





"""simple docstring"""
from typing import Optional, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


lowerCamelCase_								=		logging.get_logger(__name__)


# General docstring
lowerCamelCase_								=		"""MobileNetV1Config"""

# Base docstring
lowerCamelCase_								=		"""google/mobilenet_v1_1.0_224"""
lowerCamelCase_								=		[1, 1_0_2_4, 7, 7]

# Image classification docstring
lowerCamelCase_								=		"""google/mobilenet_v1_1.0_224"""
lowerCamelCase_								=		"""tabby, tabby cat"""


lowerCamelCase_								=		[
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		List[Any] ,   a_     :		Optional[Any]=None							)			->      int:
      __SCREAMING_SNAKE_CASE   :List[str]						=  {}

      if isinstance(_UpperCAmelCase ,   _UpperCAmelCase							):
            __SCREAMING_SNAKE_CASE   :Any						=  model.mobilenet_va
      else:
            __SCREAMING_SNAKE_CASE   :int						=  model

      __SCREAMING_SNAKE_CASE   :Dict						=  "MobilenetV1/Conv2d_0/"
      __SCREAMING_SNAKE_CASE   :str						=  backbone.conv_stem.convolution.weight
      __SCREAMING_SNAKE_CASE   :List[str]						=  backbone.conv_stem.normalization.bias
      __SCREAMING_SNAKE_CASE   :int						=  backbone.conv_stem.normalization.weight
      __SCREAMING_SNAKE_CASE   :List[str]						=  backbone.conv_stem.normalization.running_mean
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  backbone.conv_stem.normalization.running_var

      for i in range(13							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  i + 1
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  i * 2

            __SCREAMING_SNAKE_CASE   :Any						=  backbone.layer[pt_index]
            __SCREAMING_SNAKE_CASE   :Any						=  f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
            __SCREAMING_SNAKE_CASE   :Any						=  pointer.convolution.weight
            __SCREAMING_SNAKE_CASE   :Any						=  pointer.normalization.bias
            __SCREAMING_SNAKE_CASE   :str						=  pointer.normalization.weight
            __SCREAMING_SNAKE_CASE   :Dict						=  pointer.normalization.running_mean
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  pointer.normalization.running_var

            __SCREAMING_SNAKE_CASE   :Tuple						=  backbone.layer[pt_index + 1]
            __SCREAMING_SNAKE_CASE   :List[str]						=  f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
            __SCREAMING_SNAKE_CASE   :int						=  pointer.convolution.weight
            __SCREAMING_SNAKE_CASE   :Any						=  pointer.normalization.bias
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  pointer.normalization.weight
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  pointer.normalization.running_mean
            __SCREAMING_SNAKE_CASE   :Dict						=  pointer.normalization.running_var

      if isinstance(_UpperCAmelCase ,   _UpperCAmelCase							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  "MobilenetV1/Logits/Conv2d_1c_1x1/"
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  model.classifier.weight
            __SCREAMING_SNAKE_CASE   :Tuple						=  model.classifier.bias

      return tf_to_pt_map



def      __lowerCamelCase   (							a_     :		Optional[Any] ,   a_     :		Optional[int] ,   a_     :		str							)			->      str:
      try:
            import numpy as np
            import tensorflow as tf
      except ImportError:
            logger.error(
                '''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
                '''https://www.tensorflow.org/install/ for installation instructions.'''							)
            raise

      # Load weights from TF model
      __SCREAMING_SNAKE_CASE   :int						=  tf.train.list_variables(_UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :int						=  {}
      for name, shape in init_vars:
            logger.info(f'''Loading TF weight {name} with shape {shape}'''							)
            __SCREAMING_SNAKE_CASE   :Any						=  tf.train.load_variable(_UpperCAmelCase ,   _UpperCAmelCase							)
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  array

      # Build TF to PyTorch weights loading map
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  _build_tf_to_pytorch_map(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase							)

      for name, pointer in tf_to_pt_map.items():
            logger.info(f'''Importing {name}'''							)
            if name not in tf_weights:
                  logger.info(f'''{name} not in tf pre-trained weights, skipping'''							)
                  continue

