ids is not None:
     lowercase__	: int     		=   input_ids.size()
   elif inputs_embeds is not None:
     lowercase__	: int     		=   inputs_embeds.size()[:-1]
   else:
     raise ValueError("""You have to specify either input_ids or inputs_embeds""")

   lowercase__	: Any     		=   input_ids.device if input_ids is not None else inputs_embeds.device

   if attention_mask is None:
     lowercase__	: Optional[int]     		=   torch.ones(SCREAMING_SNAKE_CASE_       ,	device=SCREAMING_SNAKE_CASE_)
   if token_type_ids is None:
     lowercase__	: str     		=   torch.zeros(SCREAMING_SNAKE_CASE_       ,	dtype=torch.long       ,	device=SCREAMING_SNAKE_CASE_)

   # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
   # ourselves in which case we just need to make it broadcastable to all heads.
   lowercase__	: torch.Tensor     		=   self.get_extended_attention_mask(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

   # If a 2D ou 3D attention mask is provided for the cross-attention
   # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
   if self.config.is_decoder and encoder_hidden_states is not None:
     lowercase__		,     lowercase__		,     lowercase__	: List[Any]     		=   encoder_hidden_states.size()
     lowercase__	: Tuple     		=   (encoder_batch_size, encoder_sequence_length)
     if encoder_attention_mask is None:
       lowercase__	: Tuple     		=   torch.ones(SCREAMING_SNAKE_CASE_       ,	device=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   self.invert_attention_mask(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__	: Optional[Any]     		=   None

   # Prepare head mask if needed
   # 1.0 in head_mask indicate we keep the head
   # attention_probs has shape bsz x n_heads x N x N
   # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
   # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
   lowercase__	: int     		=   self.get_head_mask(SCREAMING_SNAKE_CASE_       ,	self.config.num_hidden_layers)

   lowercase__	: str     		=   self.embeddings(
       input_ids=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	inputs_embeds=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   embedding_output

   if self.training:
     lowercase__	: Tuple     		=   []
     for i in range(self.config.num_hidden_layers):
       lowercase__	: Tuple     		=   self.encoder.adaptive_forward(
           SCREAMING_SNAKE_CASE_       ,	current_layer=SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	head_mask=SCREAMING_SNAKE_CASE_)

       lowercase__	: List[str]     		=   self.pooler(SCREAMING_SNAKE_CASE_)
       lowercase__	: Any     		=   output_layers[i](output_dropout(SCREAMING_SNAKE_CASE_))
       res.append(SCREAMING_SNAKE_CASE_)
   elif self.patience == 0:  # Use all layers for inference
     lowercase__	: Dict     		=   self.encoder(
         SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	head_mask=SCREAMING_SNAKE_CASE_       ,	encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,	encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,	)
     lowercase__	: Optional[int]     		=   self.pooler(encoder_outputs[0])
     lowercase__	: Optional[Any]     		=   [output_layers[self.config.num_hidden_layers - 1](SCREAMING_SNAKE_CASE_)]
   else:
     lowercase__	: Tuple     		=   0
     lowercase__	: Dict     		=   None
     lowercase__	: Union[str, Any]     		=   0
     for i in range(self.config.num_hidden_layers):
       calculated_layer_num += 1
       lowercase__	: List[Any]     		=   self.encoder.adaptive_forward(
           SCREAMING_SNAKE_CASE_       ,	current_layer=SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	head_mask=SCREAMING_SNAKE_CASE_)

       lowercase__	: Optional[Any]     		=   self.pooler(SCREAMING_SNAKE_CASE_)
       lowercase__	: Any     		=   output_layers[i](SCREAMING_SNAKE_CASE_)
       if regression:
         lowercase__	: Any     		=   logits.detach()
         if patient_result is not None:
           lowercase__	: str     		=   patient_result.detach()
         if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
           patient_counter += 1
         else:
           lowercase__	: List[Any]     		=   0
       else:
         lowercase__	: Tuple     		=   logits.detach().argmax(dim=1)
         if patient_result is not None:
           lowercase__	: Union[str, Any]     		=   patient_result.detach().argmax(dim=1)
         if (patient_result is not None) and torch.all(labels.eq(SCREAMING_SNAKE_CASE_)):
           patient_counter += 1
         else:
           lowercase__	: Dict     		=   0

       lowercase__	: List[str]     		=   logits
       if patient_counter == self.patience:
         break
     lowercase__	: Union[str, Any]     		=   [patient_result]
     self.inference_layers_num += calculated_layer_num
     self.inference_instances_num += 1

   return res



@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '	,    UpperCAmelCase_	,    )
class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   config.num_labels

   lowercase__	: str     		=   BertModelWithPabee(SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   nn.Dropout(config.hidden_dropout_prob)
   lowercase__	: Optional[int]     		=   nn.ModuleList(
       [nn.Linear(config.hidden_size       ,	self.config.num_labels) for _ in range(config.num_hidden_layers)])

   self.init_weights()



