"""simple docstring"""







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_a					:					Optional[int]=			logging.get_logger(__name__)

_a					:					Tuple=			[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]  )							->      List[Any]:

	'''simple docstring'''





	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		__snake_case      :							Tuple						=			k.replace(UpperCAmelCase_      ,							UpperCAmelCase_  )

	if k.startswith('encoder'  ):
		__snake_case      :							str						=			k.replace('.attn'      ,							'.self_attn'  )
		__snake_case      :							Optional[int]						=			k.replace('norm1'      ,							'self_attn_layer_norm'  )
		__snake_case      :							Optional[Any]						=			k.replace('norm2'      ,							'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		__snake_case      :							List[str]						=			k.replace('norm1'      ,							'self_attn_layer_norm'  )
		__snake_case      :							Tuple						=			k.replace('norm2'      ,							'encoder_attn_layer_norm'  )
		__snake_case      :							List[str]						=			k.replace('norm3'      ,							'final_layer_norm'  )
	return k
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any  )							->      str:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		__snake_case      :							int						=			sd.pop(UpperCAmelCase_  )
		__snake_case      :							Optional[int]						=			k.replace('layernorm_embedding'      ,							'layer_norm'  )
		assert new_k not in sd
		__snake_case      :							int						=			v


_a					:					Union[str, Any]=			["START"]
@torch.no_grad()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Tuple  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Optional[Any]						=			torch.load(UpperCAmelCase_      ,							map_location='cpu'  )
	__snake_case      :							List[Any]						=			model['model']
	__snake_case      :							str						=			BlenderbotConfig.from_json_file(UpperCAmelCase_  )
	__snake_case      :							Any						=			BlenderbotForConditionalGeneration(UpperCAmelCase_  )
	__snake_case      :							Optional[Any]						=			m.model.state_dict().keys()
	__snake_case      :							List[Any]						=			[]
	__snake_case      :							List[Any]						=			{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		__snake_case      :							List[Any]						=			rename_state_dict_key(UpperCAmelCase_  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			__snake_case      :							List[str]						=			v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCAmelCase_  )
	m.model.load_state_dict(UpperCAmelCase_      ,							strict=UpperCAmelCase_  )
	m.half()
	m.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
	_a					:					Dict=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
	parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
	parser.add_argument(
	    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
	)
	_a					:					Optional[int]=			parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






"""simple docstring"""







import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor

class 		UpperCamelCase      (   lowercase						):



				def  _lowercase					(self    :				Any)   ->     List[Any]:
					__snake_case      :							Any						=			self.config_class(**self.inputs_dict)
					self.parent.assertTrue(hasattr(_A     ,  'hidden_sizes'))
					self.parent.assertTrue(hasattr(_A     ,  'num_attention_heads'))
					self.parent.assertTrue(hasattr(_A     ,  'num_encoder_blocks'))







class 		UpperCamelCase      :



				def __init__(self    :				Optional[int]     ,  _A    :				Any     ,  _A    :				str=13     ,  _A    :				List[str]=64     ,  _A    :				List[Any]=3     ,  _A    :				Any=4     ,  _A    :				List[str]=[2, 2, 2, 2]     ,  _A    :				Tuple=[8, 4, 2, 1]     ,  _A    :				List[str]=[16, 32, 64, 1_28]     ,  _A    :				int=[1, 4, 8, 16]     ,  _A    :				List[str]=[1, 2, 4, 8]     ,  _A    :				Dict=True     ,  _A    :				Any=True     ,  _A    :				List[str]="gelu"     ,  _A    :				Optional[int]=0.1     ,  _A    :				Union[str, Any]=0.1     ,  _A    :				List[Any]=0.02     ,  _A    :				str=3     ,  _A    :				int=None     ,  )   ->     List[Any]:
					__snake_case      :							int						=			parent
					__snake_case      :							List[Any]						=			batch_size
					__snake_case      :							Optional[int]						=			image_size
					__snake_case      :							List[str]						=			num_channels
					__snake_case      :							Any						=			num_encoder_blocks
					__snake_case      :							Dict						=			sr_ratios
					__snake_case      :							Any						=			depths
					__snake_case      :							Tuple						=			hidden_sizes
					__snake_case      :							Tuple						=			downsampling_rates
					__snake_case      :							Union[str, Any]						=			num_attention_heads
					__snake_case      :							Optional[int]						=			is_training
					__snake_case      :							Any						=			use_labels
					__snake_case      :							List[Any]						=			hidden_act
					__snake_case      :							Union[str, Any]						=			hidden_dropout_prob
					__snake_case      :							Tuple						=			attention_probs_dropout_prob
					__snake_case      :							List[Any]						=			initializer_range
					__snake_case      :							Union[str, Any]						=			num_labels
					__snake_case      :							Dict						=			scope



