'''simple docstring'''




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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       :     List[str]     =  """platform"""

  import jax
  import jax.numpy as jnp

  from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
      FlaxBlenderbotSmallForConditionalGeneration,
      FlaxBlenderbotSmallModel,
      shift_tokens_right,
  )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      List[Any]=None			,	_UpperCAmelCase  :      str=None			,	_UpperCAmelCase  :      Tuple=None			,	_UpperCAmelCase  :      Tuple=None			,	_UpperCAmelCase  :      List[str]=None			,	_UpperCAmelCase  :      List[str]=None			,	)    ->    Dict:


       """simple docstring"""







       if attention_mask is None:
              _UpperCAmelCase     :  List[str]							      =    np.where(input_ids != config.pad_token_id			,	1			,	0    )
       if decoder_attention_mask is None:
              _UpperCAmelCase     :  Union[str, Any]							      =    np.where(decoder_input_ids != config.pad_token_id			,	1			,	0    )
       if head_mask is None:
              _UpperCAmelCase     :  Union[str, Any]							      =    np.ones((config.encoder_layers, config.encoder_attention_heads)    )
       if decoder_head_mask is None:
              _UpperCAmelCase     :  List[str]							      =    np.ones((config.decoder_layers, config.decoder_attention_heads)    )
       if cross_attn_head_mask is None:
              _UpperCAmelCase     :  Union[str, 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": attention_mask,
       }

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				List[str]						,					A						:				List[Any]=13						,					A						:				Tuple=7						,					A						:				Union[str, Any]=True						,					A						:				int=False						,					A						:				Union[str, Any]=99						,					A						:				Dict=16						,					A						:				Any=2						,					A						:				int=4						,					A						:				int=4						,					A						:				str="gelu"						,					A						:				List[Any]=0.1						,					A						:				Optional[int]=0.1						,					A						:				Any=32						,					A						:				List[str]=2						,					A						:				Any=1						,					A						:				int=0						,					A						:				int=0.02						,					):
             _UpperCAmelCase     :  int							      =    parent
             _UpperCAmelCase     :  Tuple							      =    batch_size
             _UpperCAmelCase     :  Any							      =    seq_length
             _UpperCAmelCase     :  int							      =    is_training
             _UpperCAmelCase     :  Optional[Any]							      =    use_labels
             _UpperCAmelCase     :  Any							      =    vocab_size
             _UpperCAmelCase     :  List[str]							      =    hidden_size
             _UpperCAmelCase     :  List[str]							      =    num_hidden_layers
             _UpperCAmelCase     :  Optional[Any]							      =    num_attention_heads
             _UpperCAmelCase     :  int							      =    intermediate_size
             _UpperCAmelCase     :  List[Any]							      =    hidden_act
             _UpperCAmelCase     :  List[str]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Tuple							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  str							      =    max_position_embeddings
             _UpperCAmelCase     :  List[Any]							      =    eos_token_id
             _UpperCAmelCase     :  Any							      =    pad_token_id
             _UpperCAmelCase     :  Tuple							      =    bos_token_id
             _UpperCAmelCase     :  Tuple							      =    initializer_range




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,					self.vocab_size       )						,					3						,					self.vocab_size       )
             _UpperCAmelCase     :  Optional[int]							      =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,					dtype=np.intaa       ))						,					-1       )

             _UpperCAmelCase     :  str							      =    shift_tokens_right(A						,					1						,					2       )

             _UpperCAmelCase     :  str							      =    BlenderbotSmallConfig(
                 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						,					initializer_range=self.initializer_range						,					use_cache=A						,					)
             _UpperCAmelCase     :  Tuple							      =    prepare_blenderbot_inputs_dict(A						,					A						,					A       )
             return config, inputs_dict




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    self.prepare_config_and_inputs()
             return config, inputs_dict




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

             _UpperCAmelCase     :  List[Any]							      =    model.encode(inputs_dict["input_ids"]       )

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    (
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

             _UpperCAmelCase     :  List[Any]							      =    model.init_cache(decoder_input_ids.shape[0]						,					A						,					A       )
             _UpperCAmelCase     :  Tuple							      =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,					dtype="i4"       )