            __SCREAMING_SNAKE_CASE   :int						=  tf_weights[name]

            if "depthwise_weights" in name:
                  logger.info('''Transposing depthwise'''							)
                  __SCREAMING_SNAKE_CASE   :int						=  np.transpose(_UpperCAmelCase ,   (2, 3, 0, 1)							)
            elif "weights" in name:
                  logger.info('''Transposing'''							)
                  if len(pointer.shape							) == 2:  # copying into linear layer
                        __SCREAMING_SNAKE_CASE   :List[str]						=  array.squeeze().transpose()
                  else:
                        __SCREAMING_SNAKE_CASE   :Any						=  np.transpose(_UpperCAmelCase ,   (3, 2, 0, 1)							)

            if pointer.shape != array.shape:
                  raise ValueError(f'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched'''							)

            logger.info(f'''Initialize PyTorch weight {name} {array.shape}'''							)
            __SCREAMING_SNAKE_CASE   :int						=  torch.from_numpy(_UpperCAmelCase							)

            tf_weights.pop(_UpperCAmelCase ,   _UpperCAmelCase							)
            tf_weights.pop(name + '''/RMSProp''' ,   _UpperCAmelCase							)
            tf_weights.pop(name + '''/RMSProp_1''' ,   _UpperCAmelCase							)
            tf_weights.pop(name + '''/ExponentialMovingAverage''' ,   _UpperCAmelCase							)

      logger.info(f'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys()							)}'''							)
      return model



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Union[str, Any]							)			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  features.shape[-2:]
      __SCREAMING_SNAKE_CASE   :List[str]						=  conv_layer.stride
      __SCREAMING_SNAKE_CASE   :Any						=  conv_layer.kernel_size

      if in_height % stride_height == 0:
            __SCREAMING_SNAKE_CASE   :int						=  max(kernel_height - stride_height ,   0							)
      else:
            __SCREAMING_SNAKE_CASE   :Tuple						=  max(kernel_height - (in_height % stride_height) ,   0							)

      if in_width % stride_width == 0:
            __SCREAMING_SNAKE_CASE   :str						=  max(kernel_width - stride_width ,   0							)
      else:
            __SCREAMING_SNAKE_CASE   :Dict						=  max(kernel_width - (in_width % stride_width) ,   0							)

      __SCREAMING_SNAKE_CASE   :str						=  pad_along_width // 2
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  pad_along_width - pad_left
      __SCREAMING_SNAKE_CASE   :int						=  pad_along_height // 2
      __SCREAMING_SNAKE_CASE   :Tuple						=  pad_along_height - pad_top

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  (pad_left, pad_right, pad_top, pad_bottom)
      return nn.functional.pad(_UpperCAmelCase ,   _UpperCAmelCase ,   '''constant''' ,   0.0							)






class 					_SCREAMING_SNAKE_CASE(   nn.Module  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = True		,)			->      Tuple:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  config

             if in_channels % groups != 0:
                   raise ValueError(f'''Input channels ({in_channels}) are not divisible by {groups} groups.'''							)
             if out_channels % groups != 0:
                   raise ValueError(f'''Output channels ({out_channels}) are not divisible by {groups} groups.'''							)

             __SCREAMING_SNAKE_CASE   :int						=  0 if config.tf_padding else int((kernel_size - 1) / 2							)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  nn.Convad(
                 in_channels=lowerCAmelCase__		,out_channels=lowerCAmelCase__		,kernel_size=lowerCAmelCase__		,stride=lowerCAmelCase__		,padding=lowerCAmelCase__		,groups=lowerCAmelCase__		,bias=lowerCAmelCase__		,padding_mode='''zeros'''		,)

             if use_normalization:
                   __SCREAMING_SNAKE_CASE   :str						=  nn.BatchNormad(
                       num_features=lowerCAmelCase__		,eps=config.layer_norm_eps		,momentum=0.9_9_9_7		,affine=lowerCAmelCase__		,track_running_stats=lowerCAmelCase__		,)
             else:
                   __SCREAMING_SNAKE_CASE   :str						=  None

             if use_activation:
                   if isinstance(lowerCAmelCase__		,lowerCAmelCase__							):
                         __SCREAMING_SNAKE_CASE   :Dict						=  ACTaFN[use_activation]
                   elif isinstance(config.hidden_act		,lowerCAmelCase__							):
                         __SCREAMING_SNAKE_CASE   :Dict						=  ACTaFN[config.hidden_act]
                   else:
                         __SCREAMING_SNAKE_CASE   :Any						=  config.hidden_act
             else:
                   __SCREAMING_SNAKE_CASE   :int						=  None




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Any:





