




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class 			a__       (					unittest.TestCase      ):







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      tempfile.mkdtemp()

          a      =      CLIPImageProcessor()
          a      =      BertTokenizer.from_pretrained(
              "hf-internal-testing/tiny-random-BertModel"  ,		model_input_names=["input_ids", "attention_mask"] )

          a      =      GitProcessor(A  ,		A )

          processor.save_pretrained(self.tmpdirname )







     def 			lowerCAmelCase_				(	self  ,		**A )  ->    str:



          '''simple docstring'''



          return AutoProcessor.from_pretrained(self.tmpdirname  ,		**A ).tokenizer







     def 			lowerCAmelCase_				(	self  ,		**A )  ->    str:



          '''simple docstring'''



          return AutoProcessor.from_pretrained(self.tmpdirname  ,		**A ).image_processor







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          shutil.rmtree(self.tmpdirname )







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          a      =      [np.random.randint(255  ,		size=(3, 30, 400)  ,		dtype=np.uinta )]

          a      =      [Image.fromarray(np.moveaxis(A  ,		0  ,		-1 ) ) for x in image_inputs]

          return image_inputs







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



          a      =      GitProcessor(tokenizer=self.get_tokenizer()  ,		image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          a      =      self.get_tokenizer(bos_token="(BOS)"  ,		eos_token="(EOS)" )
          a      =      self.get_image_processor(do_normalize=A  ,		padding_value=1.0 )

          a      =      GitProcessor.from_pretrained(
              self.tmpdirname  ,		bos_token="(BOS)"  ,		eos_token="(EOS)"  ,		do_normalize=A  ,		padding_value=1.0 )

          self.assertEqual(processor.tokenizer.get_vocab()  ,		tokenizer_add_kwargs.get_vocab() )
          self.assertIsInstance(processor.tokenizer  ,		A )

          self.assertEqual(processor.image_processor.to_json_string()  ,		image_processor_add_kwargs.to_json_string() )
          self.assertIsInstance(processor.image_processor  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      self.get_image_processor()
          a      =      self.get_tokenizer()

          a      =      GitProcessor(tokenizer=A  ,		image_processor=A )

          a      =      self.prepare_image_inputs()

          a      =      image_processor(A  ,		return_tensors="np" )
          a      =      processor(images=A  ,		return_tensors="np" )

          for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()  ,		input_processor[key].sum()  ,		delta=1e-2 )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.get_image_processor()
          a      =      self.get_tokenizer()

          a      =      GitProcessor(tokenizer=A  ,		image_processor=A )

          a      =      "lower newer"

          a      =      processor(text=A )

          a      =      tokenizer(A  ,		return_token_type_ids=A )

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]  ,		encoded_processor[key] )







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a      =      self.get_image_processor()
          a      =      self.get_tokenizer()

          a      =      GitProcessor(tokenizer=A  ,		image_processor=A )

          a      =      "lower newer"
          a      =      self.prepare_image_inputs()

          a      =      processor(text=A  ,		images=A )

          self.assertListEqual(list(inputs.keys() )  ,		["input_ids", "attention_mask", "pixel_values"] )

          # test if it raises when no input is passed
          with pytest.raises(A ):
               processor()







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          a      =      self.get_image_processor()
          a      =      self.get_tokenizer()

          a      =      GitProcessor(tokenizer=A  ,		image_processor=A )

          a      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          a      =      processor.batch_decode(A )
          a      =      tokenizer.batch_decode(A )

          self.assertListEqual(A  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.get_image_processor()
          a      =      self.get_tokenizer()

          a      =      GitProcessor(tokenizer=A  ,		image_processor=A )

          a      =      "lower newer"
          a      =      self.prepare_image_inputs()

          a      =      processor(text=A  ,		images=A )

          # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
          self.assertListEqual(list(inputs.keys() )  ,		["input_ids", "attention_mask", "pixel_values"] )







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


lowercase__      :			int           =					logging.get_logger(__name__)

# General docstring
lowercase__      :			str           =					"ResNetConfig"

# Base docstring
lowercase__      :			Dict           =					"microsoft/resnet-50"
lowercase__      :			Dict           =					[1, 2_048, 7, 7]

# Image classification docstring
lowercase__      :			Optional[Any]           =					"microsoft/resnet-50"
lowercase__      :			str           =					"tiger cat"

lowercase__      :			Union[str, Any]           =					[
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]