             _UpperCAmelCase     :  Optional[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     :  List[Any]							      =    model.decode(
                 decoder_input_ids[:, :-1]						,					A						,					decoder_attention_mask=A						,					past_key_values=A						,					decoder_position_ids=A						,					)

             _UpperCAmelCase     :  int							      =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,					dtype="i4"       )
             _UpperCAmelCase     :  Dict							      =    model.decode(
                 decoder_input_ids[:, -1:]						,					A						,					decoder_attention_mask=A						,					past_key_values=outputs_cache.past_key_values						,					decoder_position_ids=A						,					)

             _UpperCAmelCase     :  Optional[Any]							      =    model.decode(A						,					A       )

             _UpperCAmelCase     :  int							      =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
             self.parent.assertTrue(diff < 1E-3						,					msg=F"""Max diff is {diff}"""       )





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

             _UpperCAmelCase     :  Dict							      =    model.encode(inputs_dict["input_ids"]       )

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Union[str, Any]							      =    (
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

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

             _UpperCAmelCase     :  Any							      =    model.init_cache(decoder_input_ids.shape[0]						,					A						,					A       )
             _UpperCAmelCase     :  Optional[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     :  List[str]							      =    model.decode(
                 decoder_input_ids[:, :-1]						,					A						,					decoder_attention_mask=A						,					past_key_values=A						,					decoder_position_ids=A						,					)
             _UpperCAmelCase     :  int							      =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,					dtype="i4"       )
             _UpperCAmelCase     :  Union[str, Any]							      =    model.decode(
                 decoder_input_ids[:, -1:]						,					A						,					past_key_values=outputs_cache.past_key_values						,					decoder_attention_mask=A						,					decoder_position_ids=A						,					)

             _UpperCAmelCase     :  Dict							      =    model.decode(A						,					A						,					decoder_attention_mask=A       )

             _UpperCAmelCase     :  int							      =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
             self.parent.assertTrue(diff < 1E-3						,					msg=F"""Max diff is {diff}"""       )

@require_flax
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						9_9




      def      _A	(       self						:				List[str]       ):
             _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     :  int							      =    input_ids.shape[0]
             _UpperCAmelCase     :  Any							      =    BlenderbotSmallConfig(
                 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						:				Optional[Any]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    self._get_config_and_data()
             _UpperCAmelCase     :  int							      =    FlaxBlenderbotSmallForConditionalGeneration(A       )
             _UpperCAmelCase     :  int							      =    lm_model(input_ids=A       )
             _UpperCAmelCase     :  List[str]							      =    (batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["logits"].shape						,					A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    BlenderbotSmallConfig(
                 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     :  Optional[int]							      =    FlaxBlenderbotSmallForConditionalGeneration(A       )
             _UpperCAmelCase     :  Dict							      =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,					dtype=np.intaa       )
             _UpperCAmelCase     :  List[str]							      =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,					dtype=np.intaa       )
             _UpperCAmelCase     :  List[str]							      =    lm_model(input_ids=A						,					decoder_input_ids=A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    (*summary.shape, config.vocab_size)
             self.assertEqual(outputs["logits"].shape						,					A       )





      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,					dtype=np.intaa       )
             _UpperCAmelCase     :  Dict							      =    shift_tokens_right(A						,					1						,					2       )
             _UpperCAmelCase     :  Dict							      =    np.equal(A						,					1       ).astype(np.floataa       ).sum()
             _UpperCAmelCase     :  str							      =    np.equal(A						,					1       ).astype(np.floataa       ).sum()
             self.assertEqual(shifted.shape						,					input_ids.shape       )
             self.assertEqual(A						,					n_pad_before - 1       )
             self.assertTrue(np.equal(shifted[:, 0]						,					2       ).all()       )

@require_flax
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]						=						True
      __UpperCamelCase:  Tuple						=						(
          (
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      __UpperCamelCase:  List[Any]						=						(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    FlaxBlenderbotSmallModelTester(self       )




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




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    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(A						,					A						,					A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase      ,  _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     :  Any							      =    self._prepare_for_class(A						,					A       )
                           _UpperCAmelCase     :  Any							      =    model_class(A       )

                           @jax.jit
                           def encode_jitted(A						:				Dict						,					A						:				str=None						,					**A						:				List[Any]       ):
                                  return model.encode(input_ids=A						,					attention_mask=A       )

                           with self.subTest("JIT Enabled"       ):
                                  _UpperCAmelCase     :  int							      =    encode_jitted(**A       ).to_tuple()

                           with self.subTest("JIT Disabled"       ):
                                  with jax.disable_jit():
                                         _UpperCAmelCase     :  Optional[int]							      =    encode_jitted(**A       ).to_tuple()

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




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Union[str, Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__       ):
                           _UpperCAmelCase     :  Dict							      =    model_class(A       )
                           _UpperCAmelCase     :  Union[str, Any]							      =    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(A						:				Dict						,					A						:				Tuple						,					A						:				List[str]       ):
                                  return model.decode(
                                      decoder_input_ids=A						,					decoder_attention_mask=A						,					encoder_outputs=A						,					)

                           with self.subTest("JIT Enabled"       ):
                                  _UpperCAmelCase     :  str							      =    decode_jitted(**A       ).to_tuple()

                           with self.subTest("JIT Disabled"       ):
                                  with jax.disable_jit():
                                         _UpperCAmelCase     :  Dict							      =    decode_jitted(**A       ).to_tuple()

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





      @slow
      def      _A	(       self						:				Union[str, Any]       ):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase     :  int							      =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"       )
                    # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                    _UpperCAmelCase     :  Dict							      =    np.ones((1, 1)       ) * model.config.eos_token_id
                    _UpperCAmelCase     :  Optional[int]							      =    model(A       )
                    self.assertIsNotNone(A       )


'''simple docstring'''




import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

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

      def __init__(       self						:				Optional[int]						,					A						:				Union[List[ControlNetModel], Tuple[ControlNetModel]]       ):
             super().__init__()
             _UpperCAmelCase     :  Optional[int]							      =    nn.ModuleList(A       )




      def      _A	(       self						:				Dict						,					A						:				torch.FloatTensor						,					A						:				Union[torch.Tensor, float, int]						,					A						:				torch.Tensor						,					A						:				List[torch.tensor]						,					A						:				List[float]						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[Dict[str, Any]] = None						,					A						:				bool = False						,					A						:				bool = True						,					):
             for i, (image, scale, controlnet) in enumerate(zip(A						,					A						,					self.nets       )       ):
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    controlnet(
                        A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					)

                    # merge samples
                    if i == 0:
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    down_samples, mid_sample
                    else:
                           _UpperCAmelCase     :  Optional[int]							      =    [
                               samples_prev + samples_curr
                               for samples_prev, samples_curr in zip(A						,					A       )
                           ]
                           mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample




      def      _A	(       self						:				List[str]						,					A						:				Union[str, os.PathLike]						,					A						:				bool = True						,					A						:				Callable = None						,					A						:				bool = False						,					A						:				Optional[str] = None						,					):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  str							      =    save_directory
             for controlnet in self.nets:
                    controlnet.save_pretrained(
                        A						,					is_main_process=A						,					save_function=A						,					safe_serialization=A						,					variant=A						,					)

                    idx += 1
                    _UpperCAmelCase     :  Tuple							      =    model_path_to_save + F"""_{idx}"""





      @classmethod
      def      _A	(       cls						:				int						,					A						:				Optional[Union[str, os.PathLike]]						,					**A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  int							      =    []

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             _UpperCAmelCase     :  int							      =    pretrained_model_path
             while os.path.isdir(A       ):
                    _UpperCAmelCase     :  List[str]							      =    ControlNetModel.from_pretrained(A						,					**A       )
                    controlnets.append(A       )

                    idx += 1
                    _UpperCAmelCase     :  Dict							      =    pretrained_model_path + F"""_{idx}"""

             logger.info(F"""{len(A       )} controlnets loaded from {pretrained_model_path}."""       )

             if len(A       ) == 0:
                    raise ValueError(
                        F"""No ControlNets found under {os.path.dirname(A       )}. Expected at least {pretrained_model_path + '_0'}."""       )

             return cls(A       )

