from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class      __lowercase					:

      def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      int=3							,							__lowerCamelCase				:      Optional[Any]=32							,							__lowerCamelCase				:      Any=3							,							__lowerCamelCase				:      int=10							,							__lowerCamelCase				:      List[str]=[10, 20, 30, 40]							,							__lowerCamelCase				:      Optional[Any]=[1, 1, 2, 1]							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      Union[str, Any]=True							,							__lowerCamelCase				:      Dict="relu"							,							__lowerCamelCase				:      Any=3							,							__lowerCamelCase				:      Optional[int]=None							,							)							->		Optional[Any]:


         '''simple docstring'''

         lowercase							=		parent
         lowercase							=		batch_size
         lowercase							=		image_size
         lowercase							=		num_channels
         lowercase							=		embeddings_size
         lowercase							=		hidden_sizes
         lowercase							=		depths
         lowercase							=		is_training
         lowercase							=		use_labels
         lowercase							=		hidden_act
         lowercase							=		num_labels
         lowercase							=		scope
         lowercase							=		len(__lowerCamelCase   )

      def        __a		(	self				:      Any   )							->		Dict:


         '''simple docstring'''

         lowercase							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

         lowercase							=		self.get_config()
         return config, pixel_values, labels

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


         '''simple docstring'''

         return RegNetConfig(
             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							,							)

      def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Tuple   )							->		Union[str, Any]:


         '''simple docstring'''

         lowercase							=		TFRegNetModel(config=__lowerCamelCase   )
         lowercase							=		model(__lowerCamelCase							,							training=__lowerCamelCase   )
         # 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        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      int   )							->		Any:


         '''simple docstring'''

         lowercase							=		self.num_labels
         lowercase							=		TFRegNetForImageClassification(__lowerCamelCase   )
         lowercase							=		model(__lowerCamelCase							,							labels=__lowerCamelCase							,							training=__lowerCamelCase   )
         self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)   )



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


         '''simple docstring'''

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






@require_tf
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
      lowercase					 =     (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
      lowercase					 =     (
          {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
          if is_tf_available()
          else {}
      )

      lowercase					 =     False
      lowercase					 =     False
      lowercase					 =     False
      lowercase					 =     False
      lowercase					 =     False

      def        __a		(	self				:      Dict   )							->		Optional[int]:


         '''simple docstring'''

         lowercase							=		TFRegNetModelTester(self   )
         lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase							,							has_text_modality=__lowerCamelCase   )

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


         '''simple docstring'''

         return

      @unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
      def        __a		(	self				:      List[str]   )							->		List[str]:


         '''simple docstring'''

         pass

      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''   )   ) == 0							,							reason='''TF does not support backprop for grouped convolutions on CPU.'''							,							)
      @slow
      def        __a		(	self				:      List[Any]   )							->		Optional[int]:


         '''simple docstring'''

         super().test_keras_fit()

      @unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
      def        __a		(	self				:      List[Any]   )							->		Any:


         '''simple docstring'''

         pass

      def        __a		(	self				:      str   )							->		Any:


         '''simple docstring'''

         lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

      def        __a		(	self				:      Optional[int]   )							->		str:


         '''simple docstring'''

         lowercase							=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase   )

      def        __a		(	self				:      Tuple   )							->		int:


         '''simple docstring'''

         def check_hidden_states_output(__lowerCamelCase				:      str							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Tuple   ):
            lowercase							=		model_class(__lowerCamelCase   )
            lowercase							=		model(**self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )							,							training=__lowerCamelCase   )

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

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

            # RegNet'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 // 2, self.model_tester.image_size // 2]							,							)

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

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

               check_hidden_states_output(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

      def        __a		(	self				:      int   )							->		str:


         '''simple docstring'''

         lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

         def check_equivalence(__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any={}   ):
            lowercase							=		model(__lowerCamelCase							,							return_dict=__lowerCamelCase							,							**__lowerCamelCase   )
            lowercase							=		model(__lowerCamelCase							,							return_dict=__lowerCamelCase							,							**__lowerCamelCase   ).to_tuple()

            def recursive_check(__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int   ):
               if isinstance(__lowerCamelCase							,							(List, Tuple)   ):
                  for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase							,							__lowerCamelCase   ):
                     recursive_check(__lowerCamelCase							,							__lowerCamelCase   )
               elif tuple_object is None:
                  return
               else:
                  self.assertTrue(
                      all(tf.equal(__lowerCamelCase							,							__lowerCamelCase   )   )							,							msg=(
                          '''Tuple and dict output are not equal. Difference:'''
                          f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'
                      )							,							)

               recursive_check(__lowerCamelCase							,							__lowerCamelCase   )

         for model_class in self.all_model_classes:
            lowercase							=		model_class(__lowerCamelCase   )

            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
            check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
            check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
            check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							{'''output_hidden_states''': True}   )

            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
            lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
            check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							{'''output_hidden_states''': True}   )

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


         '''simple docstring'''

         lowercase							=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )



      @slow
      def        __a		(	self				:      List[str]   )							->		List[str]:


         '''simple docstring'''

         for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowercase							=		TFRegNetModel.from_pretrained(__lowerCamelCase   )
            self.assertIsNotNone(__lowerCamelCase   )






def        __UpperCAmelCase    (       )->       Tuple:



   """simple docstring"""







   lowercase							=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
   return image






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

      @cached_property
      def        __a		(	self				:      Tuple   )							->		Tuple:


         '''simple docstring'''

         return (
             AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
             if is_vision_available()
             else None
         )



      @slow
      def        __a		(	self				:      Optional[int]   )							->		List[Any]:


         '''simple docstring'''

         lowercase							=		TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

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

         # forward pass
         lowercase							=		model(**__lowerCamelCase							,							training=__lowerCamelCase   )

         # verify the logits
         lowercase							=		tf.TensorShape((1, 10_00)   )
         self.assertEqual(outputs.logits.shape							,							__lowerCamelCase   )

         lowercase							=		tf.constant([-0.4180, -1.5051, -3.4836]   )

         tf.debugging.assert_near(outputs.logits[0, :3]							,							__lowerCamelCase							,							atol=1E-4   )






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
A_      =      logging.get_logger(__name__)






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )
			if "model" in sd.keys():
						lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )['''model''']

			# pop unnecessary weights
			lowercase							=		[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(UpperCAmelCase       )

			lowercase							=		{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowercase							=		sd.pop(UpperCAmelCase       )

			lowercase							=		list(sd.keys()       )
			for key in keys:
						if ".qkv_proj." in key:
									lowercase							=		sd[key]
									# We split QKV in separate Q,K,V

									lowercase							=		key.replace('''.qkv_proj.''',		'''.q_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.k_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.v_proj.'''       )

									lowercase							=		value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowercase		,lowercase		,lowercase							=		torch.split(UpperCAmelCase,		depth // 3,		dim=0       )

									lowercase							=		q
									lowercase							=		k
									lowercase							=		v
									del sd[key]

			return sd






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None       )->       int:



			"""simple docstring"""







			lowercase							=		load_checkpoint(UpperCAmelCase       )

			if config is not None:
						lowercase							=		OPTConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		OPTConfig()

			lowercase							=		OPTModel(UpperCAmelCase       ).half().eval()
			model.load_state_dict(UpperCAmelCase       )

			# Check results
			Path(UpperCAmelCase       ).mkdir(exist_ok=UpperCAmelCase       )
			model.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--fairseq_path",
						    type=str,
						    help=(
						        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
						        " https://huggingface.co/models?other=opt_metasq"
						    ),
						)
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
						A_      =      parser.parse_args()
						convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





