
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor






class     a_       (			unittest.TestCase							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=7				,       _lowerCamelCase=3				,       _lowerCamelCase=18				,       _lowerCamelCase=30				,       _lowerCamelCase=400				,       _lowerCamelCase=True				,       _lowerCamelCase=None				,       _lowerCamelCase=2				,       _lowerCamelCase=True				,       _lowerCamelCase=[0.5, 0.5, 0.5]				,       _lowerCamelCase=[0.5, 0.5, 0.5]				,       )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Any				      = size if size is not None else {'''shortest_edge''': 30}
           SCREAMING_SNAKE_CASE				:				int				      = parent
           SCREAMING_SNAKE_CASE				:				int				      = batch_size
           SCREAMING_SNAKE_CASE				:				str				      = num_channels
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = image_size
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = min_resolution
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = max_resolution
           SCREAMING_SNAKE_CASE				:				str				      = do_resize
           SCREAMING_SNAKE_CASE				:				Dict				      = size
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = size_divisor
           SCREAMING_SNAKE_CASE				:				int				      = do_normalize
           SCREAMING_SNAKE_CASE				:				int				      = image_mean
           SCREAMING_SNAKE_CASE				:				str				      = image_std






       def 	__lowerCAmelCase     (		self   )					->Any:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=False   )					->Optional[Any]:
           if not batched:
               SCREAMING_SNAKE_CASE				:				int				      = self.size['''shortest_edge''']
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = image_inputs[0]
               if isinstance(_lowerCamelCase				,       Image.Image   ):
                   SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image.size
               else:
                   SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				str				      = image.shape[1], image.shape[2]
               SCREAMING_SNAKE_CASE				:				Dict				      = size / min(_lowerCamelCase				,       _lowerCamelCase   )
               if h < w:
                   SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[Any]				      = size, scale * w
               else:
                   SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = scale * h, size

               SCREAMING_SNAKE_CASE				:				Tuple				      = int((1333 / 800) * size   )
               if max(_lowerCamelCase				,       _lowerCamelCase   ) > max_size:
                   SCREAMING_SNAKE_CASE				:				Optional[Any]				      = max_size / max(_lowerCamelCase				,       _lowerCamelCase   )
                   SCREAMING_SNAKE_CASE				:				str				      = newh * scale
                   SCREAMING_SNAKE_CASE				:				str				      = neww * scale

               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				int				      = int(newh + 0.5   ), int(neww + 0.5   )
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

           else:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = []
               for image in image_inputs:
                   SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				str				      = self.get_expected_values([image]   )
                   expected_values.append((expected_height, expected_width)   )
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = max(_lowerCamelCase				,       key=lambda _lowerCamelCase   : item[0]   )[0]
               SCREAMING_SNAKE_CASE				:				Any				      = max(_lowerCamelCase				,       key=lambda _lowerCamelCase   : item[1]   )[1]

           return expected_height, expected_width






@require_torch
@require_vision
class     a_       (			a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  ViltImageProcessor if is_vision_available() else None






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				int				      = ViltImageProcessingTester(self   )






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				int				      = self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(_lowerCamelCase				,       '''image_mean'''   )   )
           self.assertTrue(hasattr(_lowerCamelCase				,       '''image_std'''   )   )
           self.assertTrue(hasattr(_lowerCamelCase				,       '''do_normalize'''   )   )
           self.assertTrue(hasattr(_lowerCamelCase				,       '''do_resize'''   )   )
           self.assertTrue(hasattr(_lowerCamelCase				,       '''size'''   )   )
           self.assertTrue(hasattr(_lowerCamelCase				,       '''size_divisor'''   )   )






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size				,       {'''shortest_edge''': 30}   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.image_processing_class.from_dict(self.image_processor_dict				,       size=42   )
           self.assertEqual(image_processor.size				,       {'''shortest_edge''': 42}   )






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






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           # Initialize image_processing
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.image_processing_class(**self.image_processor_dict   )
           # create random PIL images
           SCREAMING_SNAKE_CASE				:				List[Any]				      = prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_lowerCamelCase   )
           for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase				,       Image.Image   )

           # Test not batched input
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image_processing(image_inputs[0]				,       return_tensors='''pt'''   ).pixel_values

           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				str				      = self.image_processor_tester.get_expected_values(_lowerCamelCase   )
           self.assertEqual(
               encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

           # Test batched
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = image_processing(_lowerCamelCase				,       return_tensors='''pt'''   ).pixel_values