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







     def __init__(	self  ,		A  ,		A = 3  ,		A = 1  ,		A = "relu"  ,		**A )  ->    None:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    tf.Tensor:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      self.convolution(A )
          a      =      self.normalization(A  ,		training=A )
          a      =      self.activation(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a  ,      a  ,      a  ,      a      =      shape_list(A )
          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." )
          a      =      pixel_values
          a      =      self.embedder(A )
          a      =      tf.pad(A  ,		[[0, 0], [1, 1], [1, 1], [0, 0]] )
          a      =      self.pooler(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A = 2  ,		**A )  ->    None:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      x
          a      =      self.convolution(A )
          a      =      self.normalization(A  ,		training=A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A  ,		A = 1  ,		A = "relu"  ,		**A )  ->    None:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      hidden_state
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.shortcut(A  ,		training=A )
          hidden_state += residual
          a      =      self.activation(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A  ,		A = 1  ,		A = "relu"  ,		A = 4  ,		**A  ,		)  ->    None:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      hidden_state
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.shortcut(A  ,		training=A )
          hidden_state += residual
          a      =      self.activation(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A  ,		A  ,		A = 2  ,		A = 2  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )

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

          a      =      [layer(A  ,		A  ,		stride=A  ,		activation=config.hidden_act  ,		name="layers.0" )]
          layers += [
              layer(A  ,		A  ,		activation=config.hidden_act  ,		name=F'''layers.{i + 1}''' )
              for i in range(depth - 1 )
          ]
          a      =      layers







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          for layer in self.stage_layers:
               a      =      layer(A  ,		training=A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
          a      =      [
              TFResNetStage(
                  A  ,		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(A  ,		A  ,		A  ,		depth=A  ,		name=F'''stages.{i + 1}''' ) )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False  ,		A = True  ,		A = False  ,		)  ->    TFBaseModelOutputWithNoAttention:



          '''simple docstring'''



          a      =      () if output_hidden_states else None

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

               a      =      stage_module(A  ,		training=A )

          if output_hidden_states:
               a      =      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=A  ,		hidden_states=A )







class 			a__       (					UpperCamelCase__      ):
     a		:			List[Any]							=	ResNetConfig
     a		:			str							=	"""resnet"""
     a		:			List[str]							=	"""pixel_values"""







     @property
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)  ,		dtype=tf.floataa )}







lowercase__      :			Any           =					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"


lowercase__      :			Dict           =					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 			a__       (					tf.keras.layers.Layer      ):
     a		:			List[str]							=	ResNetConfig







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      config
          a      =      TFResNetEmbeddings(A  ,		name="embedder" )
          a      =      TFResNetEncoder(A  ,		name="encoder" )
          a      =      tf.keras.layers.GlobalAveragePoolingaD(keepdims=A )







     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:



          '''simple docstring'''



          a      =      (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          a      =      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)
          a      =      tf.transpose(A  ,		perm=[0, 2, 3, 1] )
          a      =      self.embedder(A  ,		training=A )

          a      =      self.encoder(
              A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A )

          a      =      encoder_outputs[0]

          a      =      self.pooler(A )

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

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

          a      =      hidden_states if output_hidden_states else None

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







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(A  ,		**A )
          a      =      TFResNetMainLayer(config=A  ,		name="resnet" )







     @add_start_docstrings_to_model_forward(A )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC  ,		output_type=A  ,		config_class=_CONFIG_FOR_DOC  ,		modality="vision"  ,		expected_output=_EXPECTED_OUTPUT_SHAPE  ,		)
     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:



          '''simple docstring'''



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

          a      =      self.resnet(
              pixel_values=A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A  ,		)
          return resnet_outputs







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(A  ,		**A )
          a      =      config.num_labels
          a      =      TFResNetMainLayer(A  ,		name="resnet" )
          # classification head
          a      =      (
              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 			lowerCAmelCase_				(	self  ,		A )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      tf.keras.layers.Flatten()(A )
          a      =      self.classifier_layer(A )
          return logits







     @add_start_docstrings_to_model_forward(A )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT  ,		output_type=A  ,		config_class=_CONFIG_FOR_DOC  ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,		)
     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:



          '''simple docstring'''



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

          a      =      self.resnet(
              A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A )

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

          a      =      self.classifier(A )

          a      =      None if labels is None else self.hf_compute_loss(A  ,		A )

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

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

