






"""simple docstring"""
import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
			from transformers import (
			    FlaxBertModel,
			    FlaxCLIPVisionModel,
			    FlaxVisionTextDualEncoderModel,
			    FlaxViTModel,
			    VisionTextDualEncoderConfig,
			    VisionTextDualEncoderProcessor,
			)
			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)


if is_torch_available():
			import torch

			from transformers import VisionTextDualEncoderModel

if is_vision_available():
			from PIL import Image

def 							snake_case (  A__		):
			if isinstance(A__      ,collections.abc.Iterable		):
						return x
			return (x, x)







@require_flax
class      UpperCamelCase_			:





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[str]:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Dict:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					np.ndarray      ,      lowerCAmelCase_   :					np.ndarray      ,      lowerCAmelCase_   :					float		)		->     Dict:
				UpperCAmelCase_	:					Tuple     	=   np.abs((a - b)		).max()
				self.assertLessEqual(lowerCAmelCase_      ,      lowerCAmelCase_      ,      f"""Difference between torch and flax is {diff} (>= {tol})."""		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int=None      ,      **lowerCAmelCase_   :					str		)		->     List[Any]:
				UpperCAmelCase_	:					Tuple     	=   VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   FlaxVisionTextDualEncoderModel(lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   model(input_ids=lowerCAmelCase_      ,      pixel_values=lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)

				self.assertEqual(output["text_embeds"].shape      ,      (input_ids.shape[0], config.projection_dim)		)
				self.assertEqual(output["image_embeds"].shape      ,      (pixel_values.shape[0], config.projection_dim)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]=None      ,      **lowerCAmelCase_   :					List[str]		)		->     Dict:
				UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   self.get_vision_text_model(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   {"vision_model": vision_model, "text_model": text_model}
				UpperCAmelCase_	:					str     	=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   model(input_ids=lowerCAmelCase_      ,      pixel_values=lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)

				self.assertEqual(output["text_embeds"].shape      ,      (input_ids.shape[0], model.config.projection_dim)		)
				self.assertEqual(output["image_embeds"].shape      ,      (pixel_values.shape[0], model.config.projection_dim)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					str=None      ,      **lowerCAmelCase_   :					Optional[int]		)		->     Tuple:
				UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   self.get_vision_text_model(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   {"vision_model": vision_model, "text_model": text_model}
				UpperCAmelCase_	:					Optional[int]     	=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_		)

				UpperCAmelCase_	:					List[str]     	=   model(input_ids=lowerCAmelCase_      ,      pixel_values=lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   output[0]

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_		)

							UpperCAmelCase_	:					Dict     	=   model(input_ids=lowerCAmelCase_      ,      pixel_values=lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)
							UpperCAmelCase_	:					Union[str, Any]     	=   after_output[0]
							UpperCAmelCase_	:					Any     	=   np.amax(np.abs(out_a - out_a		)		)
							self.assertLessEqual(lowerCAmelCase_      ,      1e-3		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					str=None      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Dict:
				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   self.get_vision_text_model(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   {"vision_model": vision_model, "text_model": text_model}
				UpperCAmelCase_	:					Any     	=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   model(
				    input_ids=lowerCAmelCase_      ,      pixel_values=lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      output_attentions=lowerCAmelCase_		)

				UpperCAmelCase_	:					List[str]     	=   output.vision_model_output.attentions
				self.assertEqual(len(lowerCAmelCase_		)      ,      vision_config.num_hidden_layers		)

				# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
				UpperCAmelCase_	:					str     	=   to_atuple(vision_model.config.image_size		)
				UpperCAmelCase_	:					Optional[Any]     	=   to_atuple(vision_model.config.patch_size		)
				UpperCAmelCase_	:					Optional[Any]     	=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				UpperCAmelCase_	:					Dict     	=   num_patches + 1
				self.assertEqual(vision_attentions[0].shape[-3:]      ,      (vision_config.num_attention_heads, seq_len, seq_len)		)

				UpperCAmelCase_	:					List[str]     	=   output.text_model_output.attentions
				self.assertEqual(len(lowerCAmelCase_		)      ,      text_config.num_hidden_layers		)

				self.assertEqual(
				    text_attentions[0].shape[-3:]      ,      (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Dict		)		->     str:
				pt_model.to(lowerCAmelCase_		)
				pt_model.eval()

				# prepare inputs
				UpperCAmelCase_	:					Dict     	=   inputs_dict
				UpperCAmelCase_	:					List[str]     	=   {k: torch.tensor(v.tolist()		) for k, v in flax_inputs.items()}

				with torch.no_grad():
							UpperCAmelCase_	:					int     	=   pt_model(**lowerCAmelCase_		).to_tuple()

