



import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Tuple=7			,						__lowerCAmelCase					:					Any=3			,						__lowerCAmelCase					:					str=18			,						__lowerCAmelCase					:					Union[str, Any]=30			,						__lowerCAmelCase					:					Optional[Any]=4_00			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Optional[Any]=None			,						__lowerCAmelCase					:					str=True			,						)   ->	Dict:
    _A      =       size if size is not None else {'''height''': 18, '''width''': 18}
    _A      =       parent
    _A      =       batch_size
    _A      =       num_channels
    _A      =       image_size
    _A      =       min_resolution
    _A      =       max_resolution
    _A      =       do_resize
    _A      =       size
    _A      =       do_normalize

 def     snake_case_      (    self					:					Dict      )   ->	Union[str, Any]:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]      ),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }


@require_torch
@require_vision
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Union[str, Any] =  ImageGPTImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					List[str]      )   ->	str:
    _A      =       ImageGPTImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Optional[int]:
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''clusters'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_resize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''size'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_normalize'''      )      )

 def     snake_case_      (    self					:					List[Any]      )   ->	Any:
    _A      =       self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size			,						{'''height''': 18, '''width''': 18}      )

    _A      =       self.image_processing_class.from_dict(self.image_processor_dict			,						size=42      )
    self.assertEqual(image_processor.size			,						{'''height''': 42, '''width''': 42}      )

 def     snake_case_      (    self					:					str      )   ->	Optional[int]:
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    _A      =       json.loads(image_processor.to_json_string()      )
    for key, value in self.image_processor_dict.items():
       if key == "clusters":
          self.assertTrue(np.array_equal(__lowerCAmelCase			,						obj[key]      )      )
       else:
          self.assertEqual(obj[key]			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					int      )   ->	Tuple:
    _A      =       self.image_processing_class(**self.image_processor_dict      )

    with tempfile.TemporaryDirectory() as tmpdirname:
       _A      =       os.path.join(__lowerCAmelCase			,						'''image_processor.json'''      )
       image_processor_first.to_json_file(__lowerCAmelCase      )
       _A      =       self.image_processing_class.from_json_file(__lowerCAmelCase      ).to_dict()

    _A      =       image_processor_first.to_dict()
    for key, value in image_processor_first.items():
       if key == "clusters":
          self.assertTrue(np.array_equal(__lowerCAmelCase			,						image_processor_second[key]      )      )
       else:
          self.assertEqual(image_processor_first[key]			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict      )   ->	int:
    _A      =       self.image_processing_class(**self.image_processor_dict      )

    with tempfile.TemporaryDirectory() as tmpdirname:
       image_processor_first.save_pretrained(__lowerCAmelCase      )
       _A      =       self.image_processing_class.from_pretrained(__lowerCAmelCase      ).to_dict()

    _A      =       image_processor_first.to_dict()
    for key, value in image_processor_first.items():
       if key == "clusters":
          self.assertTrue(np.array_equal(__lowerCAmelCase			,						image_processor_second[key]      )      )
       else:
          self.assertEqual(image_processor_first[key]			,						__lowerCAmelCase      )

 @unittest.skip('''ImageGPT requires clusters at initialization'''      )
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    pass

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Optional[int]:
   _A      =       load_dataset('''hf-internal-testing/fixtures_image_utils'''	,   split='''test'''				)

   _A      =       Image.open(dataset[4]['''file''']				)
   _A      =       Image.open(dataset[5]['''file''']				)

   _A      =       [imagea, imagea]

   return images


@require_vision
@require_torch
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 @slow
 def     snake_case_      (    self					:					Optional[Any]      )   ->	str:
    _A      =       ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''      )

    _A      =       prepare_images()

    # test non-batched
    _A      =       image_processing(images[0]			,						return_tensors='''pt'''      )

    self.assertIsInstance(encoding.input_ids			,						torch.LongTensor      )
    self.assertEqual(encoding.input_ids.shape			,						(1, 10_24)      )

    _A      =       [3_06, 1_91, 1_91]
    self.assertEqual(encoding.input_ids[0, :3].tolist()			,						__lowerCAmelCase      )

    # test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      )

    self.assertIsInstance(encoding.input_ids			,						torch.LongTensor      )
    self.assertEqual(encoding.input_ids.shape			,						(2, 10_24)      )

    _A      =       [3_03, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()			,						__lowerCAmelCase      )







