

'''simple docstring'''





import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
           RwkvForCausalLM,
           RwkvModel,
       )
       from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
       snake_case_    						=				False


class    SCREAMING_SNAKE_CASE__      :





    def __init__(   self		,     a		,     a=14		,     a=7		,     a=True		,     a=False		,     a=True		,     a=True		,     a=True		,     a=99		,     a=32		,     a=5		,     a=37		,     a="gelu"		,     a=0.1		,     a=0.1		,     a=512		,     a=16		,     a=2		,     a=3		,     a=4		,     a=None		,     ):
         lowercase__					:     Tuple   = parent
         lowercase__					:     Optional[int]   = batch_size
         lowercase__					:     Tuple   = seq_length
         lowercase__					:     Tuple   = is_training
         lowercase__					:     str   = use_token_type_ids
         lowercase__					:     Union[str, Any]   = use_input_mask
         lowercase__					:     List[str]   = use_labels
         lowercase__					:     List[Any]   = use_mc_token_ids
         lowercase__					:     int   = vocab_size
         lowercase__					:     str   = hidden_size
         lowercase__					:     List[Any]   = num_hidden_layers
         lowercase__					:     Optional[Any]   = intermediate_size
         lowercase__					:     Tuple   = hidden_act
         lowercase__					:     Optional[Any]   = hidden_dropout_prob
         lowercase__					:     List[str]   = attention_probs_dropout_prob
         lowercase__					:     str   = max_position_embeddings
         lowercase__					:     Tuple   = type_vocab_size
         lowercase__					:     Optional[Any]   = type_sequence_label_size
         lowercase__					:     int   = num_labels
         lowercase__					:     List[str]   = num_choices
         lowercase__					:     int   = scope
         lowercase__					:     Tuple   = vocab_size - 1
         lowercase__					:     int   = vocab_size - 1
         lowercase__					:     Any   = vocab_size - 1





    def    snake_case_       (   self):
         return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b')





    def    snake_case_       (   self		,     a=False		,     a=False		,     a=False):
         lowercase__					:     str   = ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size)

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

         lowercase__					:     str   = None
         if self.use_token_type_ids:
              lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size)

         lowercase__					:     Optional[int]   = None
         if self.use_mc_token_ids:
              lowercase__					:     Optional[int]   = ids_tensor([self.batch_size, self.num_choices]		,     self.seq_length)

         lowercase__					:     Optional[int]   = None
         lowercase__					:     Tuple   = None
         lowercase__					:     Tuple   = None
         if self.use_labels:
              lowercase__					:     Dict   = ids_tensor([self.batch_size]		,     self.type_sequence_label_size)
              lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels)
              lowercase__					:     str   = ids_tensor([self.batch_size]		,     self.num_choices)

         lowercase__					:     List[str]   = self.get_config(
             gradient_checkpointing=a		,     scale_attn_by_inverse_layer_idx=a		,     reorder_and_upcast_attn=a		,     )

         return (
             config,
             input_ids,
             input_mask,
             None,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )





    def    snake_case_       (   self		,     a=False		,     a=False		,     a=False):
         return RwkvConfig(
             vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     intermediate_size=self.intermediate_size		,     activation_function=self.hidden_act		,     resid_pdrop=self.hidden_dropout_prob		,     attn_pdrop=self.attention_probs_dropout_prob		,     n_positions=self.max_position_embeddings		,     type_vocab_size=self.type_vocab_size		,     use_cache=a		,     bos_token_id=self.bos_token_id		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.pad_token_id		,     gradient_checkpointing=a		,     scale_attn_by_inverse_layer_idx=a		,     reorder_and_upcast_attn=a		,     )





    def    snake_case_       (   self):
         lowercase__					:     Optional[int]   = self.get_config()
         lowercase__					:     Union[str, Any]   = 300
         return config





    def    snake_case_       (   self):
         (
             lowercase__				
         )					:     int   = self.prepare_config_and_inputs()

         lowercase__					:     List[Any]   = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
         lowercase__					:     Any   = ids_tensor([self.batch_size, self.seq_length]		,     vocab_size=2)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
         lowercase__					:     Any   = True
         lowercase__					:     Dict   = RwkvModel(config=a)
         model.to(a)
         model.eval()

         lowercase__					:     Dict   = model(a)

         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(len(result.hidden_states)		,     config.num_hidden_layers + 1)





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
         lowercase__					:     Any   = RwkvForCausalLM(a)
         model.to(a)
         model.eval()

         lowercase__					:     Union[str, Any]   = model(a		,     labels=a)
         self.parent.assertEqual(result.loss.shape		,     ())
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size))