				UpperCAmelCase_	:					int     	=   fx_model(**lowerCAmelCase_		).to_tuple()
				self.assertEqual(len(lowerCAmelCase_		)      ,      len(lowerCAmelCase_		)      ,      "Output lengths differ between Flax and PyTorch"		)
				for fx_output, pt_output in zip(fx_outputs[:4]      ,      pt_outputs[:4]		):
							self.assert_almost_equals(lowerCAmelCase_      ,      pt_output.numpy()      ,      4e-2		)

				# PT -> Flax
				with tempfile.TemporaryDirectory() as tmpdirname:
							pt_model.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[Any]     	=   FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_      ,      from_pt=lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[Any]     	=   fx_model_loaded(**lowerCAmelCase_		).to_tuple()
				self.assertEqual(len(lowerCAmelCase_		)      ,      len(lowerCAmelCase_		)      ,      "Output lengths differ between Flax and PyTorch"		)
				for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]      ,      pt_outputs[:4]		):
							self.assert_almost_equals(lowerCAmelCase_      ,      pt_output.numpy()      ,      4e-2		)

				# Flax -> PT
				with tempfile.TemporaryDirectory() as tmpdirname:
							fx_model.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[Any]     	=   VisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_      ,      from_flax=lowerCAmelCase_		)

				pt_model_loaded.to(lowerCAmelCase_		)
				pt_model_loaded.eval()

				with torch.no_grad():
							UpperCAmelCase_	:					Tuple     	=   pt_model_loaded(**lowerCAmelCase_		).to_tuple()

				self.assertEqual(len(lowerCAmelCase_		)      ,      len(lowerCAmelCase_		)      ,      "Output lengths differ between Flax and PyTorch"		)
				for fx_output, pt_output_loaded in zip(fx_outputs[:4]      ,      pt_outputs_loaded[:4]		):
							self.assert_almost_equals(lowerCAmelCase_      ,      pt_output_loaded.numpy()      ,      4e-2		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Any		)		->     Any:
				UpperCAmelCase_	:					Union[str, Any]     	=   VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   VisionTextDualEncoderModel(lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   FlaxVisionTextDualEncoderModel(lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   fx_state

				self.check_pt_flax_equivalence(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Optional[int]		)		->     Tuple:
				UpperCAmelCase_	:					str     	=   VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   VisionTextDualEncoderModel(lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   FlaxVisionTextDualEncoderModel(lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   load_flax_weights_in_pytorch_model(lowerCAmelCase_      ,      fx_model.params		)

				self.check_pt_flax_equivalence(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Any:
				UpperCAmelCase_	:					str     	=   self.prepare_config_and_inputs()
				self.check_model_from_pretrained_configs(**lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   self.prepare_config_and_inputs()
				self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.prepare_config_and_inputs()
				self.check_save_load(**lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				UpperCAmelCase_	:					str     	=   self.prepare_config_and_inputs()
				self.check_vision_text_output_attention(**lowerCAmelCase_		)





	@is_pt_flax_cross_test
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     str:
				UpperCAmelCase_	:					int     	=   self.prepare_config_and_inputs()
				UpperCAmelCase_	:					int     	=   config_inputs_dict.pop("vision_config"		)
				UpperCAmelCase_	:					int     	=   config_inputs_dict.pop("text_config"		)

				UpperCAmelCase_	:					Optional[Any]     	=   config_inputs_dict

				self.check_equivalence_pt_to_flax(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)
				self.check_equivalence_flax_to_pt(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[Any]:
				UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   self.get_pretrained_model_and_inputs()

				UpperCAmelCase_	:					List[Any]     	=   model_a(**lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   outputs[0]

				with tempfile.TemporaryDirectory() as tmp_dirname:
							model_a.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_		)

							UpperCAmelCase_	:					Any     	=   model_a(**lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   after_outputs[0]
							UpperCAmelCase_	:					int     	=   np.amax(np.abs(out_a - out_a		)		)
							self.assertLessEqual(lowerCAmelCase_      ,      1e-5		)







@require_flax
class      UpperCamelCase_			(__A							,		unittest.TestCase	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    "hf-internal-testing/tiny-random-vit"      ,      "hf-internal-testing/tiny-bert"      ,      vision_from_pt=lowerCAmelCase_      ,      text_from_pt=lowerCAmelCase_      ,      )
				UpperCAmelCase_	:					List[Any]     	=   13
				UpperCAmelCase_	:					Optional[Any]     	=   floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ]		)
				UpperCAmelCase_	:					Tuple     	=   ids_tensor([batch_size, 4]      ,      model.config.text_config.vocab_size		)
				UpperCAmelCase_	:					Optional[Any]     	=   random_attention_mask([batch_size, 4]		)
				UpperCAmelCase_	:					str     	=   {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

				return model, inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[int]		)		->     List[str]:
				UpperCAmelCase_	:					List[str]     	=   FlaxViTModel(lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   FlaxBertModel(lowerCAmelCase_		)
				return vision_model, text_model





