



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	snake_case__					:     Dict    		=   """platform"""
	import jax
	import jax.numpy as jnp
	import numpy as np

	from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class   _A   :


						'''simple docstring'''





						_snake_case			:     List[Any]      							=      PegasusConfig
						_snake_case			:     Union[str, Any]      							=      {}
						_snake_case			:     List[Any]      							=      """gelu"""



						def __init__(	self	:							str							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Union[str, Any]=13							,      lowerCamelCase	:							List[Any]=7							,      lowerCamelCase	:							Optional[int]=True							,      lowerCamelCase	:							Optional[Any]=False							,      lowerCamelCase	:							Dict=99							,      lowerCamelCase	:							Dict=32							,      lowerCamelCase	:							Union[str, Any]=5							,      lowerCamelCase	:							Any=4							,      lowerCamelCase	:							int=37							,      lowerCamelCase	:							int=0.1							,      lowerCamelCase	:							Union[str, Any]=0.1							,      lowerCamelCase	:							Dict=20							,      lowerCamelCase	:							Optional[int]=2							,      lowerCamelCase	:							str=1							,      lowerCamelCase	:							Optional[int]=0							,      ):






							'''simple docstring'''



							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			seq_length
							__lowercase		       =			is_training
							__lowercase		       =			use_labels
							__lowercase		       =			vocab_size
							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size

							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			max_position_embeddings
							__lowercase		       =			eos_token_id
							__lowercase		       =			pad_token_id
							__lowercase		       =			bos_token_id



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			ids_tensor([self.batch_size, self.seq_length - 1]							,      self.vocab_size	).clip(3							,      self.vocab_size	)
							__lowercase		       =			np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)							,      1	)
							__lowercase		       =			np.concatenate([input_ids, eos_tensor]							,      axis=1	)

							__lowercase		       =			ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

							__lowercase		       =			self.config_cls(
							    vocab_size=self.vocab_size							,      d_model=self.hidden_size							,      encoder_layers=self.num_hidden_layers							,      decoder_layers=self.num_hidden_layers							,      encoder_attention_heads=self.num_attention_heads							,      decoder_attention_heads=self.num_attention_heads							,      encoder_ffn_dim=self.intermediate_size							,      decoder_ffn_dim=self.intermediate_size							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      eos_token_ids=[2]							,      bos_token_id=self.bos_token_id							,      pad_token_id=self.pad_token_id							,      decoder_start_token_id=self.pad_token_id							,      **self.config_updates							,      )
							__lowercase		       =			prepare_pegasus_inputs_dict(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
							return config, inputs_dict



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			20
							__lowercase		       =			model_class_name(lowerCamelCase	)

							__lowercase		       =			model.encode(inputs_dict["input_ids"]	)

							__lowercase       ,    __lowercase		       =			(
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							__lowercase		       =			model.init_cache(decoder_input_ids.shape[0]							,      lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,      dtype="i4"	)

							__lowercase		       =			jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]							,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,      )
							__lowercase		       =			model.decode(
							    decoder_input_ids[:, :-1]							,      lowerCamelCase							,      decoder_attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      decoder_position_ids=lowerCamelCase							,      )

							__lowercase		       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,      dtype="i4"	)
							__lowercase		       =			model.decode(
							    decoder_input_ids[:, -1:]							,      lowerCamelCase							,      decoder_attention_mask=lowerCamelCase							,      past_key_values=outputs_cache.past_key_values							,      decoder_position_ids=lowerCamelCase							,      )

							__lowercase		       =			model.decode(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
							self.parent.assertTrue(diff < 1e-3							,      msg=f"""Max diff is {diff}"""	)



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			20
							__lowercase		       =			model_class_name(lowerCamelCase	)

							__lowercase		       =			model.encode(inputs_dict["input_ids"]	)

							__lowercase       ,    __lowercase		       =			(
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							__lowercase		       =			jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
							    ]							,      axis=-1							,      )

							__lowercase		       =			model.init_cache(decoder_input_ids.shape[0]							,      lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]							,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,      )

							__lowercase		       =			model.decode(
							    decoder_input_ids[:, :-1]							,      lowerCamelCase							,      decoder_attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      decoder_position_ids=lowerCamelCase							,      )
							__lowercase		       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,      dtype="i4"	)
							__lowercase		       =			model.decode(
							    decoder_input_ids[:, -1:]							,      lowerCamelCase							,      past_key_values=outputs_cache.past_key_values							,      decoder_attention_mask=lowerCamelCase							,      decoder_position_ids=lowerCamelCase							,      )

							__lowercase		       =			model.decode(lowerCamelCase							,      lowerCamelCase							,      decoder_attention_mask=lowerCamelCase	)

							__lowercase		       =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
							self.parent.assertTrue(diff < 1e-3							,      msg=f"""Max diff is {diff}"""	)



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None  ,	):
	if attention_mask is None:
		__lowercase		       =			np.not_equal(_SCREAMING_SNAKE_CASE  ,	config.pad_token_id   ).astype(np.inta   )
	if decoder_attention_mask is None:
		__lowercase		       =			np.concatenate(
		    [
		        np.ones(decoder_input_ids[:, :1].shape  ,	dtype=np.inta   ),
		        np.not_equal(decoder_input_ids[:, 1:]  ,	config.pad_token_id   ).astype(np.inta   ),
		    ]  ,	axis=-1  ,	)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	}

@require_flax
class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     List[Any]      							=      (
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						_snake_case			:     Tuple      							=      (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						_snake_case			:     Tuple      							=      True
						_snake_case			:     Any      							=      False
						_snake_case			:     Optional[int]      							=      False
						_snake_case			:     int      							=      False



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			FlaxPegasusModelTester(self	)
							__lowercase		       =			ConfigTester(self							,      config_class=lowerCamelCase	)



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							self.config_tester.run_common_tests()



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									__lowercase		       =			self._prepare_for_class(lowerCamelCase							,      lowerCamelCase	)
									__lowercase		       =			model_class(lowerCamelCase	)

									@jax.jit
									def encode_jitted(lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[Any]=None							,      **lowerCamelCase	:							int	):
										return model.encode(input_ids=lowerCamelCase							,      attention_mask=lowerCamelCase	)

									with self.subTest("JIT Enabled"	):
										__lowercase		       =			encode_jitted(**lowerCamelCase	).to_tuple()

									with self.subTest("JIT Disabled"	):
										with jax.disable_jit():
											__lowercase		       =			encode_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	:							str	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									__lowercase		       =			model_class(lowerCamelCase	)
									__lowercase		       =			model.encode(inputs_dict["input_ids"]							,      inputs_dict["attention_mask"]	)

									__lowercase		       =			{
									    "decoder_input_ids": inputs_dict["decoder_input_ids"],
									    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
									    "encoder_outputs": encoder_outputs,
									}

									@jax.jit
									def decode_jitted(lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							Any	):
										return model.decode(
										    decoder_input_ids=lowerCamelCase							,      decoder_attention_mask=lowerCamelCase							,      encoder_outputs=lowerCamelCase							,      )

									with self.subTest("JIT Enabled"	):
										__lowercase		       =			decode_jitted(**lowerCamelCase	).to_tuple()

									with self.subTest("JIT Disabled"	):
										with jax.disable_jit():
											__lowercase		       =			decode_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	)



						@slow
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							for model_class_name in self.all_model_classes:
								__lowercase		       =			model_class_name.from_pretrained("google/pegasus-large"							,      from_pt=lowerCamelCase	)
								__lowercase		       =			np.ones((1, 1)	)
								__lowercase		       =			model(lowerCamelCase	)
								self.assertIsNotNone(lowerCamelCase	)



						@slow
						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"	)
							__lowercase		       =			PegasusTokenizer.from_pretrained("google/pegasus-xsum"	)

							__lowercase		       =			[
							    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
							    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
							]

							__lowercase		       =			[
							    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
							    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
							]

							__lowercase		       =			tokenizer(lowerCamelCase							,      return_tensors="np"							,      truncation=lowerCamelCase							,      max_length=512							,      padding=lowerCamelCase	)
							__lowercase		       =			model.generate(**lowerCamelCase							,      num_beams=2	).sequences
							__lowercase		       =			tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)
							assert tgt_text == decoded








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



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

	__lowercase		       =			re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"  ,	_SCREAMING_SNAKE_CASE   )
	if matches:
		__lowercase		       =			float(matches[1]   )
		__lowercase		       =			int(matches[2]   )

	# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
	# the usual 1000. The first class (index 0) is "background".
	__lowercase		       =			1_0_0_1
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ) + 1: v for k, v in idalabel.items()}
	__lowercase		       =			"background"
	__lowercase		       =			idalabel
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	return config



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_mobilenet_va_config(_SCREAMING_SNAKE_CASE   )

	# Load 🤗 model
	__lowercase		       =			MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()

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

	# Check outputs on an image, prepared by MobileNetV1ImageProcessor
	__lowercase		       =			MobileNetVaImageProcessor(
	    crop_size={"width": config.image_size, "height": config.image_size}  ,	size={"shortest_edge": config.image_size + 3_2}  ,	)
	__lowercase		       =			image_processor(images=prepare_img()  ,	return_tensors="pt"   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			outputs.logits

	assert logits.shape == (1, 1_0_0_1)

	if model_name == "mobilenet_v1_1.0_224":
		__lowercase		       =			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":
		__lowercase		       =			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
	else:
		__lowercase		       =			None

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

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

	if push_to_hub:
		print("Pushing to the hub..."   )
		__lowercase		       =			"google/" + model_name
		image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   )
		model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   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."""
	)

	snake_case__					:     Dict    		=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)