           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Dict				      = self.image_processor_tester.get_expected_values(_lowerCamelCase				,       batched=_lowerCamelCase   )
           self.assertEqual(
               encoded_images.shape				,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,       )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           # Initialize image_processing
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_lowerCamelCase				,       numpify=_lowerCamelCase   )
           for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase				,       np.ndarray   )

           # Test not batched input
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = image_processing(image_inputs[0]				,       return_tensors='''pt'''   ).pixel_values

           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[Any]				      = self.image_processor_tester.get_expected_values(_lowerCamelCase   )
           self.assertEqual(
               encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

           # Test batched
           SCREAMING_SNAKE_CASE				:				Tuple				      = image_processing(_lowerCamelCase				,       return_tensors='''pt'''   ).pixel_values

           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.image_processor_tester.get_expected_values(_lowerCamelCase				,       batched=_lowerCamelCase   )
           self.assertEqual(
               encoded_images.shape				,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,       )






       def 	__lowerCAmelCase     (		self   )					->str:
           # Initialize image_processing
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_lowerCamelCase				,       torchify=_lowerCamelCase   )
           for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase				,       torch.Tensor   )

           # Test not batched input
           SCREAMING_SNAKE_CASE				:				int				      = image_processing(image_inputs[0]				,       return_tensors='''pt'''   ).pixel_values

           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Tuple				      = self.image_processor_tester.get_expected_values(_lowerCamelCase   )
           self.assertEqual(
               encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

           # Test batched
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image_processing(_lowerCamelCase				,       return_tensors='''pt'''   ).pixel_values

           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Tuple				      = self.image_processor_tester.get_expected_values(_lowerCamelCase				,       batched=_lowerCamelCase   )
           self.assertEqual(
               encoded_images.shape				,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,       )


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
      raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
a__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.split(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataaVecTextModel.from_pretrained(
        a__ ,						checkpoint_file=a__   )
    dataavec.eval()  # disable dropout
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.models[0]
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.sentence_encoder
    SCREAMING_SNAKE_CASE				:				List[str]				      = DataaVecTextConfig(
        vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings ,						hidden_size=dataavec_model.args.encoder_embed_dim ,						num_hidden_layers=dataavec_model.args.encoder_layers ,						num_attention_heads=dataavec_model.args.encoder_attention_heads ,						intermediate_size=dataavec_model.args.encoder_ffn_embed_dim ,						max_position_embeddings=514 ,						type_vocab_size=1 ,						layer_norm_eps=1e-5 ,						)
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DataaVecTextForSequenceClassification(a__   ) if classification_head else DataaVecTextForMaskedLM(a__   )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE				:				int				      = dataavec_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE				:				int				      = torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
    SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_sent_encoder.layernorm_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers   ):
        # Encoder: start of layer
        SCREAMING_SNAKE_CASE				:				BertLayer				      = model.dataavec_text.encoder.layer[i]
        SCREAMING_SNAKE_CASE				:				TransformerSentenceEncoderLayer				      = dataavec_sent_encoder.layers[i]

        # self attention
        SCREAMING_SNAKE_CASE				:				BertSelfAttention				      = layer.attention.self
        assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )

        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.q_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.q_proj.bias
        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.k_proj.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.self_attn.k_proj.bias
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.v_proj.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        SCREAMING_SNAKE_CASE				:				BertSelfOutput				      = layer.attention.output
        assert (
            self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
        ), F"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_SNAKE_CASE				:				BertIntermediate				      = layer.intermediate
        assert (
            intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_SNAKE_CASE				:				BertOutput				      = layer.output
        assert (
            bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.bias
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].out_proj.bias
    else:
        # LM Head
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.bias
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_model.encoder.lm_head.layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.lm_head.layer_norm.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_model.encoder.lm_head.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model.encoder.lm_head.bias

    # Let's check that we get the same results.
    SCREAMING_SNAKE_CASE				:				torch.Tensor				      = dataavec.encode(a__   ).unsqueeze(0   )  # batch of size 1

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.allclose(a__ ,						a__ ,						atol=1e-3   )
    print('''Do both models output the same tensors?''' ,						'''🔥''' if success else '''💩'''   )
    if not success:
        raise Exception('''Something went wRoNg'''   )

    pathlib.Path(a__   ).mkdir(parents=a__ ,						exist_ok=a__   )
    print(F"""Saving model to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(a__   )


if __name__ == "__main__":
      a__				:      str    		=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
      )
      a__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
