






"""simple docstring"""



from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TFSharedEmbeddings,
    )

    if _tf_gpu_memory_limit is not None:
        _UpperCamelCase						:    int		=						tf.config.list_physical_devices('GPU')
        for gpu in gpus:
            # Restrict TensorFlow to only allocate x GB of memory on the GPUs
            try:
                tf.config.set_logical_device_configuration(
                    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                )
                _UpperCamelCase						:    Any		=						tf.config.list_logical_devices('GPU')
                print('Logical GPUs', logical_gpus)
            except RuntimeError as e:
                # Virtual devices must be set before GPUs have been initialized
                print(e)

@require_tf
class 			a	:
    UpperCAmelCase_					:	Union[str, Any]      	=None
    UpperCAmelCase_					:	Any      	=()
    UpperCAmelCase_					:	Optional[int]      	=()
    UpperCAmelCase_					:	Any      	=True
    UpperCAmelCase_					:	int      	=True
    UpperCAmelCase_					:	Dict      	=True
    UpperCAmelCase_					:	Any      	=False







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=False			):
      lowercase				       =			copy.deepcopy(_lowerCamelCase			)

      if model_class in get_values(_lowerCamelCase			):
        lowercase				       =			{
            k: tf.tile(tf.expand_dims(_lowerCamelCase				,						1			)				,						(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)			)
            if isinstance(_lowerCamelCase				,						tf.Tensor			) and v.ndim > 0
            else v
            for k, v in inputs_dict.items()
        }

      if return_labels:
        if model_class in get_values(_lowerCamelCase			):
          lowercase				       =			tf.ones(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in get_values(_lowerCamelCase			):
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in [
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
        ]:
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in get_values(_lowerCamelCase			):
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in [
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
        ]:
          lowercase				       =			tf.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)				,						dtype=tf.intaa			)
      return inputs_dict







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)
        lowercase				       =			model_class(_lowerCamelCase			)

        @tf.function
        def run_in_graph_mode():
          return model(_lowerCamelCase			)

        lowercase				       =			run_in_graph_mode()
        self.assertIsNotNone(_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)
        lowercase				       =			model_class(_lowerCamelCase			)

        @tf.function(experimental_compile=_lowerCamelCase			)
        def run_in_graph_mode():
          return model(_lowerCamelCase			)

        lowercase				       =			run_in_graph_mode()
        self.assertIsNotNone(_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
        lowercase				       =			model_class(_lowerCamelCase			)
        if getattr(_lowerCamelCase				,						'hf_compute_loss'				,						_lowerCamelCase			):
          # Test that model correctly compute the loss with kwargs
          lowercase				       =			self._prepare_for_class(inputs_dict.copy()				,						_lowerCamelCase				,						return_labels=_lowerCamelCase			)
          # Is there a better way to remove these decoder inputs?
          lowercase				       =			{
              key: val
              for key, val in prepared_for_class.items()
              if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
          }

          lowercase				       =			{
              'labels',
              'label',
              'label_ids',
              'start_positions',
              'start_position',
              'end_positions',
              'end_position',
              'next_sentence_label',
          }
          lowercase				       =			possible_label_cols.intersection(set(_lowerCamelCase			)			)
          self.assertGreater(len(_lowerCamelCase			)				,						0				,						msg='No matching label names found!'			)
          lowercase				       =			{key: val for key, val in prepared_for_class.items() if key in label_names}
          lowercase				       =			{key: val for key, val in prepared_for_class.items() if key not in label_names}
          self.assertGreater(len(_lowerCamelCase			)				,						0			)

          # Make sure it works with XLA!
          model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)				,						jit_compile=_lowerCamelCase			)
          # Make sure the model fits without crashing regardless of where we pass the labels
          lowercase				       =			model.fit(
              _lowerCamelCase				,						validation_data=_lowerCamelCase				,						steps_per_epoch=1				,						validation_steps=1				,						shuffle=_lowerCamelCase				,						verbose=0				,						)
          lowercase				       =			history.history['loss'][0]
          self.assertTrue(not isnan(_lowerCamelCase			)			)
          lowercase				       =			history.history['val_loss'][0]
          self.assertTrue(not isnan(_lowerCamelCase			)			)