				def  _lowercase					(self    :				List[Any])   ->     Tuple:
					__snake_case      :							Optional[Any]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

					__snake_case      :							str						=			None
					if self.use_labels:
						__snake_case      :							Optional[Any]						=			ids_tensor([self.batch_size, self.image_size, self.image_size]     ,  self.num_labels)

					__snake_case      :							Dict						=			self.get_config()
					return config, pixel_values, labels



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					return SegformerConfig(
					    image_size=self.image_size     ,  num_channels=self.num_channels     ,  num_encoder_blocks=self.num_encoder_blocks     ,  depths=self.depths     ,  hidden_sizes=self.hidden_sizes     ,  num_attention_heads=self.num_attention_heads     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  initializer_range=self.initializer_range     ,  )



				def  _lowercase					(self    :				List[str]     ,  _A    :				Union[str, Any]     ,  _A    :				List[Any]     ,  _A    :				Union[str, Any])   ->     int:
					__snake_case      :							Union[str, Any]						=			SegformerModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case      :							str						=			model(_A)
					__snake_case      :							List[Any]						=			self.image_size // (self.downsampling_rates[-1] * 2)
					self.parent.assertEqual(
					    result.last_hidden_state.shape     ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))



				def  _lowercase					(self    :				Tuple     ,  _A    :				Dict     ,  _A    :				Any     ,  _A    :				int)   ->     str:
					__snake_case      :							Any						=			self.num_labels
					__snake_case      :							List[str]						=			SegformerForSemanticSegmentation(_A)
					model.to(_A)
					model.eval()
					__snake_case      :							Dict						=			model(_A)
					self.parent.assertEqual(
					    result.logits.shape     ,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
					__snake_case      :							Dict						=			model(_A     ,  labels=_A)
					self.parent.assertEqual(
					    result.logits.shape     ,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
					self.parent.assertGreater(result.loss     ,  0.0)



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Dict     ,  _A    :				Optional[int]     ,  _A    :				str)   ->     List[Any]:
					__snake_case      :							List[Any]						=			1
					__snake_case      :							str						=			SegformerForSemanticSegmentation(config=_A)
					model.to(_A)
					model.eval()
					__snake_case      :							Any						=			torch.randint(0     ,  1     ,  (self.batch_size, self.image_size, self.image_size)).to(_A)
					__snake_case      :							Any						=			model(_A     ,  labels=_A)
					self.parent.assertGreater(result.loss     ,  0.0)