'''simple docstring'''
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
					raise Exception("""requires fairseq >= 1.0.0a""")

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

lowerCAmelCase   : Tuple             =				"""Hello world! cécé herlolip"""




def 	_A  (       A							,A							,A							)  ->				str:
	lowercase						:		Optional[Any]   					=		FairseqRobertaModel.from_pretrained(A							)
	roberta.eval()  # disable dropout
	lowercase						:		int   					=		roberta.model.encoder.sentence_encoder
	lowercase						:		Tuple   					=		XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings							,hidden_size=roberta.cfg.model.encoder_embed_dim							,num_hidden_layers=roberta.cfg.model.encoder_layers							,num_attention_heads=roberta.cfg.model.encoder_attention_heads							,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim							,max_position_embeddings=5_1_4							,type_vocab_size=1							,layer_norm_eps=1e-5							,)
	if classification_head:
		lowercase						:		Tuple   					=		roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"							,A							)

	lowercase						:		Union[str, Any]   					=		XLMRobertaXLForSequenceClassification(A							) if classification_head else XLMRobertaXLForMaskedLM(A							)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowercase						:		Optional[Any]   					=		roberta_sent_encoder.embed_tokens.weight
	lowercase						:		Dict   					=		roberta_sent_encoder.embed_positions.weight
	lowercase						:		str   					=		torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.

	lowercase						:		Dict   					=		roberta_sent_encoder.layer_norm.weight
	lowercase						:		str   					=		roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers							):
		# Encoder: start of layer
		lowercase						:		BertLayer   					=		model.roberta.encoder.layer[i]
		lowercase						:		TransformerSentenceEncoderLayer   					=		roberta_sent_encoder.layers[i]

		lowercase						:		RobertaAttention   					=		layer.attention
		lowercase						:		str   					=		roberta_layer.self_attn_layer_norm.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowercase						:		BertSelfAttention   					=		layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)							)
		)

		lowercase						:		str   					=		roberta_layer.self_attn.q_proj.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn.q_proj.bias
		lowercase						:		Union[str, Any]   					=		roberta_layer.self_attn.k_proj.weight
		lowercase						:		List[Any]   					=		roberta_layer.self_attn.k_proj.bias
		lowercase						:		Dict   					=		roberta_layer.self_attn.v_proj.weight
		lowercase						:		Tuple   					=		roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowercase						:		BertSelfOutput   					=		layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowercase						:		Tuple   					=		roberta_layer.self_attn.out_proj.weight
		lowercase						:		str   					=		roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowercase						:		List[Any]   					=		roberta_layer.final_layer_norm.weight
		lowercase						:		Optional[Any]   					=		roberta_layer.final_layer_norm.bias

		# intermediate
		lowercase						:		BertIntermediate   					=		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		Dict   					=		roberta_layer.fca.bias

		# output
		lowercase						:		BertOutput   					=		layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		str   					=		roberta_layer.fca.bias
		# end of layer

	if classification_head:
		lowercase						:		int   					=		roberta.model.classification_heads["mnli"].dense.weight
		lowercase						:		List[Any]   					=		roberta.model.classification_heads["mnli"].dense.bias
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"].out_proj.weight
		lowercase						:		Any   					=		roberta.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		lowercase						:		str   					=		roberta.model.encoder.lm_head.dense.weight
		lowercase						:		List[Any]   					=		roberta.model.encoder.lm_head.dense.bias
		lowercase						:		str   					=		roberta.model.encoder.lm_head.layer_norm.weight
		lowercase						:		Optional[Any]   					=		roberta.model.encoder.lm_head.layer_norm.bias
		lowercase						:		int   					=		roberta.model.encoder.lm_head.weight
		lowercase						:		Optional[int]   					=		roberta.model.encoder.lm_head.bias

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

	lowercase						:		Tuple   					=		model(A							)[0]
	if classification_head:
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"](roberta.extract_features(A							)							)
	else:
		lowercase						:		Optional[Any]   					=		roberta.model(A							)[0]
	print(our_output.shape							,their_output.shape							)
	lowercase						:		Optional[Any]   					=		torch.max(torch.abs(our_output - their_output							)							).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
	lowercase						:		Dict   					=		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__":
					lowerCAmelCase   : Union[str, Any]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--roberta_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."""
					)
					lowerCAmelCase   : Optional[Any]             =				parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)