          # Now test it with separate labels, to make sure that path works in XLA too.
          lowercase				       =			model_class(_lowerCamelCase			)
          model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)				,						jit_compile=_lowerCamelCase			)
          lowercase				       =			model.fit(
              _lowerCamelCase				,						_lowerCamelCase				,						validation_data=(inputs_minus_labels, labels)				,						steps_per_epoch=1				,						validation_steps=1				,						shuffle=_lowerCamelCase				,						verbose=0				,						)

          lowercase				       =			history.history['loss'][0]
          self.assertTrue(not isnan(_lowerCamelCase			)			)
          lowercase				       =			history.history['val_loss'][0]
          self.assertTrue(not isnan(_lowerCamelCase			)			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      lowercase				       =			True
      lowercase				       =			True

      if hasattr(_lowerCamelCase				,						'use_cache'			):
        lowercase				       =			True

      lowercase				       =			getattr(self.model_tester				,						'encoder_seq_length'				,						self.model_tester.seq_length			)
      lowercase				       =			getattr(self.model_tester				,						'key_length'				,						_lowerCamelCase			)

      for model_class in self.all_model_classes[:2]:
        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)
        lowercase				       =			model_class(_lowerCamelCase			)
        model.build()
        lowercase				       =			len(model(_lowerCamelCase			)			)

        for key in list(class_inputs_dict.keys()			):
          # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
          if key not in model.input_signature:
            del class_inputs_dict[key]
          # Check it's a tensor, in case the inputs dict has some bools in it too
          elif isinstance(class_inputs_dict[key]				,						tf.Tensor			) and class_inputs_dict[key].dtype.is_integer:
            lowercase				       =			tf.cast(class_inputs_dict[key]				,						tf.intaa			)

        if set(class_inputs_dict.keys()			) != set(model.input_signature.keys()			):
          continue  # Some models have inputs that the preparation functions don't create, we skip those

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(_lowerCamelCase				,						saved_model=_lowerCamelCase			)
          lowercase				       =			os.path.join(_lowerCamelCase				,						'saved_model'				,						'1'			)
          lowercase				       =			tf.keras.models.load_model(_lowerCamelCase			)
          lowercase				       =			model(_lowerCamelCase			)

          if self.is_encoder_decoder:
            lowercase				       =			outputs['encoder_hidden_states']
            lowercase				       =			outputs['encoder_attentions']
          else:
            lowercase				       =			outputs['hidden_states']
            lowercase				       =			outputs['attentions']

          self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)

          lowercase				       =			getattr(
              self.model_tester				,						'expected_num_hidden_layers'				,						self.model_tester.num_hidden_layers + 1			)

          self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)
          self.assertListEqual(
              list(output_hidden_states[0].shape[-2:]			)				,						[self.model_tester.seq_length, self.model_tester.hidden_size]				,						)

          self.assertEqual(len(_lowerCamelCase			)				,						self.model_tester.num_hidden_layers			)
          self.assertListEqual(
              list(output_attentions[0].shape[-3:]			)				,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]				,						)







    @slow
    def      UpperCamelCase_ (     self			):
      tf.keras.mixed_precision.set_global_policy('mixed_float16'			)

      # try/finally block to ensure subsequent tests run in float32
      try:
        lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)
          lowercase				       =			model_class(_lowerCamelCase			)
          lowercase				       =			model(_lowerCamelCase			)

          self.assertIsNotNone(_lowerCamelCase			)
      finally:
        tf.keras.mixed_precision.set_global_policy('float32'			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      # head_mask and decoder_head_mask has different shapes than other input args
      if "head_mask" in inputs_dict:
        del inputs_dict["head_mask"]
      if "decoder_head_mask" in inputs_dict:
        del inputs_dict["decoder_head_mask"]
      if "cross_attn_head_mask" in inputs_dict:
        del inputs_dict["cross_attn_head_mask"]
      lowercase				       =			{
          module_member
          for model_class in self.all_model_classes
          for module in (import_module(model_class.__module__			),)
          for module_member_name in dir(_lowerCamelCase			)
          if module_member_name.endswith('MainLayer'			)
          for module_member in (getattr(_lowerCamelCase				,						_lowerCamelCase			),)
          if isinstance(_lowerCamelCase				,						_lowerCamelCase			)
          and tf.keras.layers.Layer in module_member.__bases__
          and getattr(_lowerCamelCase				,						'_keras_serializable'				,						_lowerCamelCase			)
      }