             """simple docstring"""






             if self.config.tf_padding:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  apply_tf_padding(lowerCAmelCase__		,self.convolution							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.convolution(lowerCAmelCase__							)
             if self.normalization is not None:
                   __SCREAMING_SNAKE_CASE   :int						=  self.normalization(lowerCAmelCase__							)
             if self.activation is not None:
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  self.activation(lowerCAmelCase__							)
             return features






class 					_SCREAMING_SNAKE_CASE(   UpperCAmelCase_  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  MobileNetVaConfig
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  load_tf_weights_in_mobilenet_va
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  '''mobilenet_v1'''
       SCREAMING_SNAKE_CASE_   :      int							=  '''pixel_values'''
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  False




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Optional[Any]:





             """simple docstring"""






             if isinstance(lowerCAmelCase__		,(nn.Linear, nn.Convad)							):
                   module.weight.data.normal_(mean=0.0		,std=self.config.initializer_range							)
                   if module.bias is not None:
                         module.bias.data.zero_()
             elif isinstance(lowerCAmelCase__		,nn.BatchNormad							):
                   module.bias.data.zero_()
                   module.weight.data.fill_(1.0							)




lowerCamelCase_								=		R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCamelCase_								=		R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""






@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''    ,	UpperCAmelCase_    ,	)
class 					_SCREAMING_SNAKE_CASE(   UpperCAmelCase_  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True							)			->      Optional[int]:





             """simple docstring"""






             super().__init__(lowerCAmelCase__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  config

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  32
             __SCREAMING_SNAKE_CASE   :Dict						=  max(int(depth * config.depth_multiplier							)		,config.min_depth							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  MobileNetVaConvLayer(
                 lowerCAmelCase__		,in_channels=config.num_channels		,out_channels=lowerCAmelCase__		,kernel_size=3		,stride=2		,)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

             __SCREAMING_SNAKE_CASE   :str						=  nn.ModuleList()
             for i in range(13							):
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  out_channels

                   if strides[i] == 2 or i == 0:
                         depth *= 2
                         __SCREAMING_SNAKE_CASE   :str						=  max(int(depth * config.depth_multiplier							)		,config.min_depth							)

                   self.layer.append(
                       MobileNetVaConvLayer(
                           lowerCAmelCase__		,in_channels=lowerCAmelCase__		,out_channels=lowerCAmelCase__		,kernel_size=3		,stride=strides[i]		,groups=lowerCAmelCase__		,)							)

                   self.layer.append(
                       MobileNetVaConvLayer(
                           lowerCAmelCase__		,in_channels=lowerCAmelCase__		,out_channels=lowerCAmelCase__		,kernel_size=1		,)							)

             __SCREAMING_SNAKE_CASE   :List[str]						=  nn.AdaptiveAvgPoolad((1, 1)							) if add_pooling_layer else None

             # Initialize weights and apply final processing
             self.post_init()




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             raise NotImplementedError




       @add_start_docstrings_to_model_forward(lowerCAmelCase__							)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC		,output_type=lowerCAmelCase__		,config_class=_CONFIG_FOR_DOC		,modality='''vision'''		,expected_output=_EXPECTED_OUTPUT_SHAPE		,)
       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  (
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             __SCREAMING_SNAKE_CASE   :Any						=  return_dict if return_dict is not None else self.config.use_return_dict

             if pixel_values is None:
                   raise ValueError('''You have to specify pixel_values'''							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.conv_stem(lowerCAmelCase__							)

             __SCREAMING_SNAKE_CASE   :List[Any]						=  () if output_hidden_states else None

             for i, layer_module in enumerate(self.layer							):
                   __SCREAMING_SNAKE_CASE   :Tuple						=  layer_module(lowerCAmelCase__							)

                   if output_hidden_states:
                         __SCREAMING_SNAKE_CASE   :Optional[int]						=  all_hidden_states + (hidden_states,)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  hidden_states

             if self.pooler is not None:
                   __SCREAMING_SNAKE_CASE   :int						=  torch.flatten(self.pooler(lowerCAmelCase__							)		,start_dim=1							)
             else:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  None

             if not return_dict:
                   return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None							)

             return BaseModelOutputWithPoolingAndNoAttention(
                 last_hidden_state=lowerCAmelCase__		,pooler_output=lowerCAmelCase__		,hidden_states=lowerCAmelCase__		,)






