

'''simple docstring'''





import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
							from transformers import WhisperFeatureExtractor

if is_torch_available():
							import torch

UpperCamelCase_    						=       random.Random()






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Tuple=1.0   , __magic_name__						:     Optional[Any]=None   , __magic_name__						:     Union[str, Any]=None						)				->  Dict:
						if rng is None:
												lowercase :							List[Any]    		=global_rng

						lowercase :							Any    		=[]
						for batch_idx in range(shape[0]						):
												values.append([]						)
												for _ in range(shape[1]						):
																		values[-1].append(rng.random() * scale						)

						return values





@require_torch
@require_torchaudio
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		Optional[Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Tuple=7		,					UpperCAmelCase__     :		Tuple=400		,					UpperCAmelCase__     :		str=2000		,					UpperCAmelCase__     :		Union[str, Any]=10		,					UpperCAmelCase__     :		int=160		,					UpperCAmelCase__     :		Dict=8		,					UpperCAmelCase__     :		Union[str, Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=4000		,					UpperCAmelCase__     :		List[Any]=False		,					UpperCAmelCase__     :		Union[str, Any]=True		,					):
										'''simple docstring'''
										lowercase :							Any    		=parent
										lowercase :							Optional[Any]    		=batch_size
										lowercase :							Dict    		=min_seq_length
										lowercase :							Optional[Any]    		=max_seq_length
										lowercase :							Optional[Any]    		=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										lowercase :							Tuple    		=padding_value
										lowercase :							Dict    		=sampling_rate
										lowercase :							Union[str, Any]    		=return_attention_mask
										lowercase :							str    		=do_normalize
										lowercase :							List[Any]    		=feature_size
										lowercase :							List[str]    		=chunk_length
										lowercase :							Tuple    		=hop_length







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return {
										    "feature_size": self.feature_size,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										    "do_normalize": self.do_normalize,
										}







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		Dict=False		,					UpperCAmelCase__     :		List[str]=False   ):
										'''simple docstring'''
										def _flatten(UpperCAmelCase__     :		int   ):
																return list(itertools.chain(*UpperCAmelCase__   )   )

										if equal_length:
																lowercase :							List[str]    		=[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
										else:
																# make sure that inputs increase in size
																lowercase :							Any    		=[
																    floats_list((x, self.feature_size)   )
																    for x in range(self.min_seq_length		,					self.max_seq_length		,					self.seq_length_diff   )
																]
										if numpify:
																lowercase :							List[Any]    		=[np.asarray(UpperCAmelCase__   ) for x in speech_inputs]
										return speech_inputs





@require_torch
@require_torchaudio
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    WhisperFeatureExtractor if is_speech_available() else None







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Tuple    		=WhisperFeatureExtractionTester(self   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.feature_extraction_class(**self.feat_extract_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase :							int    		=feat_extract_first.save_pretrained(UpperCAmelCase__   )[0]
																check_json_file_has_correct_format(UpperCAmelCase__   )
																lowercase :							List[Any]    		=self.feature_extraction_class.from_pretrained(UpperCAmelCase__   )

										lowercase :							List[Any]    		=feat_extract_first.to_dict()
										lowercase :							Tuple    		=feat_extract_second.to_dict()
										lowercase :							Dict    		=feat_extract_first.mel_filters
										lowercase :							List[Any]    		=feat_extract_second.mel_filters
										self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__   )   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.feature_extraction_class(**self.feat_extract_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase :							Optional[int]    		=os.path.join(UpperCAmelCase__		,					'''feat_extract.json'''   )
																feat_extract_first.to_json_file(UpperCAmelCase__   )
																lowercase :							Tuple    		=self.feature_extraction_class.from_json_file(UpperCAmelCase__   )

										lowercase :							Dict    		=feat_extract_first.to_dict()
										lowercase :							List[str]    		=feat_extract_second.to_dict()
										lowercase :							List[str]    		=feat_extract_first.mel_filters
										lowercase :							Dict    		=feat_extract_second.mel_filters
										self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__   )   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										# Tests that all call wrap to encode_plus and batch_encode_plus
										lowercase :							Union[str, Any]    		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
										# create three inputs of length 800, 1000, and 1200
										lowercase :							Optional[Any]    		=[floats_list((1, x)   )[0] for x in range(800		,					1400		,					200   )]
										lowercase :							int    		=[np.asarray(UpperCAmelCase__   ) for speech_input in speech_inputs]

										# Test feature size
										lowercase :							Dict    		=feature_extractor(UpperCAmelCase__		,					padding='''max_length'''		,					return_tensors='''np'''   ).input_features
										self.assertTrue(input_features.ndim == 3   )
										self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames   )
										self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size   )

										# Test not batched input
										lowercase :							str    		=feature_extractor(speech_inputs[0]		,					return_tensors='''np'''   ).input_features
										lowercase :							Any    		=feature_extractor(np_speech_inputs[0]		,					return_tensors='''np'''   ).input_features
										self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__		,					atol=1E-3   )   )

										# Test batched
										lowercase :							List[str]    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''   ).input_features
										lowercase :							str    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''   ).input_features
										for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__		,					UpperCAmelCase__   ):
																self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__		,					atol=1E-3   )   )

										# Test 2-D numpy arrays are batched.
										lowercase :							str    		=[floats_list((1, x)   )[0] for x in (800, 800, 800)]
										lowercase :							Dict    		=np.asarray(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''   ).input_features
										lowercase :							Optional[Any]    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''   ).input_features
										for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__		,					UpperCAmelCase__   ):
																self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__		,					atol=1E-3   )   )

										# Test truncation required
										lowercase :							Optional[Any]    		=[floats_list((1, x)   )[0] for x in range(200		,					(feature_extractor.n_samples + 500)		,					200   )]
										lowercase :							List[Any]    		=[np.asarray(UpperCAmelCase__   ) for speech_input in speech_inputs]

										lowercase :							Union[str, Any]    		=[x[: feature_extractor.n_samples] for x in speech_inputs]
										lowercase :							List[Any]    		=[np.asarray(UpperCAmelCase__   ) for speech_input in speech_inputs_truncated]

										lowercase :							List[str]    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''   ).input_features
										lowercase :							List[Any]    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''   ).input_features
										for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__		,					UpperCAmelCase__   ):
																self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__		,					atol=1E-3   )   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										import torch

										lowercase :							Tuple    		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
										lowercase :							Tuple    		=np.random.rand(100		,					32   ).astype(np.floataa   )
										lowercase :							Tuple    		=np_speech_inputs.tolist()

										for inputs in [py_speech_inputs, np_speech_inputs]:
																lowercase :							Optional[int]    		=feature_extractor.pad([{'''input_features''': inputs}]		,					return_tensors='''np'''   )
																self.assertTrue(np_processed.input_features.dtype == np.floataa   )
																lowercase :							Dict    		=feature_extractor.pad([{'''input_features''': inputs}]		,					return_tensors='''pt'''   )
																self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		Any   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,					'''clean'''		,					split='''validation'''   )
										# automatic decoding with librispeech
										lowercase :							Dict    		=ds.sort('''id'''   ).select(range(UpperCAmelCase__   )   )[:num_samples]['''audio''']

										return [x["array"] for x in speech_samples]







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# fmt: off
										lowercase :							List[Any]    		=torch.tensor(
										    [
										        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
										        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
										        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
										        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
										    ]   )
										# fmt: on

										lowercase :							Any    		=self._load_datasamples(1   )
										lowercase :							List[str]    		=WhisperFeatureExtractor()
										lowercase :							List[str]    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''pt'''   ).input_features
										self.assertEqual(input_features.shape		,					(1, 80, 3000)   )
										self.assertTrue(torch.allclose(input_features[0, 0, :30]		,					UpperCAmelCase__		,					atol=1E-4   )   )







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
										lowercase :							Optional[int]    		=self._load_datasamples(1   )[0]
										lowercase :							Tuple    		=((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
										lowercase :							Tuple    		=feat_extract.zero_mean_unit_var_norm([audio]		,					attention_mask=UpperCAmelCase__   )[0]

										self.assertTrue(np.all(np.mean(UpperCAmelCase__   ) < 1E-3   )   )
										self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase__   ) - 1   ) < 1E-3   )   )











'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						lowerCamelCase__   (				a							):
						# vision encoder
						if "img_encoder.pos_embed" in name:
												__snake_case						 =   name.replace('img_encoder.pos_embed'    ,	'vision_model.embeddings.position_embeddings'							)
						if "img_encoder.patch_embed.proj" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.proj'    ,	'vision_model.embeddings.patch_embeddings.projection'							)
						if "img_encoder.patch_embed.norm" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.norm'    ,	'vision_model.embeddings.layernorm'							)
						if "img_encoder.layers" in name:
												__snake_case						 =   name.replace('img_encoder.layers'    ,	'vision_model.encoder.stages'							)
						if "blocks" in name and "res" not in name:
												__snake_case						 =   name.replace('blocks'    ,	'layers'							)
						if "attn" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('attn'    ,	'self_attn'							)
						if "proj" in name and "self_attn" in name and "text" not in name:
												__snake_case						 =   name.replace('proj'    ,	'out_proj'							)
						if "pre_assign_attn.attn.proj" in name:
												__snake_case						 =   name.replace('pre_assign_attn.attn.proj'    ,	'pre_assign_attn.attn.out_proj'							)
						if "norm1" in name:
												__snake_case						 =   name.replace('norm1'    ,	'layer_norm1'							)
						if "norm2" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('norm2'    ,	'layer_norm2'							)
						if "img_encoder.norm" in name:
												__snake_case						 =   name.replace('img_encoder.norm'    ,	'vision_model.layernorm'							)
						# text encoder
						if "text_encoder.token_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.token_embedding'    ,	'text_model.embeddings.token_embedding'							)
						if "text_encoder.positional_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.positional_embedding'    ,	'text_model.embeddings.position_embedding.weight'							)
						if "text_encoder.transformer.resblocks." in name:
												__snake_case						 =   name.replace('text_encoder.transformer.resblocks.'    ,	'text_model.encoder.layers.'							)
						if "ln_1" in name:
												__snake_case						 =   name.replace('ln_1'    ,	'layer_norm1'							)
						if "ln_2" in name:
												__snake_case						 =   name.replace('ln_2'    ,	'layer_norm2'							)
						if "c_fc" in name:
												__snake_case						 =   name.replace('c_fc'    ,	'fc1'							)
						if "c_proj" in name:
												__snake_case						 =   name.replace('c_proj'    ,	'fc2'							)
						if "text_encoder" in name:
												__snake_case						 =   name.replace('text_encoder'    ,	'text_model'							)
						if "ln_final" in name:
												__snake_case						 =   name.replace('ln_final'    ,	'final_layer_norm'							)
						# projection layers
						if "img_projector.linear_hidden." in name:
												__snake_case						 =   name.replace('img_projector.linear_hidden.'    ,	'visual_projection.'							)
						if "img_projector.linear_out." in name:
												__snake_case						 =   name.replace('img_projector.linear_out.'    ,	'visual_projection.3.'							)
						if "text_projector.linear_hidden" in name:
												__snake_case						 =   name.replace('text_projector.linear_hidden'    ,	'text_projection'							)
						if "text_projector.linear_out" in name:
												__snake_case						 =   name.replace('text_projector.linear_out'    ,	'text_projection.3'							)

						return name
def 						lowerCamelCase__   (				a    ,	a							):
						for key in orig_state_dict.copy().keys():
												__snake_case						 =   orig_state_dict.pop(a							)

												if "qkv" in key:
																		# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case ,       __snake_case						 =   int(key_split[2]							), int(key_split[4]							)
																		__snake_case						 =   config.vision_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[dim : dim * 2, :]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												elif "in_proj" in key:
																		# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case						 =   int(key_split[3]							)
																		__snake_case						 =   config.text_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[
																								    dim : dim * 2, :
																								]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												else:
																		__snake_case						 =   rename_key(a							)
																		# squeeze if necessary
																		if (
																		    "text_projection.0" in new_name
																		    or "text_projection.3" in new_name
																		    or "visual_projection.0" in new_name
																		    or "visual_projection.3" in new_name
																		):
																								__snake_case						 =   val.squeeze_()
																		else:
																								__snake_case						 =   val

						return orig_state_dict
def 						lowerCamelCase__   (				):
						__snake_case						 =   'http://images.cocodataset.org/val2017/000000039769.jpg'
						__snake_case						 =   Image.open(requests.get(a    ,	stream=a							).raw							)
						return im
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a="groupvit-gcc-yfcc"    ,	a=False							):
						__snake_case						 =   GroupViTConfig()
						__snake_case						 =   GroupViTModel(a							).eval()

						__snake_case						 =   torch.load(a    ,	map_location='cpu'							)['model']
						__snake_case						 =   convert_state_dict(a    ,	a							)
						__snake_case ,       __snake_case						 =   model.load_state_dict(a    ,	strict=a							)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a							) == 0)

						# verify result
						__snake_case						 =   CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'							)
						__snake_case						 =   prepare_img()
						__snake_case						 =   processor(text=['a photo of a cat', 'a photo of a dog']    ,	images=a    ,	padding=a    ,	return_tensors='pt'							)

						with torch.no_grad():
												__snake_case						 =   model(**a							)

						if model_name == "groupvit-gcc-yfcc":
												__snake_case						 =   torch.tensor([[13.3_523, 6.3_629]]							)
						elif model_name == "groupvit-gcc-redcaps":
												__snake_case						 =   torch.tensor([[16.1_873, 8.6_230]]							)
						else:
												raise ValueError(f'Model name {model_name} not supported.'							)
						assert torch.allclose(outputs.logits_per_image    ,	a    ,	atol=1E-3							)

						processor.save_pretrained(a							)
						model.save_pretrained(a							)
						print('Successfully saved processor and model to'    ,	a							)

						if push_to_hub:
												print('Pushing to the hub...'							)
												processor.push_to_hub(a    ,	organization='nielsr'							)
												model.push_to_hub(a    ,	organization='nielsr'							)


if __name__ == "__main__":
	_lowercase  =			argparse.ArgumentParser()
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
	)
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
	parser.add_argument(
	    """--model_name""",
	    default="""groupvit-gccy-fcc""",
	    type=str,
	    help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
	)
	_lowercase  =			parser.parse_args()

	convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