      for main_layer_class in tf_main_layer_classes:
        # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
        if "T5" in main_layer_class.__name__:
          # Take the same values than in TFT5ModelTester for this shared layer
          lowercase				       =			TFSharedEmbeddings(self.model_tester.vocab_size				,						self.model_tester.hidden_size				,						name='shared'			)
          lowercase				       =			False
          lowercase				       =			main_layer_class(_lowerCamelCase				,						embed_tokens=_lowerCamelCase			)
        else:
          lowercase				       =			main_layer_class(_lowerCamelCase			)

        lowercase				       =			{
            name: tf.keras.Input(tensor.shape[1:]				,						dtype=tensor.dtype			) for name, tensor in inputs_dict.items()
        }

        if hasattr(self.model_tester				,						'num_labels'			):
          lowercase				       =			self.model_tester.num_labels
        else:
          lowercase				       =			2

        lowercase				       =			tf.data.Dataset.from_tensor_slices(
            (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)			))			).batch(1			)

        lowercase				       =			main_layer(_lowerCamelCase			)[0]
        lowercase				       =			tf.keras.layers.Dense(_lowerCamelCase				,						activation='softmax'				,						name='outputs'			)(_lowerCamelCase			)
        lowercase				       =			tf.keras.models.Model(inputs=_lowerCamelCase				,						outputs=[outputs]			)

        model.compile(loss='binary_crossentropy'				,						optimizer='adam'				,						metrics=['binary_accuracy']			)
        model.fit(_lowerCamelCase				,						epochs=1			)

        with tempfile.TemporaryDirectory() as tmpdirname:
          lowercase				       =			os.path.join(_lowerCamelCase				,						'keras_model.h5'			)
          model.save(_lowerCamelCase			)
          if "T5" in main_layer_class.__name__:
            lowercase				       =			tf.keras.models.load_model(
                _lowerCamelCase				,						custom_objects={
                    main_layer_class.__name__: main_layer_class,
                    'TFSharedEmbeddings': TFSharedEmbeddings,
                }				,						)
          else:
            lowercase				       =			tf.keras.models.load_model(
                _lowerCamelCase				,						custom_objects={main_layer_class.__name__: main_layer_class}			)
          assert isinstance(_lowerCamelCase				,						tf.keras.Model			)
          model(_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:2]:
        lowercase				       =			model_class(_lowerCamelCase			)

        lowercase				       =			copy.deepcopy(_lowerCamelCase			)

        if not self.is_encoder_decoder:
          lowercase				       =			inputs['input_ids']
          del inputs["input_ids"]
        else:
          lowercase				       =			inputs['input_ids']
          lowercase				       =			inputs.get('decoder_input_ids'				,						_lowerCamelCase			)
          del inputs["input_ids"]
          inputs.pop('decoder_input_ids'				,						_lowerCamelCase			)

        if not self.is_encoder_decoder:
          lowercase				       =			model.get_input_embeddings()(_lowerCamelCase			)
        else:
          lowercase				       =			model.get_input_embeddings()(_lowerCamelCase			)
          lowercase				       =			model.get_input_embeddings()(_lowerCamelCase			)

        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)

        @tf.function
        def run_in_graph_mode():
          return model(_lowerCamelCase			)

        lowercase				       =			run_in_graph_mode()
        self.assertIsNotNone(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      # special tokens cannot be bad tokens
      lowercase				       =			[]
      if model.config.bos_token_id is not None:
        special_tokens.append(model.config.bos_token_id			)
      if model.config.pad_token_id is not None:
        special_tokens.append(model.config.pad_token_id			)
      if model.config.eos_token_id is not None:
        special_tokens.append(model.config.eos_token_id			)

      # create random bad tokens that are not special tokens
      lowercase				       =			[]
      while len(_lowerCamelCase			) < num_bad_tokens:
        lowercase				       =			tf.squeeze(ids_tensor((1, 1)				,						self.model_tester.vocab_size			)				,						0			).numpy()[0]
        if token not in special_tokens:
          bad_tokens.append(_lowerCamelCase			)
      return bad_tokens







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      for token_id in output_ids[0].numpy().tolist():
        self.assertGreaterEqual(_lowerCamelCase				,						0			)
        self.assertLess(_lowerCamelCase				,						self.model_tester.vocab_size			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      # for all bad word tokens
      for bad_word_ids in bad_words_ids:
        # for all slices in batch
        for generated_ids_slice in generated_ids:
          # for all word idx
          for i in range(len(_lowerCamelCase			)				,						len(_lowerCamelCase			)			):
            # if tokens match
            if generated_ids_slice[i - len(_lowerCamelCase			) : i] == bad_word_ids:
              return True
      return False










"""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:
   _UpperCamelCase :    Union[str, Any]      =			False






class __a :



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



  def     A_					(self	:		List[str] )		->				Optional[int]:
   return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b' )



  def     A_					(self	:		Dict					,      lowercase_	:		str=False					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		int=False )		->				List[str]:
   __A						:							List[Any]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size )

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

   __A						:							Tuple      =						None
   if self.use_token_type_ids:
    __A						:							Optional[int]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size )

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

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

   __A						:							Any      =						self.get_config(
       gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )

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



  def     A_					(self	:		Optional[int]					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		Dict=False					,      lowercase_	:		List[str]=False )		->				Any:
   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=lowercase_					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )



  def     A_					(self	:		Optional[int] )		->				Dict:
   __A						:							Any      =						self.get_config()
   __A						:							Any      =						300
   return config



  def     A_					(self	:		Tuple )		->				Dict:
   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							Tuple      =						self.prepare_config_and_inputs()

   __A						:							Tuple      =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
   __A						:							List[str]      =						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     A_					(self	:		Any					,      lowercase_	:		Any					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		str					,      *lowercase_	:		Any )		->				Tuple:
   __A						:							List[str]      =						True
   __A						:							str      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							List[Any]      =						model(lowercase_ )

   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     A_					(self	:		List[str]					,      lowercase_	:		Dict					,      lowercase_	:		List[str]					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[Any]					,      *lowercase_	:		int )		->				Union[str, Any]:
   __A						:							Any      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   model.eval()

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



  def     A_					(self	:		str					,      lowercase_	:		List[Any]					,      lowercase_	:		Dict					,      lowercase_	:		Tuple					,      lowercase_	:		str					,      lowercase_	:		Dict					,      *lowercase_	:		Optional[Any] )		->				Union[str, Any]:
   __A						:							Any      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Tuple      =						model(lowercase_ )
   __A						:							str      =						outputs.last_hidden_state

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

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

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



  def     A_					(self	:		List[str]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[str]					,      lowercase_	:		int					,      lowercase_	:		int					,      lowercase_	:		Union[str, Any]					,      *lowercase_	:		Dict					,      lowercase_	:		List[Any]=False )		->				str:
   __A						:							Tuple      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   if gradient_checkpointing:
    model.gradient_checkpointing_enable()

   __A						:							Optional[int]      =						model(lowercase_					,      labels=lowercase_ )
   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     A_					(self	:		int )		->				Tuple:
   __A						:							Union[str, Any]      =						self.prepare_config_and_inputs()

   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							int      =						config_and_inputs