				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			self.prepare_config_and_inputs()
					__snake_case		,    __snake_case		,    __snake_case      :							str						=			config_and_inputs
					__snake_case      :							List[str]						=			{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class 		UpperCamelCase      (   lowercase				,     lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				List[str]   =		(
				    (
				        SegformerModel,
				        SegformerForSemanticSegmentation,
				        SegformerForImageClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase    :				Dict   =		(
				    {
				        """feature-extraction""": SegformerModel,
				        """image-classification""": SegformerForImageClassification,
				        """image-segmentation""": SegformerForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase    :				Optional[int]   =		True
				UpperCAmelCase    :				Optional[int]   =		False
				UpperCAmelCase    :				List[Any]   =		False
				UpperCAmelCase    :				List[Any]   =		False



				def  _lowercase					(self    :				str)   ->     Union[str, Any]:
					__snake_case      :							Optional[int]						=			SegformerModelTester(self)
					__snake_case      :							Any						=			SegformerConfigTester(self     ,  config_class=_A)



				def  _lowercase					(self    :				List[str])   ->     Optional[int]:
					self.config_tester.run_common_tests()



				def  _lowercase					(self    :				List[Any])   ->     List[str]:
					__snake_case      :							int						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A)



				def  _lowercase					(self    :				Optional[int])   ->     str:
					__snake_case      :							List[Any]						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_binary_image_segmentation(*_A)



				def  _lowercase					(self    :				int)   ->     Union[str, Any]:
					__snake_case      :							Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_segmentation(*_A)



				@unittest.skip('SegFormer does not use inputs_embeds')
				def  _lowercase					(self    :				Union[str, Any])   ->     str:
					pass



				@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
				def  _lowercase					(self    :				int)   ->     str:
					pass



				def  _lowercase					(self    :				List[str])   ->     Any:
					__snake_case		,    __snake_case      :							Dict						=			self.model_tester.prepare_config_and_inputs_for_common()

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

						__snake_case      :							Optional[int]						=			['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  _A)



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					__snake_case		,    __snake_case      :							Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case      :							Optional[int]						=			True

					for model_class in self.all_model_classes:
						__snake_case      :							Union[str, Any]						=			True
						__snake_case      :							List[str]						=			False
						__snake_case      :							Tuple						=			True
						__snake_case      :							int						=			model_class(_A)
						model.to(_A)
						model.eval()
						with torch.no_grad():
							__snake_case      :							int						=			model(**self._prepare_for_class(_A     ,  _A))
						__snake_case      :							Union[str, Any]						=			outputs.attentions

						__snake_case      :							int						=			sum(self.model_tester.depths)
						self.assertEqual(len(_A)     ,  _A)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						__snake_case      :							int						=			True
						__snake_case      :							Union[str, Any]						=			model_class(_A)
						model.to(_A)
						model.eval()
						with torch.no_grad():
							__snake_case      :							List[str]						=			model(**self._prepare_for_class(_A     ,  _A))
						__snake_case      :							Optional[int]						=			outputs.attentions

						self.assertEqual(len(_A)     ,  _A)

						# verify the first attentions (first block, first layer)
						__snake_case      :							Optional[int]						=			(self.model_tester.image_size // 4) ** 2
						__snake_case      :							Tuple						=			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(attentions[0].shape[-3:])     ,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,  )

						# verify the last attentions (last block, last layer)
						__snake_case      :							int						=			(self.model_tester.image_size // 32) ** 2
						__snake_case      :							Any						=			(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
						self.assertListEqual(
						    list(attentions[-1].shape[-3:])     ,  [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]     ,  )
						__snake_case      :							int						=			len(_A)

						# Check attention is always last and order is fine
						__snake_case      :							Any						=			True
						__snake_case      :							Tuple						=			True
						__snake_case      :							Optional[Any]						=			model_class(_A)
						model.to(_A)
						model.eval()
						with torch.no_grad():
							__snake_case      :							Any						=			model(**self._prepare_for_class(_A     ,  _A))

						self.assertEqual(out_len + 1     ,  len(_A))

						__snake_case      :							List[Any]						=			outputs.attentions

						self.assertEqual(len(_A)     ,  _A)
						# verify the first attentions (first block, first layer)
						__snake_case      :							Any						=			(self.model_tester.image_size // 4) ** 2
						__snake_case      :							List[Any]						=			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:])     ,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,  )



				def  _lowercase					(self    :				str)   ->     List[str]:
					def check_hidden_states_output(_A    :				Union[str, Any]     ,  _A    :				List[str]     ,  _A    :				Tuple):
						__snake_case      :							Tuple						=			model_class(_A)
						model.to(_A)
						model.eval()

						with torch.no_grad():
							__snake_case      :							Optional[int]						=			model(**self._prepare_for_class(_A     ,  _A))

