




from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


SCREAMING_SNAKE_CASE:    Optional[Any]											=   logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE:    Optional[int]											=   '''ResNetConfig'''

# Base docstring
SCREAMING_SNAKE_CASE:    int											=   '''microsoft/resnet-50'''
SCREAMING_SNAKE_CASE:    int											=   [1, 2_0_4_8, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE:    Dict											=   '''microsoft/resnet-50'''
SCREAMING_SNAKE_CASE:    str											=   '''tiger cat'''

SCREAMING_SNAKE_CASE:    str											=   [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



class       lowercase_		(tf.keras.layers.Layer   ):



      def __init__( self  :      Tuple				,       snake_case__  :      Any				,       snake_case__  :      List[Any] = 3				,       snake_case__  :      str = 1				,       snake_case__  :      str = "relu"				,       **snake_case__  :      Optional[Any]     ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       kernel_size // 2
        SCREAMING_SNAKE_CASE_							=       tf.keras.layers.ConvaD(
            __lowerCamelCase				,       kernel_size=__lowerCamelCase				,       strides=__lowerCamelCase				,       padding='valid'				,       use_bias=__lowerCamelCase				,       name='convolution'     )
        # Use same default momentum and epsilon as PyTorch equivalent
        SCREAMING_SNAKE_CASE_							=       tf.keras.layers.BatchNormalization(epsilon=1e-5				,       momentum=0.9				,       name='normalization'     )
        SCREAMING_SNAKE_CASE_							=       ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'     )



      def   __a				( self  :      Tuple				,       snake_case__  :      Tuple     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       (self.pad_value, self.pad_value)
        SCREAMING_SNAKE_CASE_							=       tf.pad(__lowerCamelCase				,       [(0, 0), height_pad, width_pad, (0, 0)]     )
        SCREAMING_SNAKE_CASE_							=       self.conv(__lowerCamelCase     )
        return hidden_state






      def   __a				( self  :      Tuple				,       snake_case__  :      List[Any]				,       snake_case__  :      Optional[int] = False     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.convolution(__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.normalization(__lowerCamelCase				,       training=__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.activation(__lowerCamelCase     )
        return hidden_state



class       lowercase_		(tf.keras.layers.Layer   ):



      def __init__( self  :      Any				,       snake_case__  :      Tuple				,       **snake_case__  :      Optional[int]     ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       TFResNetConvLayer(
            config.embedding_size				,       kernel_size=7				,       stride=2				,       activation=config.hidden_act				,       name='embedder'				,       )
        SCREAMING_SNAKE_CASE_							=       tf.keras.layers.MaxPoolaD(pool_size=3				,       strides=2				,       padding='valid'				,       name='pooler'     )
        SCREAMING_SNAKE_CASE_							=       config.num_channels






      def   __a				( self  :      Optional[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      List[Any] = False     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       shape_list(__lowerCamelCase     )
        if tf.executing_eagerly() and num_channels != self.num_channels:
          raise ValueError(
              'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'     )
        SCREAMING_SNAKE_CASE_							=       pixel_values
        SCREAMING_SNAKE_CASE_							=       self.embedder(__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       tf.pad(__lowerCamelCase				,       [[0, 0], [1, 1], [1, 1], [0, 0]]     )
        SCREAMING_SNAKE_CASE_							=       self.pooler(__lowerCamelCase     )
        return hidden_state



class       lowercase_		(tf.keras.layers.Layer   ):



      def __init__( self  :      Dict				,       snake_case__  :      Optional[int]				,       snake_case__  :      List[str] = 2				,       **snake_case__  :      Dict     ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       tf.keras.layers.ConvaD(
            __lowerCamelCase				,       kernel_size=1				,       strides=__lowerCamelCase				,       use_bias=__lowerCamelCase				,       name='convolution'     )
        # Use same default momentum and epsilon as PyTorch equivalent
        SCREAMING_SNAKE_CASE_							=       tf.keras.layers.BatchNormalization(epsilon=1e-5				,       momentum=0.9				,       name='normalization'     )






      def   __a				( self  :      Optional[Any]				,       snake_case__  :      List[str]				,       snake_case__  :      List[Any] = False     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       x
        SCREAMING_SNAKE_CASE_							=       self.convolution(__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.normalization(__lowerCamelCase				,       training=__lowerCamelCase     )
        return hidden_state