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
         lowercase__					:     Optional[int]   = RwkvModel(config=a)
         model.to(a)
         model.eval()

         lowercase__					:     List[str]   = model(a)
         lowercase__					:     int   = outputs.last_hidden_state

         lowercase__					:     Optional[int]   = model(input_ids[:, :2])
         lowercase__					:     Optional[int]   = outputs.last_hidden_state

         # Using the state computed on the first inputs, we will get the same output
         lowercase__					:     int   = model(input_ids[:, 2:]		,     state=outputs.state)
         lowercase__					:     List[Any]   = outputs.last_hidden_state

         self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]		,     dim=1)		,     a		,     atol=1e-5))





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a		,     a=False):
         lowercase__					:     Optional[int]   = RwkvForCausalLM(a)
         model.to(a)
         if gradient_checkpointing:
              model.gradient_checkpointing_enable()

         lowercase__					:     Tuple   = model(a		,     labels=a)
         self.parent.assertEqual(result.loss.shape		,     ())
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size))
         result.loss.backward()





    def    snake_case_       (   self):
         lowercase__					:     List[Any]   = self.prepare_config_and_inputs()

         (
             lowercase__				
         )					:     Optional[int]   = config_and_inputs

         lowercase__					:     int   = {'input_ids': input_ids}

         return config, inputs_dict



@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	, reason="""See https://github.com/huggingface/transformers/pull/24204"""  )
@require_torch
class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case	, __snake_case	, unittest.TestCase  ):
    __lowerCamelCase		: List[Any]														=  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
    __lowerCamelCase		: Tuple														=  (
        {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
    )
    # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
    __lowerCamelCase		: Optional[Any]														=  False
    __lowerCamelCase		: Any														=  False
    __lowerCamelCase		: List[str]														=  False
    __lowerCamelCase		: int														=  False
    __lowerCamelCase		: List[str]														=  False  # Rwkv does not support head masking





    def    snake_case_       (   self):
         lowercase__					:     Dict   = RwkvModelTester(self)
         lowercase__					:     List[Any]   = ConfigTester(
             self		,     config_class=a		,     n_embd=37		,     common_properties=['hidden_size', 'num_hidden_layers'])





    def    snake_case_       (   self		,     a		,     a		,     a=None):
         if isinstance(a		,     torch.Tensor):
              lowercase__					:     Optional[Any]   = member.max().item(), member.min().item()
         elif isinstance(a		,     a) or isinstance(a		,     a):
              lowercase__					:     Optional[int]   = max(a), min(a)

         if not isinstance(a		,     a):
              raise TypeError('container should be a list or tuple')
         elif len(a) != 2:
              raise ValueError('container should have 2 elements')

         lowercase__					:     Union[str, Any]   = container

         lowercase__					:     str   = (min_value >= expected_min) and (max_value <= expected_max)

         if not is_inside_interval:
              lowercase__					:     Tuple   = '%s not found in %s' % (safe_repr(a), safe_repr(a))
              self.fail(self._formatMessage(a		,     a))





    def    snake_case_       (   self):
         self.config_tester.run_common_tests()





    def    snake_case_       (   self):
         lowercase__					:     str   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_rwkv_model(*a)





    def    snake_case_       (   self):
         lowercase__					:     Dict   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_causl_lm(*a)





    def    snake_case_       (   self):
         lowercase__					:     int   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_state_equivalency(*a)





    def    snake_case_       (   self):
         lowercase__					:     str   = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__					:     Union[str, Any]   = model_class(config=a)
              for name, param in model.named_parameters():
                   if "time_decay" in name:
                        if param.requires_grad:
                             self.assertTrue(param.data.max().item() == 3.0)
                             self.assertTrue(param.data.min().item() == -5.0)
                   elif "time_first" in name:
                        if param.requires_grad:
                             # check if it's a ones like
                             self.assertTrue(torch.allclose(param.data		,     torch.ones_like(param.data)		,     atol=1e-5		,     rtol=1e-5))
                   elif any(x in name for x in ['time_mix_key', 'time_mix_receptance']):
                        if param.requires_grad:
                             self.assertInterval(
                                 param.data		,     [0.0, 1.0]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )
                   elif "time_mix_value" in name:
                        if param.requires_grad:
                             self.assertInterval(
                                 param.data		,     [0.0, 1.3]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )





    def    snake_case_       (   self):
         lowercase__					:     Optional[Any]   = self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__					:     Optional[Any]   = True

         lowercase__					:     List[Any]   = getattr(self.model_tester		,     'seq_length'		,     a)