						__snake_case      :							List[str]						=			outputs.hidden_states

						__snake_case      :							Tuple						=			self.model_tester.num_encoder_blocks
						self.assertEqual(len(_A)     ,  _A)

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

					__snake_case		,    __snake_case      :							Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						__snake_case      :							List[str]						=			True
						check_hidden_states_output(_A     ,  _A     ,  _A)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__snake_case      :							Any						=			True

						check_hidden_states_output(_A     ,  _A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     int:
					if not self.model_tester.is_training:
						return

					__snake_case		,    __snake_case      :							Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case      :							int						=			True

					for model_class in self.all_model_classes:
						if model_class in get_values(_A):
							continue

						__snake_case      :							Tuple						=			model_class(_A)
						model.to(_A)
						model.train()
						__snake_case      :							str						=			self._prepare_for_class(_A     ,  _A     ,  return_labels=_A)
						__snake_case      :							Dict						=			model(**_A).loss
						loss.backward()



				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
				def  _lowercase					(self    :				Tuple)   ->     Dict:
					pass






				@slow
				def  _lowercase					(self    :				Any)   ->     List[str]:
					for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							List[Any]						=			SegformerModel.from_pretrained(_A)
						self.assertIsNotNone(_A)







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

	'''simple docstring'''





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

@require_torch
class 		UpperCamelCase      (   unittest.TestCase						):



				@slow
				def  _lowercase					(self    :				Union[str, Any])   ->     Any:
					# only resize + normalize
					__snake_case      :							List[str]						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Tuple						=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
					    _A)

					__snake_case      :							Optional[Any]						=			prepare_img()
					__snake_case      :							Tuple						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							List[str]						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Optional[int]						=			torch.Size((1, model.config.num_labels, 1_28, 1_28))
					self.assertEqual(outputs.logits.shape     ,  _A)

					__snake_case      :							Tuple						=			torch.tensor(
					    [
					        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
					        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
					        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
					    ]).to(_A)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,  _A     ,  atol=1E-4))



				@slow
				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# only resize + normalize
					__snake_case      :							int						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Tuple						=			SegformerForSemanticSegmentation.from_pretrained(
					    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(_A)

					__snake_case      :							str						=			prepare_img()
					__snake_case      :							Union[str, Any]						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							str						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Any						=			torch.Size((1, model.config.num_labels, 1_28, 1_28))
					self.assertEqual(outputs.logits.shape     ,  _A)

					__snake_case      :							List[Any]						=			torch.tensor(
					    [
					        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
					        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
					        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
					    ]).to(_A)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,  _A     ,  atol=1E-1))






				@slow
				def  _lowercase					(self    :				Optional[int])   ->     Union[str, Any]:
					# only resize + normalize
					__snake_case      :							List[Any]						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Optional[Any]						=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
					    _A)

					__snake_case      :							Optional[Any]						=			prepare_img()
					__snake_case      :							Optional[Any]						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							Union[str, Any]						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Optional[Any]						=			outputs.logits.detach().cpu()

					__snake_case      :							Any						=			image_processor.post_process_semantic_segmentation(outputs=_A     ,  target_sizes=[(5_00, 3_00)])
					__snake_case      :							Any						=			torch.Size((5_00, 3_00))
					self.assertEqual(segmentation[0].shape     ,  _A)

					__snake_case      :							Any						=			image_processor.post_process_semantic_segmentation(outputs=_A)
					__snake_case      :							str						=			torch.Size((1_28, 1_28))
					self.assertEqual(segmentation[0].shape     ,  _A)