class       lowercase_		(tf.keras.layers.Layer   ):



      def __init__( self  :      Union[str, Any]				,       snake_case__  :      List[Any]				,       snake_case__  :      int				,       snake_case__  :      Union[str, Any] = 1				,       snake_case__  :      List[str] = "relu"				,       **snake_case__  :      Union[str, Any]     ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       in_channels != out_channels or stride != 1
        SCREAMING_SNAKE_CASE_							=       TFResNetConvLayer(__lowerCamelCase				,       stride=__lowerCamelCase				,       name='layer.0'     )
        SCREAMING_SNAKE_CASE_							=       TFResNetConvLayer(__lowerCamelCase				,       activation=__lowerCamelCase				,       name='layer.1'     )
        SCREAMING_SNAKE_CASE_							=       (
            TFResNetShortCut(__lowerCamelCase				,       stride=__lowerCamelCase				,       name='shortcut'     )
            if should_apply_shortcut
            else tf.keras.layers.Activation('linear'				,       name='shortcut'     )
        )
        SCREAMING_SNAKE_CASE_							=       ACTaFN[activation]






      def   __a				( self  :      List[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      Tuple = False     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       hidden_state
        SCREAMING_SNAKE_CASE_							=       self.conva(__lowerCamelCase				,       training=__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.conva(__lowerCamelCase				,       training=__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.shortcut(__lowerCamelCase				,       training=__lowerCamelCase     )
        hidden_state += residual
        SCREAMING_SNAKE_CASE_							=       self.activation(__lowerCamelCase     )
        return hidden_state



class       lowercase_		(tf.keras.layers.Layer   ):



      def __init__( self  :      int				,       snake_case__  :      Union[str, Any]				,       snake_case__  :      int				,       snake_case__  :      int = 1				,       snake_case__  :      int = "relu"				,       snake_case__  :      Optional[Any] = 4				,       **snake_case__  :      Union[str, Any]				,       ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       in_channels != out_channels or stride != 1
        SCREAMING_SNAKE_CASE_							=       out_channels // reduction
        SCREAMING_SNAKE_CASE_							=       TFResNetConvLayer(__lowerCamelCase				,       kernel_size=1				,       name='layer.0'     )
        SCREAMING_SNAKE_CASE_							=       TFResNetConvLayer(__lowerCamelCase				,       stride=__lowerCamelCase				,       name='layer.1'     )
        SCREAMING_SNAKE_CASE_							=       TFResNetConvLayer(__lowerCamelCase				,       kernel_size=1				,       activation=__lowerCamelCase				,       name='layer.2'     )
        SCREAMING_SNAKE_CASE_							=       (
            TFResNetShortCut(__lowerCamelCase				,       stride=__lowerCamelCase				,       name='shortcut'     )
            if should_apply_shortcut
            else tf.keras.layers.Activation('linear'				,       name='shortcut'     )
        )
        SCREAMING_SNAKE_CASE_							=       ACTaFN[activation]






      def   __a				( self  :      Any				,       snake_case__  :      Any				,       snake_case__  :      str = False     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       hidden_state
        SCREAMING_SNAKE_CASE_							=       self.conva(__lowerCamelCase				,       training=__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.conva(__lowerCamelCase				,       training=__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.conva(__lowerCamelCase				,       training=__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.shortcut(__lowerCamelCase				,       training=__lowerCamelCase     )
        hidden_state += residual
        SCREAMING_SNAKE_CASE_							=       self.activation(__lowerCamelCase     )
        return hidden_state



class       lowercase_		(tf.keras.layers.Layer   ):



      def __init__( self  :      Dict				,       snake_case__  :      List[str]				,       snake_case__  :      Optional[int]				,       snake_case__  :      List[Any]				,       snake_case__  :      List[Any] = 2				,       snake_case__  :      Dict = 2				,       **snake_case__  :      str     ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )

        SCREAMING_SNAKE_CASE_							=       TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

        SCREAMING_SNAKE_CASE_							=       [layer(__lowerCamelCase				,       __lowerCamelCase				,       stride=__lowerCamelCase				,       activation=config.hidden_act				,       name='layers.0'     )]
        layers += [
            layer(__lowerCamelCase				,       __lowerCamelCase				,       activation=config.hidden_act				,       name=f'''layers.{i + 1}'''     )
            for i in range(depth - 1     )
        ]
        SCREAMING_SNAKE_CASE_							=       layers






      def   __a				( self  :      Optional[Any]				,       snake_case__  :      List[str]				,       snake_case__  :      Tuple = False     ):

