



"""simple docstring"""
import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      logging.get_logger(__name__)



def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       str:
				_lowerCamelCase        =				MobileNetVaConfig(layer_norm_eps=0.0_0_1							)

				if "_quant" in model_name:
								raise ValueError('''Quantized models are not supported.'''							)

				_lowerCamelCase        =				re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''						,   lowercase_							)
				if matches:
								_lowerCamelCase        =				float(matches[1]							)
								_lowerCamelCase        =				int(matches[2]							)

				# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
				# the usual 1000. The first class (index 0) is "background".
				_lowerCamelCase        =				10_01
				_lowerCamelCase        =				'''imagenet-1k-id2label.json'''
				_lowerCamelCase        =				'''huggingface/label-files'''
				_lowerCamelCase        =				json.load(open(hf_hub_download(lowercase_						,   lowercase_						,   repo_type='''dataset'''							)						,   '''r'''							)							)
				_lowerCamelCase        =				{int(lowercase_							) + 1: v for k, v in idalabel.items()}
				_lowerCamelCase        =				'''background'''
				_lowerCamelCase        =				idalabel
				_lowerCamelCase        =				{v: k for k, v in idalabel.items()}

				return config



def        lowerCAmelCase_(   )	->       str:
				_lowerCamelCase        =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_lowerCamelCase        =				Image.open(requests.get(lowercase_						,   stream=lowercase_							).raw							)
				return im



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str						,   lowercase_     :							str						,   lowercase_     :							Union[str, Any]=False							)	->       str:
				_lowerCamelCase        =				get_mobilenet_va_config(lowercase_							)

				# Load 🤗 model
				_lowerCamelCase        =				MobileNetVaForImageClassification(lowercase_							).eval()

				# Load weights from TensorFlow checkpoint
				load_tf_weights_in_mobilenet_va(lowercase_						,   lowercase_						,   lowercase_							)

				# Check outputs on an image, prepared by MobileNetV1ImageProcessor
				_lowerCamelCase        =				MobileNetVaImageProcessor(
				    crop_size={'''width''': config.image_size, '''height''': config.image_size}						,   size={'''shortest_edge''': config.image_size + 32}						,   )
				_lowerCamelCase        =				image_processor(images=prepare_img()						,   return_tensors='''pt'''							)
				_lowerCamelCase        =				model(**lowercase_							)
				_lowerCamelCase        =				outputs.logits

				assert logits.shape == (1, 10_01)

				if model_name == "mobilenet_v1_1.0_224":
								_lowerCamelCase        =				torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]							)
				elif model_name == "mobilenet_v1_0.75_192":
								_lowerCamelCase        =				torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]							)
				else:
								_lowerCamelCase        =				None

				if expected_logits is not None:
								assert torch.allclose(logits[0, :3]						,   lowercase_						,   atol=1e-4							)

				Path(lowercase_							).mkdir(exist_ok=lowercase_							)
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
				model.save_pretrained(lowercase_							)
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
				image_processor.save_pretrained(lowercase_							)

				if push_to_hub:
								print('''Pushing to the hub...'''							)
								_lowerCamelCase        =				'''google/''' + model_name
								image_processor.push_to_hub(lowercase_							)
								model.push_to_hub(lowercase_							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						str    							=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''mobilenet_v1_1.0_224''',
			    type=str,
			    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
			)
			parser.add_argument(
			    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)











"""simple docstring"""
import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax

			from transformers.models.big_bird.modeling_flax_big_bird import (
			    FlaxBigBirdForCausalLM,
			    FlaxBigBirdForMaskedLM,
			    FlaxBigBirdForMultipleChoice,
			    FlaxBigBirdForPreTraining,
			    FlaxBigBirdForQuestionAnswering,
			    FlaxBigBirdForSequenceClassification,
			    FlaxBigBirdForTokenClassification,
			    FlaxBigBirdModel,
			)


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=2				,       lowerCamelCase__=5_6				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=9_9				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=2				,       lowerCamelCase__=7				,       lowerCamelCase__="gelu_new"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=1_6				,       lowerCamelCase__=2				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=4				,       lowerCamelCase__="block_sparse"				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_attention_mask
									_lowerCamelCase        =				use_token_type_ids
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				max_position_embeddings
									_lowerCamelCase        =				type_vocab_size
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				num_choices

									_lowerCamelCase        =				rescale_embeddings
									_lowerCamelCase        =				attention_type
									_lowerCamelCase        =				use_bias
									_lowerCamelCase        =				block_size
									_lowerCamelCase        =				num_random_blocks

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				None
									if self.use_attention_mask:
													_lowerCamelCase        =				random_attention_mask([self.batch_size, self.seq_length]   )

									_lowerCamelCase        =				None
									if self.use_token_type_ids:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size   )

									_lowerCamelCase        =				BigBirdConfig(
									    vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       is_decoder=lowerCamelCase__				,       initializer_range=self.initializer_range				,       attention_type=self.attention_type				,       block_size=self.block_size				,       num_random_blocks=self.num_random_blocks				,       use_bias=self.use_bias				,       rescale_embeddings=self.rescale_embeddings				,       )

									return config, input_ids, token_type_ids, attention_mask





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				config_and_inputs
									_lowerCamelCase        =				{
									    '''input_ids''': input_ids,
									    '''token_type_ids''': token_type_ids,
									    '''attention_mask''': attention_mask,
									}
									return config, inputs_dict


@require_flax
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= (
					    (
					        FlaxBigBirdForCausalLM,
					        FlaxBigBirdModel,
					        FlaxBigBirdForPreTraining,
					        FlaxBigBirdForMaskedLM,
					        FlaxBigBirdForMultipleChoice,
					        FlaxBigBirdForQuestionAnswering,
					        FlaxBigBirdForSequenceClassification,
					        FlaxBigBirdForTokenClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					lowercase__   :       Any										= False
					lowercase__   :       Optional[int]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxBigBirdModelTester(self   )

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_from_pretrained_save_pretrained()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_from_pretrained_with_no_automatic_init()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_no_automatic_init()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_hidden_states_output()

					@slow
					def 			snake_case__      (    self   ):
									for model_class_name in self.all_model_classes:
													_lowerCamelCase        =				model_class_name.from_pretrained('''google/bigbird-roberta-base'''   )
													self.assertIsNotNone(lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									if self.test_attn_probs:
													super().test_attention_outputs()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																	_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
																	_lowerCamelCase        =				model_class(lowerCamelCase__   )

																	@jax.jit
																	def model_jitted(lowerCamelCase__				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
																					return model(input_ids=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       **lowerCamelCase__   )

																	with self.subTest('''JIT Enabled'''   ):
																					_lowerCamelCase        =				model_jitted(**lowerCamelCase__   ).to_tuple()

																	with self.subTest('''JIT Disabled'''   ):
																					with jax.disable_jit():
																									_lowerCamelCase        =				model_jitted(**lowerCamelCase__   ).to_tuple()

																	self.assertEqual(len(lowerCamelCase__   )				,       len(lowerCamelCase__   )   )
																	for jitted_output, output in zip(lowerCamelCase__				,       lowerCamelCase__   ):
																					self.assertEqual(jitted_output.shape				,       output.shape   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=1e-5				,       lowerCamelCase__="outputs"				,       lowerCamelCase__=None   ):
									# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
									# an effort was done to return `attention_probs` (yet to be verified).
									if name.startswith('''outputs.attentions'''   ):
													return
									else:
													super().check_pt_flax_outputs(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