 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_)
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




   lowercase__	: Any     		=   self.bert(
       input_ids=SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	head_mask=SCREAMING_SNAKE_CASE_       ,	inputs_embeds=SCREAMING_SNAKE_CASE_       ,	output_dropout=self.dropout       ,	output_layers=self.classifiers       ,	regression=self.num_labels == 1       ,	)

   lowercase__	: Optional[Any]     		=   (logits[-1],)

   if labels is not None:
     lowercase__	: List[str]     		=   None
     lowercase__	: Union[str, Any]     		=   0
     for ix, logits_item in enumerate(SCREAMING_SNAKE_CASE_):
       if self.num_labels == 1:
         #  We are doing regression
         lowercase__	: int     		=   MSELoss()
         lowercase__	: List[str]     		=   loss_fct(logits_item.view(-1)       ,	labels.view(-1))
       else:
         lowercase__	: Union[str, Any]     		=   CrossEntropyLoss()
         lowercase__	: Dict     		=   loss_fct(logits_item.view(-1       ,	self.num_labels)       ,	labels.view(-1))
       if total_loss is None:
         lowercase__	: Optional[Any]     		=   loss
       else:
         total_loss += loss * (ix + 1)
       total_weights += ix + 1
     lowercase__	: List[Any]     		=   (total_loss / total_weights,) + outputs

   return outputs








import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
     import torch



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=14       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	):


   '''simple docstring'''




   lowercase__	: str     		=   parent
   lowercase__	: Optional[int]     		=   batch_size
   lowercase__	: Optional[int]     		=   seq_length
   lowercase__	: Union[str, Any]     		=   is_training
   lowercase__	: Any     		=   use_input_mask
   lowercase__	: Optional[int]     		=   use_token_type_ids
   lowercase__	: Optional[Any]     		=   use_labels
   lowercase__	: Optional[int]     		=   vocab_size
   lowercase__	: Optional[Any]     		=   hidden_size
   lowercase__	: Any     		=   rotary_dim
   lowercase__	: Optional[Any]     		=   num_hidden_layers
   lowercase__	: Tuple     		=   num_attention_heads
   lowercase__	: Tuple     		=   intermediate_size
   lowercase__	: List[str]     		=   hidden_act
   lowercase__	: Optional[Any]     		=   hidden_dropout_prob
   lowercase__	: int     		=   attention_probs_dropout_prob
   lowercase__	: Any     		=   max_position_embeddings
   lowercase__	: Optional[int]     		=   initializer_range
   lowercase__	: Optional[int]     		=   None
   lowercase__	: str     		=   vocab_size - 1
   lowercase__	: Any     		=   vocab_size - 1
   lowercase__	: Dict     		=   vocab_size - 1


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size)

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

   lowercase__	: List[Any]     		=   GPTJConfig(
       vocab_size=self.vocab_size       ,	n_embd=self.hidden_size       ,	n_layer=self.num_hidden_layers       ,	n_head=self.num_attention_heads       ,	n_positions=self.max_position_embeddings       ,	use_cache=SCREAMING_SNAKE_CASE_       ,	bos_token_id=self.bos_token_id       ,	eos_token_id=self.eos_token_id       ,	pad_token_id=self.pad_token_id       ,	rotary_dim=self.rotary_dim       ,	)

   return (config, input_ids, input_mask)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.prepare_config_and_inputs()
   lowercase__		,     lowercase__		,     lowercase__	: Optional[Any]     		=   config_and_inputs
   lowercase__	: Optional[Any]     		=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
   return config, inputs_dict


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   20
   lowercase__	: int     		=   model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="""i4""")

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

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

   lowercase__	: Tuple     		=   model(SCREAMING_SNAKE_CASE_)

   lowercase__	: 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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   20
   lowercase__	: List[Any]     		=   model_class_name(SCREAMING_SNAKE_CASE_)

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

   lowercase__	: Dict     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1))

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

   lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_)

   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}')



@require_flax
class    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Dict	   =      (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __lowerCAmelCase	:  str	   =      (FlaxGPTJForCausalLM,) if is_flax_available() else ()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   FlaxGPTJModelTester(self)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   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_with_attn_mask(
         SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   GPTaTokenizer.from_pretrained("""gpt2"""       ,	pad_token="""<|endoftext|>"""       ,	padding_side="""left""")
   lowercase__	: List[str]     		=   tokenizer(["""Hello this is a long string""", """Hey"""]       ,	return_tensors="""np"""       ,	padding=SCREAMING_SNAKE_CASE_       ,	truncation=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""")
   lowercase__	: Optional[Any]     		=   False
   lowercase__	: List[str]     		=   model.config.eos_token_id

   lowercase__	: List[Any]     		=   jax.jit(model.generate)

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

   lowercase__	: List[str]     		=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,	skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   [
       """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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: 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
       lowercase__	: List[Any]     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: Any     		=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

       lowercase__	: List[str]     		=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: List[Any]     		=   fx_state

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

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

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

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


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: 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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: str     		=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

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

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

       with torch.no_grad():
         lowercase__	: Dict     		=   pt_model(**SCREAMING_SNAKE_CASE_).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: Tuple     		=   pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_flax=SCREAMING_SNAKE_CASE_)

       with torch.no_grad():
         lowercase__	: Tuple     		=   pt_model_loaded(**SCREAMING_SNAKE_CASE_).to_tuple()

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



 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__	: Any     		=   model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""")
     lowercase__	: int     		=   model(np.ones((1, 1)))
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)