        """simple docstring"""

        for layer in self.stage_layers:
          SCREAMING_SNAKE_CASE_							=       layer(__lowerCamelCase				,       training=__lowerCamelCase     )
        return hidden_state



class       lowercase_		(tf.keras.layers.Layer   ):



      def __init__( self  :      Tuple				,       snake_case__  :      int				,       **snake_case__  :      List[Any]     ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )
        # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
        SCREAMING_SNAKE_CASE_							=       [
            TFResNetStage(
                __lowerCamelCase				,       config.embedding_size				,       config.hidden_sizes[0]				,       stride=2 if config.downsample_in_first_stage else 1				,       depth=config.depths[0]				,       name='stages.0'				,       )
        ]
        for i, (in_channels, out_channels, depth) in enumerate(
            zip(config.hidden_sizes				,       config.hidden_sizes[1:]				,       config.depths[1:]     )     ):
          self.stages.append(TFResNetStage(__lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       depth=__lowerCamelCase				,       name=f'''stages.{i + 1}'''     )     )






      def   __a				( self  :      Optional[Any]				,       snake_case__  :      Optional[int]				,       snake_case__  :      Union[str, Any] = False				,       snake_case__  :      Dict = True				,       snake_case__  :      Dict = False				,       ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       () if output_hidden_states else None

        for stage_module in self.stages:
          if output_hidden_states:
            SCREAMING_SNAKE_CASE_							=       hidden_states + (hidden_state,)

          SCREAMING_SNAKE_CASE_							=       stage_module(__lowerCamelCase				,       training=__lowerCamelCase     )

        if output_hidden_states:
          SCREAMING_SNAKE_CASE_							=       hidden_states + (hidden_state,)

        if not return_dict:
          return tuple(v for v in [hidden_state, hidden_states] if v is not None     )

        return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowerCamelCase				,       hidden_states=__lowerCamelCase     )



class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      lowerCAmelCase__ 			=ResNetConfig
      lowerCAmelCase__ 			="resnet"
      lowerCAmelCase__ 			="pixel_values"



      @property
      def   __a				( self  :      Optional[Any]     ):

        """simple docstring"""

        return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)				,       dtype=tf.floataa     )}

SCREAMING_SNAKE_CASE:    Dict											=   r'''\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'''


SCREAMING_SNAKE_CASE:    Optional[Any]											=   r'''\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'''



@keras_serializable
class       lowercase_		(tf.keras.layers.Layer   ):
      lowerCAmelCase__ 			=ResNetConfig



      def __init__( self  :      int				,       snake_case__  :      Union[str, Any]				,       **snake_case__  :      Optional[int]     ):

        """simple docstring"""

        super().__init__(**__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       config
        SCREAMING_SNAKE_CASE_							=       TFResNetEmbeddings(__lowerCamelCase				,       name='embedder'     )
        SCREAMING_SNAKE_CASE_							=       TFResNetEncoder(__lowerCamelCase				,       name='encoder'     )
        SCREAMING_SNAKE_CASE_							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCamelCase     )






      @unpack_inputs
      def   __a				( self  :      List[str]				,       snake_case__  :      Any				,       snake_case__  :      Any = None				,       snake_case__  :      List[str] = None				,       snake_case__  :      Tuple = False				,       ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        SCREAMING_SNAKE_CASE_							=       return_dict if return_dict is not None else self.config.use_return_dict

        # TF 2.0 image layers can't use NCHW format when running on CPU.
        # We transpose to NHWC format and then transpose back after the full forward pass.
        # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
        SCREAMING_SNAKE_CASE_							=       tf.transpose(__lowerCamelCase				,       perm=[0, 2, 3, 1]     )
        SCREAMING_SNAKE_CASE_							=       self.embedder(__lowerCamelCase				,       training=__lowerCamelCase     )

        SCREAMING_SNAKE_CASE_							=       self.encoder(
            __lowerCamelCase				,       output_hidden_states=__lowerCamelCase				,       return_dict=__lowerCamelCase				,       training=__lowerCamelCase     )

        SCREAMING_SNAKE_CASE_							=       encoder_outputs[0]

        SCREAMING_SNAKE_CASE_							=       self.pooler(__lowerCamelCase     )

        # Transpose all the outputs to the NCHW format
        # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
        SCREAMING_SNAKE_CASE_							=       tf.transpose(__lowerCamelCase				,       (0, 3, 1, 2)     )
        SCREAMING_SNAKE_CASE_							=       tf.transpose(__lowerCamelCase				,       (0, 3, 1, 2)     )
        SCREAMING_SNAKE_CASE_							=       ()
        for hidden_state in encoder_outputs[1:]:
          SCREAMING_SNAKE_CASE_							=       hidden_states + tuple(tf.transpose(__lowerCamelCase				,       (0, 3, 1, 2)     ) for h in hidden_state     )

        if not return_dict:
          return (last_hidden_state, pooled_output) + hidden_states

        SCREAMING_SNAKE_CASE_							=       hidden_states if output_hidden_states else None

        return TFBaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=__lowerCamelCase				,       pooler_output=__lowerCamelCase				,       hidden_states=__lowerCamelCase				,       )



@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."       ,  SCREAMING_SNAKE_CASE__       ,  )
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):



      def __init__( self  :      int				,       snake_case__  :      Tuple				,       **snake_case__  :      List[str]     ):

        """simple docstring"""

        super().__init__(__lowerCamelCase				,       **__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       TFResNetMainLayer(config=__lowerCamelCase				,       name='resnet'     )






      @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				,       )
      @unpack_inputs
      def   __a				( self  :      List[str]				,       snake_case__  :      Any				,       snake_case__  :      str = None				,       snake_case__  :      Optional[Any] = None				,       snake_case__  :      List[str] = False				,       ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        SCREAMING_SNAKE_CASE_							=       return_dict if return_dict is not None else self.config.use_return_dict

        SCREAMING_SNAKE_CASE_							=       self.resnet(
            pixel_values=__lowerCamelCase				,       output_hidden_states=__lowerCamelCase				,       return_dict=__lowerCamelCase				,       training=__lowerCamelCase				,       )
        return resnet_outputs



@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "       ,  SCREAMING_SNAKE_CASE__       ,  )
class       lowercase_		(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__   ):



      def __init__( self  :      Dict				,       snake_case__  :      List[str]				,       **snake_case__  :      Dict     ):

        """simple docstring"""

        super().__init__(__lowerCamelCase				,       **__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       config.num_labels
        SCREAMING_SNAKE_CASE_							=       TFResNetMainLayer(__lowerCamelCase				,       name='resnet'     )
        # classification head
        SCREAMING_SNAKE_CASE_							=       (
            tf.keras.layers.Dense(config.num_labels				,       name='classifier.1'     )
            if config.num_labels > 0
            else tf.keras.layers.Activation('linear'				,       name='classifier.1'     )
        )



      def   __a				( self  :      Optional[int]				,       snake_case__  :      Any     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       tf.keras.layers.Flatten()(__lowerCamelCase     )
        SCREAMING_SNAKE_CASE_							=       self.classifier_layer(__lowerCamelCase     )
        return logits






      @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				,       )
      @unpack_inputs
      def   __a				( self  :      int				,       snake_case__  :      Union[str, Any] = None				,       snake_case__  :      Any = None				,       snake_case__  :      str = None				,       snake_case__  :      Optional[Any] = None				,       snake_case__  :      List[str] = False				,       ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       return_dict if return_dict is not None else self.config.use_return_dict

        SCREAMING_SNAKE_CASE_							=       self.resnet(
            __lowerCamelCase				,       output_hidden_states=__lowerCamelCase				,       return_dict=__lowerCamelCase				,       training=__lowerCamelCase     )

        SCREAMING_SNAKE_CASE_							=       outputs.pooler_output if return_dict else outputs[1]

        SCREAMING_SNAKE_CASE_							=       self.classifier(__lowerCamelCase     )

        SCREAMING_SNAKE_CASE_							=       None if labels is None else self.hf_compute_loss(__lowerCamelCase				,       __lowerCamelCase     )

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

        return TFImageClassifierOutputWithNoAttention(loss=__lowerCamelCase				,       logits=__lowerCamelCase				,       hidden_states=outputs.hidden_states     )





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE:    List[str]											=   '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''



class       lowercase_		(unittest.TestCase       ,  SCREAMING_SNAKE_CASE__   ):



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'     )
								self.tool.setup()
								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'				,       remote=snake_case__     )



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )






						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )