



from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
    from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor



class 				lowerCAmelCase_		(  a__							):
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Optional[Any]							=	self.config_class(**self.inputs_dict       )
          self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'embed_dim'       )       )
          self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'num_heads'       )       )






class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=[16, 48, 96],			SCREAMING_SNAKE_CASE_=[1, 3, 6],			SCREAMING_SNAKE_CASE_=[1, 2, 10],			SCREAMING_SNAKE_CASE_=[7, 3, 3],			SCREAMING_SNAKE_CASE_=[4, 2, 2],			SCREAMING_SNAKE_CASE_=[2, 1, 1],			SCREAMING_SNAKE_CASE_=[2, 2, 2],			SCREAMING_SNAKE_CASE_=[False, False, True],			SCREAMING_SNAKE_CASE_=[0.0, 0.0, 0.0],			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=2,			)     ->		Optional[Any]:
          UpperCamelCase    :  List[Any]							=	parent
          UpperCamelCase    :  str							=	batch_size
          UpperCamelCase    :  int							=	image_size
          UpperCamelCase    :  Any							=	patch_sizes
          UpperCamelCase    :  Optional[int]							=	patch_stride
          UpperCamelCase    :  int							=	patch_padding
          UpperCamelCase    :  int							=	is_training
          UpperCamelCase    :  str							=	use_labels
          UpperCamelCase    :  Union[str, Any]							=	num_labels
          UpperCamelCase    :  Optional[int]							=	num_channels
          UpperCamelCase    :  List[Any]							=	embed_dim
          UpperCamelCase    :  Optional[int]							=	num_heads
          UpperCamelCase    :  List[str]							=	stride_kv
          UpperCamelCase    :  Optional[int]							=	depth
          UpperCamelCase    :  Tuple							=	cls_token
          UpperCamelCase    :  Optional[int]							=	attention_drop_rate
          UpperCamelCase    :  Any							=	initializer_range
          UpperCamelCase    :  Union[str, Any]							=	layer_norm_eps
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Optional[Any]							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          UpperCamelCase    :  Optional[int]							=	None
          if self.use_labels:
             # create a random int32 tensor of given shape
             UpperCamelCase    :  List[Any]							=	ids_tensor([self.batch_size],			self.num_labels       )

          UpperCamelCase    :  str							=	self.get_config()
          return config, pixel_values, labels
       def 					snake_case_       (			self       )     ->		Dict:
          return CvtConfig(
              image_size=self.image_size,			num_labels=self.num_labels,			num_channels=self.num_channels,			embed_dim=self.embed_dim,			num_heads=self.num_heads,			patch_sizes=self.patch_sizes,			patch_padding=self.patch_padding,			patch_stride=self.patch_stride,			stride_kv=self.stride_kv,			depth=self.depth,			cls_token=self.cls_token,			attention_drop_rate=self.attention_drop_rate,			initializer_range=self.initializer_range,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  Union[str, Any]							=	TFCvtModel(config=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model(SCREAMING_SNAKE_CASE_,			training=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  int							=	(self.image_size, self.image_size)
          UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	image_size[0], image_size[1]
          for i in range(len(self.depth       )       ):
             UpperCamelCase    :  List[str]							=	floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
             UpperCamelCase    :  str							=	floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
          self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.embed_dim[-1], height, width)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  str							=	self.num_labels
          UpperCamelCase    :  Any							=	TFCvtForImageClassification(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	model(SCREAMING_SNAKE_CASE_,			labels=SCREAMING_SNAKE_CASE_,			training=SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)       )






       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Any							=	self.prepare_config_and_inputs()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  int							=	config_and_inputs
          UpperCamelCase    :  Dict							=	{'pixel_values': pixel_values}
          return config, inputs_dict






@require_tf
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
       UpperCAmelCase__				:					List[str]        =  (
           {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
           if is_tf_available()
           else {}
       )
       UpperCAmelCase__				:					Optional[Any]        =  False
       UpperCAmelCase__				:					Optional[int]        =  False
       UpperCAmelCase__				:					List[Any]        =  False
       UpperCAmelCase__				:					int        =  False
       UpperCAmelCase__				:					List[str]        =  False
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Tuple							=	TFCvtModelTester(self       )
          UpperCamelCase    :  List[str]							=	TFCvtConfigTester(self,			config_class=SCREAMING_SNAKE_CASE_,			has_text_modality=SCREAMING_SNAKE_CASE_,			hidden_size=37       )
       def 					snake_case_       (			self       )     ->		List[str]:
          self.config_tester.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()
       @unittest.skip(reason='Cvt does not output attentions'       )
       def 					snake_case_       (			self       )     ->		Tuple:
          pass
       @unittest.skip(reason='Cvt does not use inputs_embeds'       )
       def 					snake_case_       (			self       )     ->		int:
          pass
       @unittest.skip(reason='Cvt does not support input and output embeddings'       )
       def 					snake_case_       (			self       )     ->		List[str]:
          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.',			)
       def 					snake_case_       (			self       )     ->		List[str]:
          super().test_dataset_conversion()
       @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 					snake_case_       (			self       )     ->		Any:
          super().test_keras_fit()
       @unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'       )
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Dict							=	tf.keras.mixed_precision.Policy('mixed_float16'       )
          tf.keras.mixed_precision.set_global_policy(SCREAMING_SNAKE_CASE_       )
          super().test_keras_fit()
          tf.keras.mixed_precision.set_global_policy('float32'       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase	,     UpperCamelCase    :  Any							=	self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase    :  Tuple							=	['pixel_values']
             self.assertListEqual(arg_names[:1],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Union[str, Any]							=	model_class(SCREAMING_SNAKE_CASE_       )

             UpperCamelCase    :  Tuple							=	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )
             UpperCamelCase    :  Optional[Any]							=	outputs.hidden_states

             UpperCamelCase    :  List[Any]							=	len(self.model_tester.depth       )
             self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_       )

             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-3:]       ),			[
                     self.model_tester.embed_dim[0],
                     self.model_tester.image_size // 4,
                     self.model_tester.image_size // 4,
                 ],			)

          UpperCamelCase	,     UpperCamelCase    :  Any							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Tuple							=	True
             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase    :  Union[str, Any]							=	True

             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Dict							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )






       @slow
       def 					snake_case_       (			self       )     ->		Optional[int]:
          for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  Dict							=	TFCvtModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






def 		UpperCamelCase	(   )	->     Union[str, Any]:
   UpperCamelCase    :  List[str]							=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



@require_tf
@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Any:
          return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )






       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Optional[int]							=	TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

          UpperCamelCase    :  str							=	self.default_image_processor
          UpperCamelCase    :  Tuple							=	prepare_img()
          UpperCamelCase    :  List[Any]							=	image_processor(images=SCREAMING_SNAKE_CASE_,			return_tensors='tf'       )

          # forward pass
          UpperCamelCase    :  Tuple							=	model(**SCREAMING_SNAKE_CASE_       )

          # verify the logits
          UpperCamelCase    :  Any							=	tf.TensorShape((1, 1000)       )
          self.assertEqual(outputs.logits.shape,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[str]							=	tf.constant([0.92_85, 0.90_15, -0.31_50]       )
          self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(),			SCREAMING_SNAKE_CASE_,			atol=1e-4       )       )








import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__UpperCAmelCase          =       ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
__UpperCAmelCase          =       {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
    raise Exception('''requires fairseq >= 0.9.0''')


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

__UpperCAmelCase          =       ''' Hello world! cécé herlolip'''

__UpperCAmelCase          =       [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def 		UpperCamelCase	(   snake_case__       :    Union[str, Any]	)	->     List[str]:
   UpperCamelCase    :  int							=	[
       'encoder.version',
       'decoder.version',
       'model.encoder.version',
       'model.decoder.version',
       '_float_tensor',
   ]
   for k in ignore_keys:
      state_dict.pop(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Any:
   UpperCamelCase    :  Dict							=	dct.pop(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	val
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     Tuple:
   UpperCamelCase    :  int							=	torch.load(snake_case__       ,     map_location='cpu'	)
   UpperCamelCase    :  Dict							=	torch.hub.load('pytorch/fairseq'       ,     'bart.large.cnn'	).eval()
   hub_interface.model.load_state_dict(sd['model']	)
   return hub_interface
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     Dict:
   UpperCamelCase	,     UpperCamelCase    :  str							=	emb.weight.shape
   UpperCamelCase    :  Optional[int]							=	nn.Linear(snake_case__       ,     snake_case__       ,     bias=snake_case__	)
   UpperCamelCase    :  List[str]							=	emb.weight.data
   return lin_layer
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    str=None	)	->     Optional[Any]:
   if not os.path.exists(snake_case__	):
      UpperCamelCase    :  List[str]							=	torch.hub.load('pytorch/fairseq'       ,     snake_case__	).eval()
   else:
      UpperCamelCase    :  int							=	load_xsum_checkpoint(snake_case__	)

   bart.model.upgrade_state_dict(bart.model.state_dict()	)
   if hf_checkpoint_name is None:
      UpperCamelCase    :  Tuple							=	checkpoint_path.replace('.'       ,     '-'	)
   UpperCamelCase    :  Optional[int]							=	BartConfig.from_pretrained(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	bart.encode(snake_case__	).unsqueeze(0	)
   UpperCamelCase    :  Any							=	BartTokenizer.from_pretrained(snake_case__	).encode(snake_case__       ,     return_tensors='pt'	).unsqueeze(0	)
   if not torch.eq(snake_case__       ,     snake_case__	).all():
      raise ValueError(
          F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""	)

   if checkpoint_path == "bart.large.mnli":
      UpperCamelCase    :  Union[str, Any]							=	bart.state_dict()
      remove_ignore_keys_(snake_case__	)
      UpperCamelCase    :  int							=	state_dict['model.decoder.embed_tokens.weight']
      for src, dest in mnli_rename_keys:
         rename_key(snake_case__       ,     snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Any							=	BartForSequenceClassification(snake_case__	).eval()
      model.load_state_dict(snake_case__	)
      UpperCamelCase    :  Any							=	bart.predict('mnli'       ,     snake_case__       ,     return_logits=snake_case__	)
      UpperCamelCase    :  Tuple							=	model(snake_case__	)[0]  # logits
   else:  # no classification heads to worry about
      UpperCamelCase    :  List[str]							=	bart.model.state_dict()
      remove_ignore_keys_(snake_case__	)
      UpperCamelCase    :  List[str]							=	state_dict['decoder.embed_tokens.weight']
      UpperCamelCase    :  Union[str, Any]							=	bart.extract_features(snake_case__	)
      if hf_checkpoint_name == "facebook/bart-large":
         UpperCamelCase    :  List[str]							=	BartModel(snake_case__	).eval()
         model.load_state_dict(snake_case__	)
         UpperCamelCase    :  Optional[int]							=	model(snake_case__	).model[0]
      else:
         UpperCamelCase    :  Union[str, Any]							=	BartForConditionalGeneration(snake_case__	).eval()  # an existing summarization ckpt
         model.model.load_state_dict(snake_case__	)
         if hasattr(snake_case__       ,     'lm_head'	):
            UpperCamelCase    :  Optional[int]							=	make_linear_from_emb(model.model.shared	)
         UpperCamelCase    :  Dict							=	model.model(snake_case__	)[0]

    # Check results
   if fairseq_output.shape != new_model_outputs.shape:
      raise ValueError(
          F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""	)
   if (fairseq_output != new_model_outputs).any().item():
      raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'	)
   Path(snake_case__	).mkdir(exist_ok=snake_case__	)
   model.save_pretrained(snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    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='''Which huggingface architecture to use: bart-large-xsum'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