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   FlaxViTModelTester(self		)
				UpperCAmelCase_	:					Optional[int]     	=   FlaxBertModelTester(self		)
				UpperCAmelCase_	:					Any     	=   vit_model_tester.prepare_config_and_inputs()
				UpperCAmelCase_	:					Optional[Any]     	=   bert_model_tester.prepare_config_and_inputs()

				UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   vision_config_and_inputs

				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}







@require_torch
class      UpperCamelCase_			(__A							,		unittest.TestCase	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    "hf-internal-testing/tiny-random-clip"      ,      "hf-internal-testing/tiny-bert"      ,      vision_from_pt=lowerCAmelCase_      ,      text_from_pt=lowerCAmelCase_      ,      )
				UpperCAmelCase_	:					Any     	=   13
				UpperCAmelCase_	:					Union[str, Any]     	=   floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ]		)
				UpperCAmelCase_	:					Optional[int]     	=   ids_tensor([batch_size, 4]      ,      model.config.text_config.vocab_size		)
				UpperCAmelCase_	:					int     	=   random_attention_mask([batch_size, 4]		)
				UpperCAmelCase_	:					int     	=   {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

				return model, inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					str		)		->     str:
				UpperCAmelCase_	:					Tuple     	=   FlaxCLIPVisionModel(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   FlaxBertModel(lowerCAmelCase_		)
				return vision_model, text_model





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   FlaxCLIPVisionModelTester(self		)
				UpperCAmelCase_	:					Union[str, Any]     	=   FlaxBertModelTester(self		)
				UpperCAmelCase_	:					List[str]     	=   clip_model_tester.prepare_config_and_inputs()
				UpperCAmelCase_	:					Union[str, Any]     	=   bert_model_tester.prepare_config_and_inputs()

				UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   vision_config_and_inputs

				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}







@require_flax
@require_vision
class      UpperCamelCase_			(unittest.TestCase	):





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Any     	=   FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"      ,      logit_scale_init_value=1.0		)
				UpperCAmelCase_	:					Tuple     	=   VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"		)

				UpperCAmelCase_	:					Optional[Any]     	=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				UpperCAmelCase_	:					int     	=   processor(
				    text=["una foto di un gatto", "una foto di un cane"]      ,      images=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      return_tensors="np"		)

				UpperCAmelCase_	:					Optional[int]     	=   model(**lowerCAmelCase_		)

				# verify the logits
				self.assertEqual(outputs.logits_per_image.shape      ,      (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
				self.assertEqual(
				    outputs.logits_per_text.shape      ,      (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])      ,      )

				UpperCAmelCase_	:					List[str]     	=   np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]		)

				self.assertTrue(np.allclose(outputs.logits_per_image      ,      lowerCAmelCase_      ,      atol=1e-3		)		)










'''simple docstring'''




import argparse

import torch

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


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)

lowercase__       : str         =       [
    ['''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 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )

     if k.startswith('encoder'     ):
          __A		:					Any						=			k.replace('.attn'					,				'.self_attn'     )
          __A		:					Any						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'final_layer_norm'     )
     elif k.startswith('decoder'     ):
          __A		:					Tuple						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'encoder_attn_layer_norm'     )
          __A		:					int						=			k.replace('norm3'					,				'final_layer_norm'     )
     return k






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Dict:
     __A		:					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:
          __A		:					Tuple						=			sd.pop(__snake_case     )
          __A		:					Union[str, Any]						=			k.replace('layernorm_embedding'					,				'layer_norm'     )
          assert new_k not in sd
          __A		:					str						=			v


lowercase__       : Tuple         =       ['''START''']






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					List[Any]     )	->	int:
     __A		:					List[str]						=			torch.load(__snake_case					,				map_location='cpu'     )
     __A		:					Tuple						=			model['model']
     __A		:					str						=			BlenderbotConfig.from_json_file(__snake_case     )
     __A		:					int						=			BlenderbotForConditionalGeneration(__snake_case     )
     __A		:					List[Any]						=			m.model.state_dict().keys()
     __A		:					Optional[int]						=			[]
     __A		:					Optional[int]						=			{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
  lowercase__       : 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'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)