   __A						:							Tuple      =						{'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 __a ( snake_case_			,snake_case_			,snake_case_			,unittest.TestCase					):
  __lowercase     :     Any       						=       (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  __lowercase     :     Dict       						=       (
      {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
  )
  # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  __lowercase     :     List[str]       						=       False
  __lowercase     :     int       						=       False
  __lowercase     :     Optional[Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False  # Rwkv does not support head masking



  def     A_					(self	:		List[str] )		->				Any:
   __A						:							List[str]      =						RwkvModelTester(self )
   __A						:							List[Any]      =						ConfigTester(
       self					,      config_class=lowercase_					,      n_embd=37					,      common_properties=['hidden_size', 'num_hidden_layers'] )



  def     A_					(self	:		int					,      lowercase_	:		Any					,      lowercase_	:		Optional[int]					,      lowercase_	:		Optional[Any]=None )		->				Tuple:
   if isinstance(lowercase_					,      torch.Tensor ):
    __A     ,       __A						:							str      =						member.max().item(), member.min().item()
   elif isinstance(lowercase_					,      lowercase_ ) or isinstance(lowercase_					,      lowercase_ ):
    __A     ,       __A						:							List[str]      =						max(lowercase_ ), min(lowercase_ )

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

   __A     ,       __A						:							Optional[Any]      =						container

   __A						:							List[str]      =						(min_value >= expected_min) and (max_value <= expected_max)

   if not is_inside_interval:
    __A						:							Optional[int]      =						'%s not found in %s' % (safe_repr(lowercase_ ), safe_repr(lowercase_ ))
    self.fail(self._formatMessage(lowercase_					,      lowercase_ ) )



  def     A_					(self	:		int )		->				Optional[Any]:
   self.config_tester.run_common_tests()



  def     A_					(self	:		Tuple )		->				str:
   __A						:							Dict      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_rwkv_model(*lowercase_ )



  def     A_					(self	:		List[Any] )		->				Union[str, Any]:
   __A						:							Any      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_causl_lm(*lowercase_ )



  def     A_					(self	:		Any )		->				Optional[int]:
   __A						:							Optional[int]      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_state_equivalency(*lowercase_ )



  def     A_					(self	:		List[str] )		->				int:
   __A     ,       __A						:							Optional[Any]      =						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A						:							Optional[int]      =						model_class(config=lowercase_ )
    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     A_					(self	:		str )		->				Any:
   __A     ,       __A						:							List[str]      =						self.model_tester.prepare_config_and_inputs_for_common()
   __A						:							Optional[Any]      =						True

   __A						:							List[str]      =						getattr(self.model_tester					,      'seq_length'					,      lowercase_ )

   for model_class in self.all_model_classes:
    __A						:							str      =						True
    __A						:							str      =						False
    __A						:							List[str]      =						True
    __A						:							Optional[Any]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

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

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __A						:							Optional[int]      =						True
    __A						:							Dict      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

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

    self.assertListEqual(
        list(attentions[0].shape[-3:] )					,      [batch_size, seq_len, config.hidden_size]					,      )
    __A						:							Tuple      =						len(lowercase_ )

    # Check attention is always last and order is fine
    __A						:							Any      =						True
    __A						:							str      =						True
    __A						:							List[str]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							List[Any]      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Union[str, Any]      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							Dict      =						model(**lowercase_ )

    __A						:							Dict      =						1
    self.assertEqual(out_len + added_hidden_states					,      len(lowercase_ ) )

    __A						:							Optional[Any]      =						outputs.attentions

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



  @slow
  def     A_					(self	:		Optional[Any] )		->				Any:
   for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __A						:							Optional[int]      =						RwkvModel.from_pretrained(lowercase_ )
    self.assertIsNotNone(lowercase_ )






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



  def     A_					(self	:		str )		->				Optional[Any]:
   __A						:							Optional[int]      =						'RWKV/rwkv-4-169m-pile'
   __A						:							Tuple      =						AutoTokenizer.from_pretrained(self.model_id )



  def     A_					(self	:		Optional[Any] )		->				str:
   __A						:							int      =						'Hello my name is Jasmine and I am a newbie to the'
   __A						:							str      =						RwkvForCausalLM.from_pretrained(self.model_id ).to(lowercase_ )

   __A						:							Union[str, Any]      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							List[str]      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							List[str]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )



  def     A_					(self	:		Any )		->				Optional[Any]:
   __A						:							Optional[int]      =						'Hello my name is Jasmine and I am a newbie to the'

   __A						:							Any      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							int      =						RwkvForCausalLM.from_pretrained(self.model_id					,      torch_dtype=torch.bfloataa ).to(lowercase_ )

   __A						:							str      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							Union[str, Any]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )





