



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFResNetForImageClassification, TFResNetModel
  from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor



class 			__lowerCAmelCase  :




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase=3						,					__UpperCAmelCase=32						,					__UpperCAmelCase=3						,					__UpperCAmelCase=10						,					__UpperCAmelCase=[10, 20, 30, 40]						,					__UpperCAmelCase=[1, 1, 2, 1]						,					__UpperCAmelCase=True						,					__UpperCAmelCase=True						,					__UpperCAmelCase="relu"						,					__UpperCAmelCase=3						,					__UpperCAmelCase=None						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     parent
    __lowerCamelCase    				=     batch_size
    __lowerCamelCase    				=     image_size
    __lowerCamelCase    				=     num_channels
    __lowerCamelCase    				=     embeddings_size
    __lowerCamelCase    				=     hidden_sizes
    __lowerCamelCase    				=     depths
    __lowerCamelCase    				=     is_training
    __lowerCamelCase    				=     use_labels
    __lowerCamelCase    				=     hidden_act
    __lowerCamelCase    				=     num_labels
    __lowerCamelCase    				=     scope
    __lowerCamelCase    				=     len(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

    __lowerCamelCase    				=     None
    if self.use_labels:
       __lowerCamelCase    				=     ids_tensor([self.batch_size]						,					self.num_labels					)

    __lowerCamelCase    				=     self.get_config()

    return config, pixel_values, labels




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return ResNetConfig(
        num_channels=self.num_channels						,					embeddings_size=self.embeddings_size						,					hidden_sizes=self.hidden_sizes						,					depths=self.depths						,					hidden_act=self.hidden_act						,					num_labels=self.num_labels						,					image_size=self.image_size						,					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     TFResNetModel(config=__UpperCAmelCase					)
    __lowerCamelCase    				=     model(__UpperCAmelCase					)
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape						,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.num_labels
    __lowerCamelCase    				=     TFResNetForImageClassification(__UpperCAmelCase					)
    __lowerCamelCase    				=     model(__UpperCAmelCase						,					labels=__UpperCAmelCase					)
    self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.prepare_config_and_inputs()
    __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     config_and_inputs
    __lowerCamelCase    				=     {'''pixel_values''': pixel_values}
    return config, inputs_dict



@require_tf
class 			__lowerCAmelCase  (						lowerCAmelCase__	,						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
 lowerCAmelCase__							       =	(
     {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
     if is_tf_available()
     else {}
 )

 lowerCAmelCase__							       =	False
 lowerCAmelCase__							       =	False
 lowerCAmelCase__							       =	False
 lowerCAmelCase__							       =	False
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     TFResNetModelTester(self					)
    __lowerCamelCase    				=     ConfigTester(self						,					config_class=__UpperCAmelCase						,					has_text_modality=__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return




 @unittest.skip(reason='''ResNet does not use inputs_embeds'''					)
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 @unittest.skip(reason='''ResNet does not support input and output embeddings'''					)
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase				,__lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowerCamelCase    				=     model_class(__UpperCAmelCase					)
       __lowerCamelCase    				=     inspect.signature(model.call					)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowerCamelCase    				=     [*signature.parameters.keys()]

       __lowerCamelCase    				=     ['''pixel_values''']
       self.assertListEqual(arg_names[:1]						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    def check_hidden_states_output(__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					):
       __lowerCamelCase    				=     model_class(__UpperCAmelCase					)
       __lowerCamelCase    				=     model(**self._prepare_for_class(__UpperCAmelCase						,					__UpperCAmelCase					)					)

       __lowerCamelCase    				=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __lowerCamelCase    				=     self.model_tester.num_stages
       self.assertEqual(len(__UpperCAmelCase					)						,					expected_num_stages + 1					)

       # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]					)						,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,					)

    __lowerCamelCase				,__lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase    				=     ['''basic''', '''bottleneck''']
    for model_class in self.all_model_classes:
       for layer_type in layers_type:
          __lowerCamelCase    				=     layer_type
          __lowerCamelCase    				=     True
          check_hidden_states_output(__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          __lowerCamelCase    				=     True

          check_hidden_states_output(__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCamelCase    				=     TFResNetModel.from_pretrained(__UpperCAmelCase					)
       self.assertIsNotNone(__UpperCAmelCase					)





def 		a__      (		):
   __lowerCamelCase    				=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
   return image





@require_tf
@require_vision
class 			__lowerCAmelCase  (						unittest.TestCase ):




 @cached_property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return (
        AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
        if is_vision_available()
        else None
    )




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)

    __lowerCamelCase    				=     self.default_image_processor
    __lowerCamelCase    				=     prepare_img()
    __lowerCamelCase    				=     image_processor(images=__UpperCAmelCase						,					return_tensors='''tf'''					)

    # forward pass
    __lowerCamelCase    				=     model(**__UpperCAmelCase					)

    # verify the logits
    __lowerCamelCase    				=     tf.TensorShape((1, 1000)					)
    self.assertEqual(outputs.logits.shape						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tf.constant([-11.1_069, -9.7_877, -8.3_777]					)

    self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()						,					__UpperCAmelCase						,					atol=1E-4					)					)











from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a_								=       logging.get_logger(__name__)

a_								=       {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""t5"""
 lowerCAmelCase__							       =	["""past_key_values"""]
 lowerCAmelCase__							       =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




 def __init__(    self						,					__UpperCAmelCase=32128						,					__UpperCAmelCase=512						,					__UpperCAmelCase=64						,					__UpperCAmelCase=2048						,					__UpperCAmelCase=6						,					__UpperCAmelCase=None						,					__UpperCAmelCase=8						,					__UpperCAmelCase=32						,					__UpperCAmelCase=128						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=1E-6						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase="relu"						,					__UpperCAmelCase=True						,					__UpperCAmelCase=True						,					__UpperCAmelCase=0						,					__UpperCAmelCase=1						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     d_model
    __lowerCamelCase    				=     d_kv
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     num_layers
    __lowerCamelCase    				=     (
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __lowerCamelCase    				=     num_heads
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     relative_attention_max_distance
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     layer_norm_epsilon
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     feed_forward_proj
    __lowerCamelCase    				=     use_cache

    __lowerCamelCase    				=     self.feed_forward_proj.split('''-'''					)
    __lowerCamelCase    				=     act_info[-1]
    __lowerCamelCase    				=     act_info[0] == '''gated'''

    if len(__UpperCAmelCase					) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase					) > 2:
       raise ValueError(
           F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
           '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
           '''\'gated-gelu\' or \'relu\''''					)

    # for backwards compatibility
    if feed_forward_proj == "gated-gelu":
       __lowerCamelCase    				=     '''gelu_new'''

    super().__init__(
        pad_token_id=__UpperCAmelCase						,					eos_token_id=__UpperCAmelCase						,					is_encoder_decoder=__UpperCAmelCase						,					**__UpperCAmelCase						,					)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
       __lowerCamelCase    				=     '''past_encoder_sequence + sequence'''
       __lowerCamelCase    				=     {0: '''batch'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}

    if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase						,					direction='''inputs'''					)

    return common_inputs




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return 13