@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''    ,	UpperCAmelCase_    ,	)
class 					_SCREAMING_SNAKE_CASE(   UpperCAmelCase_  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__							)			->      Dict:





             """simple docstring"""






             super().__init__(lowerCAmelCase__							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  config.num_labels
             __SCREAMING_SNAKE_CASE   :Dict						=  MobileNetVaModel(lowerCAmelCase__							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  self.mobilenet_va.layer[-1].convolution.out_channels

             # Classifier head
             __SCREAMING_SNAKE_CASE   :str						=  nn.Dropout(config.classifier_dropout_prob		,inplace=lowerCAmelCase__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  nn.Linear(lowerCAmelCase__		,config.num_labels							) if config.num_labels > 0 else nn.Identity()

             # Initialize weights and apply final processing
             self.post_init()




       @add_start_docstrings_to_model_forward(lowerCAmelCase__							)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT		,output_type=lowerCAmelCase__		,config_class=_CONFIG_FOR_DOC		,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,)
       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  return_dict if return_dict is not None else self.config.use_return_dict

             __SCREAMING_SNAKE_CASE   :List[str]						=  self.mobilenet_va(lowerCAmelCase__		,output_hidden_states=lowerCAmelCase__		,return_dict=lowerCAmelCase__							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  outputs.pooler_output if return_dict else outputs[1]

             __SCREAMING_SNAKE_CASE   :Tuple						=  self.classifier(self.dropout(lowerCAmelCase__							)							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  None
             if labels is not None:
                   if self.config.problem_type is None:
                         if self.num_labels == 1:
                               __SCREAMING_SNAKE_CASE   :List[Any]						=  "regression"
                         elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                               __SCREAMING_SNAKE_CASE   :int						=  "single_label_classification"
                         else:
                               __SCREAMING_SNAKE_CASE   :str						=  "multi_label_classification"

                   if self.config.problem_type == "regression":
                         __SCREAMING_SNAKE_CASE   :Dict						=  MSELoss()
                         if self.num_labels == 1:
                               __SCREAMING_SNAKE_CASE   :Any						=  loss_fct(logits.squeeze()		,labels.squeeze()							)
                         else:
                               __SCREAMING_SNAKE_CASE   :int						=  loss_fct(lowerCAmelCase__		,lowerCAmelCase__							)
                   elif self.config.problem_type == "single_label_classification":
                         __SCREAMING_SNAKE_CASE   :Any						=  CrossEntropyLoss()
                         __SCREAMING_SNAKE_CASE   :Dict						=  loss_fct(logits.view(-1		,self.num_labels							)		,labels.view(-1							)							)
                   elif self.config.problem_type == "multi_label_classification":
                         __SCREAMING_SNAKE_CASE   :Dict						=  BCEWithLogitsLoss()
                         __SCREAMING_SNAKE_CASE   :Dict						=  loss_fct(lowerCAmelCase__		,lowerCAmelCase__							)

             if not return_dict:
                   __SCREAMING_SNAKE_CASE   :int						=  (logits,) + outputs[2:]
                   return ((loss,) + output) if loss is not None else output

             return ImageClassifierOutputWithNoAttention(
                 loss=lowerCAmelCase__		,logits=lowerCAmelCase__		,hidden_states=outputs.hidden_states		,)


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
						import torch



@is_pipeline_test
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			_UpperCAmelCase   :							str										=     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       TextaTextGenerationPipeline(model=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)
									return generator, ["Something to write", "Something else"]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there")
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ANY(lowerCAmelCase__)}])
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))

									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator(["This is great !", "Something else"]   ,						num_return_sequences=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       generator(
									    ["This is great !", "Something else"]   ,						num_return_sequences=2   ,						batch_size=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									with self.assertRaises(lowerCAmelCase__):
															generator(4)



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="pt")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       3
									SCREAMING_SNAKE_CASE_: Any  		=       generator(
									    "Something there"   ,						num_return_sequences=lowerCAmelCase__   ,						num_beams=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       [
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": ""},
									]
									self.assertEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       generator("This is a test"   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						return_tensors=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        {"generated_token_ids": ANY(torch.Tensor)},
									        {"generated_token_ids": ANY(torch.Tensor)},
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: str  		=       generator.model.config.eos_token_id
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: Tuple  		=       generator(
									    ["This is a test", "This is a second test"]   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						batch_size=2   ,						return_tensors=lowerCAmelCase__   ,						)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									    ]   ,						)




			@require_tf
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="tf")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])