         for model_class in self.all_model_classes:
              lowercase__					:     Tuple   = True
              lowercase__					:     Optional[Any]   = False
              lowercase__					:     Optional[Any]   = True
              lowercase__					:     Any   = model_class(a)
              model.to(a)
              model.eval()

              lowercase__					:     Optional[Any]   = self._prepare_for_class(a		,     a)
              lowercase__					:     Dict   = inputs['input_ids'].shape[0]
              with torch.no_grad():
                   lowercase__					:     Optional[int]   = model(**a)
              lowercase__					:     List[str]   = outputs.attentions
              self.assertEqual(len(a)		,     self.model_tester.num_hidden_layers)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              lowercase__					:     Optional[Any]   = True
              lowercase__					:     Any   = model_class(a)
              model.to(a)
              model.eval()

              lowercase__					:     int   = self._prepare_for_class(a		,     a)
              lowercase__					:     Dict   = inputs['input_ids'].shape[0]
              with torch.no_grad():
                   lowercase__					:     List[Any]   = model(**a)
              lowercase__					:     List[Any]   = outputs.attentions
              self.assertEqual(len(a)		,     self.model_tester.num_hidden_layers)

              self.assertListEqual(
                  list(attentions[0].shape[-3:])		,     [batch_size, seq_len, config.hidden_size]		,     )
              lowercase__					:     int   = len(a)

              # Check attention is always last and order is fine
              lowercase__					:     Dict   = True
              lowercase__					:     Dict   = True
              lowercase__					:     Dict   = model_class(a)
              model.to(a)
              model.eval()

              lowercase__					:     List[str]   = self._prepare_for_class(a		,     a)
              lowercase__					:     Any   = inputs['input_ids'].shape[0]
              with torch.no_grad():
                   lowercase__					:     int   = model(**a)

              lowercase__					:     Any   = 1
              self.assertEqual(out_len + added_hidden_states		,     len(a))

              lowercase__					:     int   = outputs.attentions

              self.assertEqual(len(a)		,     self.model_tester.num_hidden_layers)
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:])		,     [batch_size, seq_len, config.hidden_size]		,     )





    @slow
    def    snake_case_       (   self):
         for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__					:     Union[str, Any]   = RwkvModel.from_pretrained(a)
              self.assertIsNotNone(a)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	, reason="""See https://github.com/huggingface/transformers/pull/24204"""  )
@slow
class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):





    def    snake_case_       (   self):
         lowercase__					:     Any   = 'RWKV/rwkv-4-169m-pile'
         lowercase__					:     int   = AutoTokenizer.from_pretrained(self.model_id)





    def    snake_case_       (   self):
         lowercase__					:     Optional[int]   = 'Hello my name is Jasmine and I am a newbie to the'
         lowercase__					:     Optional[Any]   = RwkvForCausalLM.from_pretrained(self.model_id).to(a)

         lowercase__					:     List[Any]   = self.tokenizer('Hello my name is'		,     return_tensors='pt').input_ids.to(a)
         lowercase__					:     Dict   = model.generate(a		,     max_new_tokens=10)
         lowercase__					:     int   = self.tokenizer.decode(output[0].tolist())

         self.assertEqual(a		,     a)





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = 'Hello my name is Jasmine and I am a newbie to the'

         lowercase__					:     str   = self.tokenizer('Hello my name is'		,     return_tensors='pt').input_ids.to(a)
         lowercase__					:     Optional[int]   = RwkvForCausalLM.from_pretrained(self.model_id		,     torch_dtype=torch.bfloataa).to(a)

         lowercase__					:     Optional[int]   = model.generate(a		,     max_new_tokens=10)
         lowercase__					:     Union[str, Any]   = self.tokenizer.decode(output[0].tolist())

         self.assertEqual(a		,     a)




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    @classmethod
    def    snake_case_       (   cls):
         return cls





    @slow
    @require_torch_gpu
    def    snake_case_       (   self):
         lowercase__					:     List[Any]   = self.get_auto_remove_tmp_dir()
         lowercase__					:     List[str]   = f"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

         # we want this test to run even if there is only one GPU, but if there are more we use them all
         lowercase__					:     int   = get_gpu_count()
         lowercase__					:     Tuple   = f"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
         lowercase__					:     Optional[Any]   = [sys.executable] + distributed_args + args
         execute_subprocess_async(a		,     env=self.get_env())

         lowercase__					:     str   = os.path.join(a		,     'test_bleu.json')
         lowercase__					:     List[str]   = load_json(a)
         # print(metrics)
         self.assertGreaterEqual(metrics['bleu']		,     25)

